Smarten – Blog https://www.smarten.com/blog Simply Smarter! Thu, 14 Nov 2019 06:31:31 +0000 en-US hourly 1 https://wordpress.org/?v=4.7.13 How Can My Business Get the Most Out of Self-Serve Advanced Analytics? https://www.smarten.com/blog/how-can-my-business-get-the-most-out-of-self-serve-advanced-analytics/ Thu, 14 Nov 2019 06:31:31 +0000 https://www.smarten.com/blog/?p=6474 Continued]]> How Can My Business Get the Most Out of Self-Serve Advanced Analytics?

How does an organization help the self-serve advanced analytics model grow and thrive? Responsibility lies in a number of places within the enterprise.

Data Analysts/Data Scientists: If the business is large enough and sophisticated enough to have data analysts or data scientists on its team, those individuals will benefit from self-serve analytics. Because business users can leverage the tools to make better day-to-day decisions and to perform their own analysis, the professional analyst or data scientist will have more time to focus on strategic initiatives and more complex analytical projects. Look to these professionals to monitor the effects of self-serve analytics on their workload, and to identify opportunities to capitalize on power users, and the advent of data popularity within the organization. By encouraging the adoption of these tools and mentoring appropriate business users, the data analyst will contribute to the advancement of these tools within the organization.

Senior Management: As the executive team incorporates analytics into its strategy and business decisions, it will encourage the use of these tools within the organization. When a middle manager or team member understands that the senior management team values analytics and expects to see data-driven decisions, each business unit and department will embrace the use of self-serve advanced analytics.

IT: The IT team can offer encouragement and ensure that business users have insight into the origin and dependability of data by establishing watermarks, labeling and other systems that help users identify the source of data and when and why it was edited or changed by other users. The IT team provides critical support to ensure appropriate data integration from various data sources and to support an environment that allows for dependable performance, mobile access and appropriate data security.

Business Users: The enterprise should identify and encourage power users and those who apply their skills, creativity and knowledge to develop and share reports and to adopt self-serve advanced analytics tools. These team members can become powerful mentors and role models for the organization and can help other team members understand how best to leverage these analytical tools to achieve their goals.

As business users adopt and gain confidence with advanced analytics, power users and data popularity will emerge and the organization will experience a transformation from business users to Citizen Data Scientists and Analytics Translator. Analytics Translators can also introduce efficiencies, productivity and optimization into the organization as natural leaders emerge within the organization and Analytics Translators provide a liaison role between IT, data scientists and business users. These power users can be role models and offer support within business user teams to assure enterprise-wide comfort with analytics tools.

As you can see, the responsibility for growing and capitalizing on the potential of self-serve advanced analytics lies within every aspect of the organization. These tools can offer numerous benefits and the ways the business can leverage the tools are as varied and unique as the organization itself. If the business management team makes the commitment to engrain the analytical philosophy in its organization at every level, it will have the best chance of success.

Original Post: How Can My Business Get the Most Out of Self-Serve Advanced Analytics?

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Analytics for Data Scientists and Business Users! https://www.smarten.com/blog/2019/11/12/november-12-2019/analytics-for-business-users-and-data-scientists/ Tue, 12 Nov 2019 10:07:50 +0000 https://www.smarten.com/blog/?p=6470 Continued]]> Business Users and Data Scientists Need Analytics Tools!

Analytics for All: You Can Serve Business Users AND Data Scientists!

I hope you would agree that every team member in your business is an important and valuable resource. When we talk, in the tech world, about self-serve apps and the concept of cascading analytics to everyone in the organization, managers and team members start to get nervous.

Does the use of analytical tools mean that every business user has to become an analyst or a data scientist? Does the roll-out of Augmented Analytics to business users mean that data scientists will lose their jobs? Not to worry. There is a good reason to pursue your self-serve augmented analytics projects AND a good reason to keep those data scientists right where they are!

With the democratization of analytics and the advent of self-serve tools, organizations can encourage and create Citizen Data Scientists and enable the average business user to leverage sophisticated predictive algorithms without the expertise and skill of a trained data scientist. This approach allows users who are not statisticians, data scientists, programmers or analysts to leverage self-service tools to confidently make business decisions, share data, and reports and present data in a way that allows the organization to identify trends, patterns, opportunities and challenges.

But, the transformation of business users into Citizen Data Scientists does not mean there is no need for professional data scientists. A business user might use Assisted Predictive Modeling, to analyze customer churn and find out which customers are likely to move away (churn) based on purchase patterns, demographics, geographic and other macro parameters. BUT, if the business needs to fine-tune or further explore an issue, a parameter, or a model, the Data Scientist is there to do the strategic work. If there is a need for precision calculation, a unique algorithm application, analysis of selection bias, theory-driven methodology or other in-depth analysis, the organization would look to a data scientist.

By providing the right tools to the right people and focusing on the core job responsibilities that drive the day-to-day tasks of a business users or a data scientist, the organization can gain insight to make good decisions AND carefully craft and refine strategic initiatives by leveraging the skills of its Data Scientist team. So, YES, there is a place in your organization for both team members and there are Advanced Analytics Tools to serve the needs of both of these team members.

Find out how augmented analytics and advanced analytics tools can support the needs of your business users, your IT team, your analysts and your data scientists…and let’s not forget your management team! Analytics for All.

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Digital Transformation and Augmented Analytics Go Hand in Hand! https://www.smarten.com/blog/begin-your-digital-transformation-with-augmented-analytics/ Wed, 06 Nov 2019 08:06:09 +0000 https://www.smarten.com/blog/?p=6465 Continued]]> Begin Your Digital Transformation with Augmented Analytics!

How Can My Business Begin Digital Transformation? How About Augmented Analytics?

If you read industry and technology journals, you have probably seen the term, ‘digital transformation’. So, what is digital transformation Gartner asked, and here is what the Gartner glossary of terms says about that: ‘Digital business transformation is the process of exploiting digital technologies and supporting capabilities to create a robust new digital business model.’ So, that is the digital technology business definition and that definition will translate differently for every organization in terms of where the enterprise will begin to focus the transformation process and how it will get to its goal.

Among the many digital transformation examples one might find in an organization is the focus on data democratization and the introduction of initiatives to encourage and support Data Literacy among an enterprise team at the business user level. You can find numerous digital transformation articles online today but when it comes to your own business, you will have to decide how and where to focus your initiative.

One such starting point might be Advanced Analytics. With the introduction of advanced analytics to the business user community, the organization can encourage the adoption, sharing and popularity of data and support fact-based decision making to achieve its goals and objectives. Business users that employ Augmented Analytics Tools can leverage sophisticated analytics, forecasting and predictive algorithms and techniques, smart data visualization, tools that utilize machine learning, simple search analytics and natural language processing (NLP) to achieve better results and, in the process, to become more data literate, allowing data scientists and IT staff to focus more on strategic projects.

In short, transforming business users into Citizen Data Scientists is a great place to start your digital transformation initiative. This type of engagement builds confidence and comfort with technology, data and analytics and allows the organization to better leverage its resources and build skills within the enterprise.

Contact Us today if you want to explore the opportunities of digital transformation through Augmented Analytics.

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There are Many Advantages of Advanced Analytics! https://www.smarten.com/blog/2019/10/14/october-14-2019/augmented-analytics-advantages/ Mon, 14 Oct 2019 11:00:03 +0000 https://www.smarten.com/blog/?p=6461 Continued]]> Augmented Analytics Benefits are Numerous and Impressive!

Enjoy the Benefits of Advanced Analytics with Augmented Analytics Support!

As a business manager or a business team member, you probably make it your business to stay abreast of industry and market trends and to understand how best to use technology to refine business results and better understand your market, competition and customers. If you have been reading industry publications, you are probably familiar with the concept of augmented analytics and augmented analytics benefits.

But, understanding the Advantages of Advanced Analytics and implementing advanced analytics throughout your organization may be two very different things. To democratize advanced analytics, you will need to choose the right solution. To enjoy the Benefits of Advanced Analytics in your enterprise you will need to select a partner and a solution that provides an Augmented Analytics approach with auto-suggestions, recommendations and guidance that will help every user to find, analyze and report on information without the assistance of an IT staff member or a data scientist.

Advanced analytics support that is built-in to the solution and allows even a user with average skills to leverage these sophisticated tools will ensure that users adopt the solution and ensure that your organization enjoys all advanced analytics benefits. If you want to democratize analytics and provide your users with tools that allow them to achieve consistent, dependable results and meet objectives, you can find expert Augmented Analytics Support and Products Here.

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NLP and Augmented Analytics: A Recipe for Success! https://www.smarten.com/blog/how-can-natural-language-processing-help-with-data-analytics/ Wed, 09 Oct 2019 06:33:56 +0000 https://www.smarten.com/blog/?p=6456 Continued]]> How Can Natural Language Processing Help with Data Analytics?

Augmented Analytics Benefits Include NLP for Easy Analysis!

Everyone has heard the term ‘natural language processing’ or NLP. So, what is natural language processing, exactly, and how can it help your business? NLP allows a program or software to understand language as written/spoken by humans so that people can interact with a system without using long strings of code. You use natural language processing every day when you engage with your word processing software to perform spell check, when your smart phone auto-corrects your words, when you use voice to text messaging, when your email system applies a spam filter, when you are served keywords that relate to a search you did on a search engine and when you use ‘assistants’ like Siri or Alexa.

So, how can NLP help your business users to leverage advanced analytics solutions and enjoy Augmented Analytics Benefits? Natural Language Processing Search Analytics (NLP) is a crucial component of search analytics and smart data discovery today. NLP search allows business users to create complex searches without engaging in endless clicks and complex navigation and commands. Using this type of search analytics, users can access and view clear, concise answers and analysis quickly and easily.

Advanced Analytics with Natural Language Processing (NLP) provides a familiar Google-type interface where users can compose and enter a question using common human language. For example, a business user might ask, ‘who sold the most bakery products in 2017 in the Southwest region?’ Users can simply enter a search query in natural language and the system will translate the query and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

Let’s take it one step further with Clickless Analytics. What is clickless analytics? This process provides seamless augmented analytics and Advanced Data Discovery using machine learning and natural language processing (NLP), in a self-serve environment that is easy enough for every business user.

If you want to find out how NLP and simplified Search Analytics can help your organization and your users to be more productive and to make faster, simpler decisions with confidence, Contact Us today.

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Visual Analytics for Business Users! https://www.smarten.com/blog/data-visualization-software-for-your-users/ Thu, 26 Sep 2019 09:05:02 +0000 https://www.smarten.com/blog/?p=6452 Continued]]> Data Visualization Software For Your Users!

Data Visualization Tools Provide Crucial Decision Support!

If they are to help your users achieve their goals and understand what is happening within your organization, data visualization tools must be easy to use, with sophisticated functionality and an intuitive user interface. If you want business users to leverage data visualization software, you have to give them something they can easily use and adopt with smart visualization that will recommend the best way to process and visualize data, depending on what the user wants to interpret and the type of data they are working with and sharing.

Visual Analytics does not have to be the domain of data scientists or IT staff. With Smart Data Visualization business users can view and analyze data to identify a problem and clarify a root cause. Business users can interact easily with Data Discovery Tools and build a view that will tell a story using guided visualization and recommended data presentation. Guided recommendations are made based on data type, volume, dimensions, patterns and nature of data.

By combining cutting-edge technology and machine learning on the backend, and an intuitive user experience on the front end, business users can easily leverage sophisticated tools to personalize data displays and create meaningful views and collaboration.

Contact Us today if you want to explore the opportunities of Smart Visualization.

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Smarten Offers 30-day Free Trial of Augmented Analytics Solution on AWS Marketplace https://www.smarten.com/blog/smarten-offer-30-day-free-trial-of-augmented-analytics-solution-on-aws-marketplace/ Wed, 18 Sep 2019 06:34:43 +0000 https://www.smarten.com/blog/?p=6447 Continued]]> Smarten Offers 30-day Free Trial of Augmented Analytics Solution on AWS Marketplace

The Smarten suite of advanced analytics tools is now available for a free 30-day trial on the AWS Marketplace. This augmented analytics solution includes smart data visualization, assisted predictive modeling and a self-serve data preparation assisted by machine learning to help business users without any data science skills to create, use and share predictive models on their own.

The Elegant MicroWeb (Smarten SSDP solution) is listed as a Representative Vendor in the Gartner Market Guide for Data Preparation Report, as a Niche BI and Analytics Vendor in the Gartner Report, Competitive Landscape: BI Platforms and Analytics Software, Asia/Pacific, as a Representative Vendor in the Gartner Market Guide for Enterprise-Reporting-Based Platforms, and a Listed Vendor in the Other Vendors to Consider for Modern BI and Analytics, Gartner Report

Smarten CEO, Kartik Patel says, “We are pleased to announce the availability of the innovative Smarten preconfigured solution on the AWS Marketplace. This 30-day free trial provides access to those who wish to explore the benefits and possibilities inherent in the features of this augmented analytics solution.”

The Smarten tool is designed for business users working in an environment that encourages and supports data democratization and data literacy within the ranks of the enterprise team. Augmented analytics provides recommendations and auto-suggestions to ensure that business users can leverage these sophisticated tools to advance user adoption in the business environment, transform users into Citizen Data Scientists, and encourage empowerment and accountability for all team members.

“The 30-day free trial of the preconfigured Smarten product provides ample opportunity for users to explore and understand the value of advanced analytics,” says Patel. “These tools support fact-based day-to-day decisions and strategic, operational and tactical initiatives, goals and objectives.”

The Smarten 30-day free trial download is preconfigured and easily accessible on the AWS Marketplace, and the trial includes Smarten team support to help prospective users get started with Smarten.

Following the 30-day free trial, the Smarten product is available for extended use with Bring Your Own License (BYOL) availability. One can launch a preconfigured AWS Smarten instance with a free 30-day trial period from the AWS marketplace and answers to Frequently Asked Questions (FAQs), as well as Learning Opportunities to help users understand the benefits and value of the Smarten solution. Launch Smarten with the 30-day free trial on AWS now.

More information on the benefits of the Smarten Augmented Analytics product suite is available here.

Original Post: Smarten Offers 30-day Free Trial of Augmented Analytics Solution on AWS Marketplace

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Is Advanced Data Discovery Suitable for Business Users? https://www.smarten.com/blog/can-data-discovery-improve-my-business-users-performance/ Tue, 17 Sep 2019 07:49:22 +0000 https://www.smarten.com/blog/?p=6443 Continued]]> Can Data Discovery Improve My Business Users’ Performance?

Is Augmented Data Discovery Easy Enough for My Business Users?

A data discovery tool is a crucial tool for every business user in your organization. With so many sources of data, in so many locations with your enterprise, it is impossible for users to know whether they have access to complete, accurate data to make decisions.

With the right Advanced Analytics Tools, business users can have access to data integrated from disparate data sources and can use that date for Advanced Data Discovery so they can gain insight into problems and opportunities, share information with other users and be more productive, empowered and accountable. So, what is advanced analytics and should you consider this type of solution for your business users?

Advanced Analytics comprises the discovery, interpretation, and communication of meaningful patterns in data; and considers and applies patterns and trends to achieve clear, fact-based decision-making. To put it another way, advanced analytics connects information to decisions and strategies and allows the organization to set objectives and goals that are practical and attainable given the competitive and market landscape. While businesses had looked to IT and data scientists to find and analyze and interpret data in the past, the business market is moving too fast today to wait for this information and the truth is that business users need this data and insight to do their jobs just as much (if not more) than senior managers.

Augmented Analytics provides guidance and recommendations so users can visualize data in a way that makes sense for the type of data they want to analyze and it offers auto-suggestions to enable forecasting and predictive analytics methodology that is right for the data the user is considering.

Contact Us today to find out more about how Augmented Data Discovery can help your business to succeed.

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Can Business Users Leverage Self-Serve Data Prep? https://www.smarten.com/blog/2019/09/16/september-16-2019/augmented-data-prep/ Mon, 16 Sep 2019 06:44:04 +0000 https://www.smarten.com/blog/?p=6439 Continued]]> What Is Self-Serve Data Preparation?

How About Giving Your Business Users the Power to Prepare Data for Analysis?

Can Your Business Achieve Self-Serve Data Prep? Lots of my friends talk about the difficulty of preparing data for analysis and how long it takes to get IT or data scientists or analysts to take on the project, get the data prepared and run reports or perform analytics. Frankly, this problem is a puzzle to me!

Data prep does not have to be something your IT or data science staff does. It can be done easily with simple tools that are accessible and can be leveraged by every business user without any specialized technical skills. Self-Serve Data Preparation and self-serve ETL (data extraction, transformation and loading) can and should be easy. Users can transform, shape, reduce, combine, explore, clean, sample and aggregate data, without the need for SQL skills, ETL or other programming language.

With the right Augmented Analytics solution, data preparation is presented with tools, guidance and machine learning. Machine learning capability provides guidance to determine the best techniques and the best fit transformations for the data business users want to analyze, allowing for better understanding of data.

Data Preparation Tools can make your life a lot easier and will allow every user in your organization to gather and prepare data for analysis without delay so users can leverage fact-based analytics to make decisions and to move forward with projects, capitalize on opportunities and find the root cause of problems.

With the mobility and affordability of these tools, there is no reason why your organization cannot enjoy Self-Serve ETL.

If you want to find out more about Self-Serve Data ETL, start here: Augmented Data Preparation.

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What is SSDP and Can it Truly Make Analytics Self-Serve? https://www.smarten.com/blog/what-is-ssdp-and-can-it-truly-make-analytics-self-serve/ Fri, 06 Sep 2019 08:28:40 +0000 https://www.smarten.com/blog/?p=6431 Continued]]> What is SSDP and Can it Truly Make Analytics Self-Serve?

SSDP (otherwise known as self-serve data preparation) is the logical evolution of business intelligence analytical tools. With self-serve tools, data discovery and analytics tools are accessible to team members and business users across the enterprise.

What is SSDP?
SSDP or Self-Serve Data Preparation is a crucial component of Advanced Data Discovery. With self-serve data prep, data analytics moves out of the sole domain of analysts and IT and into the domain of business users. With true self-serve business intelligence and analytics solutions, the average business user can perform data preparation, test theories and hypotheses by prototyping on their own and share clear, objective data with others.

The traditional definition of data preparation describes an iterative process, typically executed by IT staff or analysts to extract and transform raw data so that the data can be used for discovery, analytics and reporting. Self-Serve Data Preparation (SSDP) allows the organization, and its users, to gather, manipulate and analyze complex data from multiple sources in a single interface with easy-to-use tools.

SSDP allows business users to leverage tools without the restrictions placed on managed dashboards or standardized reporting tools. Users can access complex tools in an easy-to-use environment without the help of programmers or data scientists. SSDP (Self-Service Data Preparation) empowers business users and allows them to perform tasks, make decisions and recommendations quickly and with speed, agility and accuracy.

Rather than preparing data at the central meta-data layer, and restricting what business users can do and see, these
IT enabled (NOT IT controlled),
self-serve data preparation and business intelligence tools and features put meaningful views of data in the hands of business users.

SSDP allows average business users to compile and prepare data and use that data in analytics to test hypotheses, visualize and share data, prepare reports and support day-to-day tasks with complete drill-down and drill-through capability, custom alerts and mobile access that supports the needs of every team member. Users can control the data elements, the volume and the timing of the analysis and reporting.

Can SSDP Truly Make Analytics Self-Serve?
The key to self-serve solutions is to provide sophisticated features and functionality in an easy-to-use, intuitive, interactive environment that users will want to adopt and leverage. By putting the power of data preparation in the hands of business users, the business can unshackle its users and make them more of an asset to the enterprise, while empowering them to find and solve day-to-day problems. SSDP can, and does make analytics self-serve, so analysts, data scientists and IT staff can focus on strategic and long-term organizational needs and provide expert advice and support as needed. In the meantime, business users have a tool that is sophisticated enough to present clear, accurate, measurable results and allow them to find the source of problems, optimize results and share data to support business decisions.

SSDP tools allow users to compile and analyze data and test theories and prototypes to support dynamic decisions and planning on their own so the business does not lose time or competitive advantage while waiting for reports or analysis from a central source.

A Gartner report published on May 24, 2016 (ID G00274731) , entitled ‘Embrace Self-Service Data Preparation Tools for Agility, but Govern to Avoid Data Chaos’, predicts that, ‘By 2018, data discovery and data management evolution will drive most organizations to augment centralized analytic architectures with decentralized approaches.’

2017 has certainly proven this to be true, as businesses embrace the value of self-serve data preparation and analytics tools.

SSDP tools integrate data from multiple data sources and make it accessible in a single, uniform, interactive view with features like smart suggestions, auto-suggested relationships, JOINs, type casts, hierarchies, data cleansing and statistical algorithms like binning, clustering, and regression for noise reduction, and trend and pattern identification. SSDP balances flexibility and agility with data governance so business users have access to the right data at the right time, and the IT team can maintain crucial security and data privacy controls and standards, as well as data quality.

Self-Serve Data Prep in Action

Perhaps the most important aspect of SSDP success is making the Self-Serve Data Prep solution simple and valuable enough to encourage business user adoption so that the enterprise can achieve and sustain ROI and TCO goals.

In a business environment where business users expect tools that are simple and powerful, SSDP can do much to advance the objectives and goals of the organization and make each business user a more valuable business resource. Self-serve tools allow users to leverage knowledge and skill and better perform against forecasts and plans.

Original Post: What is SSDP and Can it Truly Make Analytics Self-Serve?

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Augmented Data Preparation and Self-Service ETL! https://www.smarten.com/blog/self-service-data-preparation-that-is-easy-and-worry-free/ Wed, 04 Sep 2019 08:19:35 +0000 https://www.smarten.com/blog/?p=6425 Continued]]> Self-Service Data Preparation that is Easy and Worry-Free!

What is Self Service Data Preparation?

Data prep can slow down analytics and cause delays. Self-service data preparation (when done right) can enable business users to leverage sophisticated, easy-to-use tools for self-serve ETL. Data extraction, transformation and loading (otherwise known as ETL) can be time-consuming and requires professional skills but self-service ETL will walk business users through an augmented data preparation process and take the complicated, confusing steps out of the process by helping the user make decisions on how to prepare, clean, reduce and use the data in the best way possible.

The right Self-Serve Data Preparation tool makes all the difference in the world! It enables self-serve, advanced analytics and provides business users with tools they will want to adopt and use so the enterprise can make the most of their Augmented Analytics environment and reap rapid return on investment (ROI) and low total cost of ownership (TCO).

If you want to improve productivity and empower business users with tools that will guide them through an otherwise complex process, Contact Us today to find out more about how Self-Serve Data Preparation can help your organization.

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White Paper – Accelerating Advanced Analytics in an Immature Analytics Culture https://www.smarten.com/blog/white-paper-accelerating-advanced-analytics-in-an-immature-analytics-culture/ Thu, 29 Aug 2019 09:07:38 +0000 https://www.smarten.com/blog/?p=6416 Continued]]>

For organizations that wish to leverage advanced analytics, the first order of business is to evaluate the maturity of the advanced analytical culture within the organization and among its users and decide whether the entire team is ready to take on the task of accurately analyzing business results, and planning and making course corrections on a daily basis. If the organization is not ready for this change, the business management team must plan for and execute a transition to quickly, carefully, and successfully move the enterprise into the advanced analytics arena.

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Smarten Augmented Analytics: Sophistication and Simplicity https://www.smarten.com/blog/smarten-augmented-analytics-sophistication-and-simplicity/ Wed, 21 Aug 2019 10:42:45 +0000 https://www.smarten.com/blog/?p=6409 Continued]]> Smarten Augmented Analytics: Sophistication and Simplicity

Discover the Smarten approach to Augmented Analytics and Advanced Data Discovery tools. These tools allow business users to leverage augmented analytics that provide guides and suggestions to help the business user more quickly and effectively produce clear advanced analytics with little to no training, thereby transforming the average business user into a Citizen Data Scientist. Using the Smarten approach, users can quickly and easily prepare and analyze data and visualize and explore data, notate and highlight data and share data with others.

Smarten CEO, Kartik Patel says, “Our business users leverage Augmented Analytics to identify the important ‘nuggets’, buried in traditional data, and to connect the dots, find exceptions, identify patterns and trends and better predict results, and they can do so without specialized skills or a knowledge of statistical analysis or assistance from data scientists or the IT team.”

There are many features and benefits to the Smarten approach to Advanced Data Discovery. Here are just a few:

  • Self-Serve Data Preparation – Self-Serve Data Preparation empowers business users with access to meaningful data and prepares data for analysis without the assistance of data scientists or ETL experts or IT staff.
  • Smart Data Visualization – Smart Data Visualization gives users a helping hand with suggestions and recommendations for the best possible visualization of underlying data to generate meaningful insights from data.
  • Assisted Predictive Modeling – These tools enable the average business user to leverage sophisticated predictive algorithms without requiring statistical or data science skills.

“The Smarten roadmap to advanced analytics includes Clickless Analytics, leveraging searches that utilize Natural Language Processing,” says Patel. “The product roadmap also includes Auto Insights to free business users and reduce the time and skills required to produce accurate, clear results, quickly and dependably, using machine learning that to collect and analyze data with the guided assistance of a ‘smart’ solution.”

Augmented Analytics allows every user, data scientist and enterprise user to enjoy the benefits of advanced analytics with sophisticated features that include suggested relationships, clear, concise identification of patterns and suggested visualization techniques and formats. Users can highlight trends and patterns, test hypotheses and theories to reduce business risk, and easily predict and forecast results.

This 100% browser-based, mobile BI solution is flexible, scalable and easy to implement with minimal training and implementation time and unmatched ROI and TCO. By empowering users with Smarten tools, every organization can optimize resources, achieve strategic and operational goals and create a competitive advantage.

Original Post: Smarten Augmented Analytics: Sophistication and Simplicity

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Citizen Data Scientists Rule! https://www.smarten.com/blog/2019/08/09/august-09-2019/citizen-data-scientists/ Fri, 09 Aug 2019 07:08:46 +0000 https://www.smarten.com/blog/?p=6400 Continued]]> What’s So Great About Citizen Data Scientists?

Why Should Your Business Users Want to Become Citizen Data Scientists?

You have probably heard a lot about the benefits of transforming business users into Citizen Data Scientists by deploying an advanced analytics platform. Let’s say you did your homework and found just the right solution so that all your needs are met and your business users can happily embrace the solution and become empowered – and in so doing, add value and accountability to your organization.

But, in order to complete this picture, you will need to help your business users see the benefit to THEM! So, what kind of value can your business users expect to experience when they engage in the use of Augmented Analytics?

First, they do not have to wait for crucial information to be gathered and reported by IT or data scientists or analysts. If they have an issue they want to understand so they can answer a customer question or present data to a manager, they can use auto-recommendations on how to best visualize data and what algorithms to use to analyze a particular type of data and get to where they are going quickly and without frustration.

Secondly, they will gain insight into problems and opportunities and get to the root cause of issues before they negatively impact the business. They can gain perspective and even test theories and hypotheses so they can assess results before making that decision, thereby avoiding a misstep.

Thirdly, business users can make timely, accurate decisions and managers will know that these decisions are based on facts and driven by data rather than guesswork or opinion.

For those users that love using their creativity and may be inclined toward power use of solutions, these Advanced Analytics Tools can allow them to spread their wings and help them to show their true potential.

Transforming business users with average technical skills into Citizen Data Scientists is definitely a benefit to the individual and to the organization.

If you want to enjoy the benefits of Citizen Data Scientists, improve user satisfaction and make timely, accurate decisions, you can start here: Benefits of Citizen Data Scientists and Augmented Analytics.

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Get Augmented Analytics Advantages Now! https://www.smarten.com/blog/discover-the-advantages-of-augmented-analytics/ Wed, 07 Aug 2019 09:48:14 +0000 https://www.smarten.com/blog/?p=6396 Continued]]> Discover the Advantages of Augmented Analytics!

Do You Know the Benefits of Advanced Analytics? Find out Now!

The benefits of advanced analytics are many and the current support in the market for business user access and data insight provides expanded advantages of advanced analytics.

Access to these crucial tools is no longer limited to IT and data scientists or analysts. Business users can leverage Augmented Analytics advantages by using tools that make suggestions and recommendations to help a user decide how to visualize data, which algorithms and analytical techniques to use for forecasting and prediction and many other features that make the use of these tools simple and add value.

Augmented Analytics Benefits include clear insight into data to find those ‘ah hah’ moments that will help a user to solve a problem, find the root cause of an issue, test theories and hypotheses, share data and information and clearly identify new approaches to customers, suppliers and to the market.

Contact Us today to find out more about the Advantages of Augmented Analytics.

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How Do I Prove the Value of Self-Serve Augmented Data Discovery? https://www.smarten.com/blog/how-do-i-prove-the-value-of-self-serve-augmented-data-discovery/ Wed, 31 Jul 2019 10:50:26 +0000 https://www.smarten.com/blog/?p=6392 Continued]]> How Do I Prove The Value Of Self-Serve Augmented Data Discovery?

How does one measure the effectiveness of a new Augmented Data Discovery solution? Once the business has chosen data democratization and implemented a self-serve analytics solution, it must measure ROI & TCO and establish metrics that will compare business results achieved before and after the implementation.

Without measurable results, it will be difficult to garner and sustain support for business transformation and for creating an environment where Citizen Data Scientists can thrive. Senior managers, corporate stakeholders and naysayers may resort to gut feelings and business users may perceive that there is little to be gained from embracing self-serve advanced analytics tools if the management team does not value these new tools.

When an organization establishes metrics, it must consider its goals and objectives and analyze the results of actions taken and decisions made with the support of data analytics versus those made ‘the old way’. For example, if one were to set new pricing, decide on a new business location or create a new promotion based on Assisted Predictive Modeling, the business would measure the success and results versus the results achieved when decisions were made without data analytics.

Many organizations depend on the vision of one individual or a small team of executives who have market or industry experience. This approach can be difficult to defeat without clearly defined metrics and results to support the case for data-driven decisions. At its inception, these tools may be used to ‘test’ a theory or compare a direction or decision to another option. But, that is OK. By offering recommendations and options, the team will begin to consider other possibilities and, as the data foundation is created, the argument for ‘going with your gut’ will fade.

As the enterprise shifts its focus, and team members adopt and depend on Augmented Analytics for presentations, data sharing and reporting, the culture will shift and the business will gain a new appreciation for clear, precise data and the results it can produce in the planning process, in day-to-day decisions and in solving problems and identifying market potential.

Original Post: How Do I Prove the Value of Self-Serve Augmented Data Discovery?

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Data Modeling Pulls it All Together for the Business! https://www.smarten.com/blog/predictive-modeling-creates-a-clear-picture-of-the-future/ Wed, 24 Jul 2019 06:03:20 +0000 https://www.smarten.com/blog/?p=6384 Continued]]> Predictive Modeling Creates a Clear Picture of the Future!

What is Predictive Analytics and How Can it Help My Business?

What is predictive analytics? Put simply, predictive analytics is a method used to forecast and predict the future results and needs of an organization using historical data and a comprehensive set of data from across and outside the enterprise.

Predictive Modeling allows users to test theories and hypotheses and develop the best strategy. It enables more accurate, dependable planning and allows the organization to use fact-based planning, rather than using guesswork or opinion or using incomplete data.

Data modeling provides a picture of products, services, locations, customers, investments, resources, competitors, marketing, sales and other aspects of the organization that will impact forecasting and planning.

If the business chooses an Advanced Analytics solution that includes Natural Language Processing (NLP), every member of the enterprise team can leverage these tools and plan for the future, asking questions in natural language and receiving answers in the same way. There is no need for programming or training in algorithms or statistical or analytical techniques.

Contact Us today to find out Predictive Analytics can help your business users analyze data and achieve results to make the best business decision.

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Augmented Analytics with Smart Data Visualization! https://www.smarten.com/blog/2019/07/11/july-11-2019/smart-data-visualization/ Thu, 11 Jul 2019 07:19:21 +0000 https://www.smarten.com/blog/?p=6388 Continued]]> Smart Data Visualization Works for All Business Users!

Smart Data Visualization and Augmented Analytics Makes Users Smarter!

Why is smart data visualization important? Consider how difficult it would be to put together a jigsaw puzzle if you were blindfolded. You might have access to all the pieces, but you could not truly see the picture unless you were able to visualize the outcome.

Data is like that. You know you have all the pieces but you can’t really see the meaning until you can put it all together in a way that makes sense. Imagine how much better it would be to take all those disconnected pieces of data in your business and put them together into a meaningful picture – one that will tell you a story and help you get to the bottom of an issue, solve a problem, find an opportunity or understand why and how things are working the way they are!

Smart Data Visualization does that! With augmented analytics tools that allow you to choose how to visualize the data based on the type of data and what you want to accomplish…well, you can bypass all that confusion and get right to the solution. You can achieve that ‘ah hah’ moment without frustration. You can interact easily with Advanced Analytics Software (without the skills of a data scientists or IT professional) and build a view that will tell a story using guided visualization and recommended data presentation. Guided recommendations are made based on data type, volume, dimensions, patterns and nature of data.

Visual Analytics tools enable users to identify relationships, patterns, trends and opportunities and to explore detailed data with simple drill down and drill through capabilities and make sense of data from all sources, with a guided approach that allows users to identify patterns and trends, and quickly complete analysis with clear results.

How does that sound? If you want the benefits of smart data visualization, you can start here!

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Data Discovery, Data Visualization: You Can Have it All! https://www.smarten.com/blog/augmented-analytics-software-frustration-free/ Wed, 03 Jul 2019 05:30:06 +0000 https://www.smarten.com/blog/?p=6380 Continued]]> Augmented Analytics Software: Frustration Free!

Self-Service Data Prep and Advanced Analytics for Business Users!

Are your business users trying their best to leverage advanced analytics tools, only to find that the software solution requires them to be an expert in data science? Do your users have to ask IT to professional analysts for data preparation assistance in order to ensure that they can access, prepare and use the data they need to analyze.

Data analysis does not have to be frustrating! With the right Augmented Analytics solution, business users can transform, shape, reduce, combine, explore, clean, sample and aggregate data, without the need for SQL skills, ETL or other programming languages. Self-Service Data Prep empowers every business user and allows them to prepare data for their analytics using tools that enable data extraction transformation and loading – ETL for business users! In other words, business users can quickly move data into the analytics system without waiting for IT. Users can access sophisticated, intuitive tools to compile and prepare data for use in analytics to test hypotheses, visualize data and create and share reports with other users.

Once the data is prepared and analyzed, business users can leverage visual analytics with augmented data visualization tools that will clearly and easily identify a problem and clarify a root cause. Business users can interact easily with Data Discovery Tools and analytics software and build a view that will tell a story using guided visualization and recommended data presentation. Guided recommendations are made based on data type, volume, dimensions, patterns and nature of data.

If this all sounds like something your team can use, Contact Us to discover the Benefits of Augmented Analytics now!

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Advanced Analytics for Business Users? That’s Right! https://www.smarten.com/blog/augmented-analytics-is-as-easy-as-1-2-3/ Tue, 25 Jun 2019 09:51:03 +0000 https://www.smarten.com/blog/?p=6376 Continued]]> Augmented Analytics is as Easy as 1-2-3!

Data Discovery and Data Exploration to Advance the Organization!

Data discovery is not data management. If one is to make the right decisions in business, one must engage in data exploration and data profiling. In other words, one must gather, prepare and analyze data to truly understand how the business is working, what must change, what activities and tasks support the goals of the business, and what the business should anticipate in the future in terms of revenue, risk, resources, financial investment, growth, products, location and all other aspects of business.

When a business chooses an Advanced Analytics solution and deploys that solution to every corner of the enterprise, it can leverage all of its data in real time and allow every user to look at a problem, prepare for a meeting, test hypotheses and theories and share information with other users in order to advance the objectives of the business and align all activities and decisions with the desired outcome.

The concept of Augmented Analytics is one that is crucial to business success. Augmented analytics allows business users to access and analyze data from numerous sources in a simple, intuitive interface that provides an underpinning of sophisticated tools, analytical techniques and algorithms. Augmented analytics allows for data prep, Smart Data Visualization and Assisted Predictive Modeling with the help of machine learning and natural language processing (NLP), so users need not be trained as data scientists to get to the heart of the data and find those elusive nuggets of information that will help them create, change and improve.

If your business and business users are ready for Advanced Analytics and better, more accurate decisions, Contact Us today.

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Predictive Analytics for Business Users! https://www.smarten.com/blog/learning-for-citizen-data-scientists-easy-and-swift/ Wed, 19 Jun 2019 10:08:46 +0000 https://www.smarten.com/blog/?p=6367 Continued]]> Learning or Citizen Data Scientists – Easy and Swift!Encourage Data Literacy and Achieve Results with Assisted Predictive Modeling!

If you want to include predictive analytics and forecasting in your planning process, there are numerous analytical techniques and algorithms at your disposal.

You may hire a data scientist or analyst to help you plan OR, if your organization wishes to provide analytical access to business users, you will want to implement a self-serve Advanced Analytics solution that incorporates all of the standard, comprehensive techniques in an easy-to-use interface. These tools will provide auto-suggestions and recommendations and allow the average business user to leverage sophisticated tools without the knowledge or skill of a data scientist.

You can encourage data literacy and learning for Citizen Data Scientists so that business users with average technical skills can employ sophisticated techniques like Naïve Bayes Classifier, ARIMAX Forecasting, Frequent Pattern Mining, Karl Pearson Correlation, Hierarchical Clustering Algorithm and more, without the need for an analytical degree!

In short, augmented analytics learning does not have to include years of study. By its nature, augmented analytics and Assisted Predictive Modeling can be performed with little training, because the system does the work, and will make the appropriate recommendations for the type of algorithm or technique that is most appropriate for a certain type of data analysis.

Contact Us today to find out Augmented Analytics can help your business users analyze data and achieve results to make the best business decision.

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What is Natural Language Processing & How Does it Benefit a Business? (Part 3 of 3 articles) https://www.smarten.com/blog/what-is-natural-language-processing-how-does-it-benefit-business/ Fri, 14 Jun 2019 11:27:37 +0000 https://www.smarten.com/blog/?p=6362 Continued]]> What is Natural Language Processing & How Does it Benefit a Business? (Part 3 of 3 articles)

Whether we know it or not, we use Natural Language Processing every day. It makes it easier for us to interact with computers and software and allows us to perform complex searches and tasks without the help of a programmer, developer or analyst.

In this, the last article in our three-article series we discuss Natural Language Processing and how it can benefit a business.

What is Natural Language Processing (NLP) Driven Analytics?

Natural Language Processing (NLP) is an integral part of today’s advanced analytics. If you have clicked in the search window on Google and entered a question, you know NLP! When NLP is incorporated into the business intelligence environment, business users can enter a question in human language. For example, ‘which sales team member achieved the best numbers last month?’ or ‘which of our products sells best in New York?’

The system translates this natural language search into a more traditional analytics query, and returns the most appropriate answer in the most appropriate form, so users can benefit from smart visualization, tables, numbers or natural language descriptions that are easy to understand.

How Does NLP-Based Analytics Benefit a Business Organization?

Perhaps the most important benefit of NLP is that it allows the business to implement Augmented Analytics in a self-serve environment with very little required training and ensures that users will adopt business intelligence and analytics as a tool to use every day.

NLP allows the enterprise to expand the use of business intelligence across the enterprise by offering business users an intuitive tool to ask for and receive crucial data and to understand the analytical output and share it with other users.

NLP opens and expands the data repositories and information in an organization in a way that is meaningful, and easy to understand, so data is more accessible and answers are more valuable. This will improve the accuracy of planning and forecasting and allow for a better overall understanding of business results.

Natural Language Processing helps business users sort through integrated data sources (internal and external) to answer a question in the way the user can understand, and will provide a foundation to simplify and speed the decision process with fact-based, data-driven analysis.

The enterprise can find and use information using natural language queries, rather than complex queries, so business users can achieve results without the assistance of IT or business analysts.

NLP presents results through Smart Visualization and contextual information delivered in natural language. Because these tools are easy to use and to understand, users are more likely to adopt them and to add value to the organization.

With NLP searches and queries, business users are free to explore data and achieve accurate results and the organization can achieve rapid ROI and sustain low total cost of ownership (TCO) with tools as familiar as a Google search.

Users can combine NLP with Plug n’ Play Predictive Analysis or Assisted Predictive Modeling so the organization can achieve data democratization.

NLP and the Advanced Data Discovery tools it supports can provide important, sophisticated tools in a user-friendly environment to suggest relationships, identify patterns and trends, and offer insight to previously hidden information so business users can ‘discover’ subtle, crucial problems and opportunities.

Natural Language Processing (NLP) is an integral part of today’s Advanced Analytics. It establishes an easy-to-use, interactive environment where users can create a search query in natural language and, as such, will support user adoption and provide numerous benefits to the enterprise.

For more information on this topic read, ‘What is Clickless Analysis? Can it Simplify Adoption of Augmented Analytics? (Part 1 of 3 articles)’ and, ‘What is Search Analytics and Can it Improve Self-Serve Data Discovery? (Part 2 of 3 articles)’

Original Post: What is Natural Language Processing & How Does it Benefit a Business? (Part 3 of 3 articles)

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Business Users Can Enjoy Self-Serve Data Preparation! https://www.smarten.com/blog/2019/06/13/june-13-2019/self-serve-data-preparation/ Thu, 13 Jun 2019 07:57:06 +0000 https://www.smarten.com/blog/?p=6358 Continued]]> Self-Serve Data Preparation for Business Users!

The Benefits of Self-Serve Data Prep for YOU and Business Users!

I won’t lie to you! The benefits of augmented analytics that includes self-serve data preparation for business users…well, those benefits are truly impressive! So, let’s cut to the chase. With self-serve data preparation tools, you can:

  • Improve business analyst and business user productivity
  • Reduce the time to prepare data for analysis
  • Reduce user dependence on analysts, ETL and SQL expertise
  • Engender social BI and data popularity
  • Balance agility with data governance and data quality

So, why wouldn’t your organization want to implement Data Preparation Software that is easy enough for every business user? Probably because your IT team and/or your executive management team believe it is a) too expensive, b) will take a long time to implement, c) will not be something users CAN adopt because of the need for expertise or skills.

To those naysayers, I would respond that they simply haven’t found the right Advanced Analytics solution! With the right solution and features, business users with average skills can perform data preparation tasks with ease and get results quickly. These easy-to-use tools allow business users to transform, shape, reduce, combine, explore, clean, sample and aggregate data, without the need for SQL skills, ETL or other programming language.

Self-Service Data Prep empowers every business user and allows them to prepare data for their analytics using tools that enable data extraction transformation and loading (ETL) so users can quickly move data into the analytics system without waiting for IT or data scientists.

Give your business users the power to access sophisticated, intuitive tools and compile and prepare data for use in Data Analytics, so they can test hypotheses, visualize data and create and share reports with other users. These simple tools provide recommendations and guidance so your business users will get the output they expect and can make the right business decision.

If you want the benefits of self-serve data preparation, you can Start Here!

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The Benefits of Augmented Analytics Are Many! https://www.smarten.com/blog/enjoy-the-advantages-of-advanced-analytics/ Thu, 06 Jun 2019 09:15:07 +0000 https://www.smarten.com/blog/?p=6350 Continued]]> Enjoy the Advantages of Advanced Analytics!

What are the Advantages of Augmented Analytics and How Can My Business Benefit?

It is hard to overstate the benefits and advantages of advanced analytics. If an enterprise takes the time to review and document requirements for an advanced analytics solution and chooses wisely, it can improve its bottom line, optimize competitive strategies, plan for new products and pricing initiatives, establish marketing channels and messaging for target markets and customers and plan for resources, training, financial investment, supplier and partnership opportunities and more.

And, the Benefits of Augmented Analytics provide enough more advantages. An augmented analytics solution is designed to encourage user empowerment and user adoption and to enable data sharing. Augmented analytics support allows the organization to produce fast, dependable insights and improve the value of business analysis across the enterprise.

If you can democratize Advanced Analytics and distribute these tools to all business users, you can allow every team member to have access to and share data and to gather, integrate and analyze data to drive fact-based decisions across the enterprise. Users can be creative and the organization will engender power users and data popularity and support timely, accurate decisions by providing insight and perspective.

Analysts and IT team members will have more time to attend to critical projects and strategic initiatives and business users will become more data literate.

If your organization leverages the Benefits of Advanced Analytics, your business will enjoy maximum results, with minimal training requirements, minimum implementation time and minimal support and you will achieve rapid ROI and low TCO.

Contact Us to discover the Advantages of Augmented Analytics now!

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Smarten Advanced Analytics is a Silver Sponsor for Gartner Data & Analytics Summit, June 2019 in Mumbai, India https://www.smarten.com/blog/smarten-advanced-analytics-is-a-silver-sponsor-for-gartner-data-analytics-summit-june-2019-mumbai-india/ Wed, 05 Jun 2019 08:21:18 +0000 https://www.smarten.com/blog/?p=6346 Continued]]> Smarten Advanced Analytics is a Silver Sponsor for Gartner Data & Analytics Summit, June 2019 in Mumbai, India

Smarten, an advanced analytics service provider, has announced that it will act as a Silver Sponsor for the Gartner Data & Analytics Summit 2019, June 10 through June 11 in Mumbai, India where it will demonstrate its Smarten Advanced Analytics solution and its product roadmap for the future of the Smarten Augmented Analytics product suite.

Kartik Patel is the CEO of Smarten. “The Gartner Data & Analytics Summit attracts the leading minds of the analytical community, including information architects, CAOs, CDOs, data analysts and executives from many functional disciplines,” says Patel. “We are pleased to represent the Smarten Advanced Analytics vision as a Silver Sponsor for this important Summit.”

The Smarten advanced analytics team will participate in the Gartner Data & Analytics Summit and engage with partners and clients to demonstrate the Smarten approach to Augmented Analytics and join with Summit attendees to explore the future of analytics and fact-based business strategy. The Summit will take place at the Renaissance Mumbai Convention Centre Hotel in Mumbai, India on June 10 and June 11, 2019.

Smarten clients, partners and technology innovators are invited to visit the Smarten team at Booth No. S2 to discover the Smarten advanced analytics product and explore the Smarten roadmap that promises to lead this product into the exciting future of advanced analytics and Data Literacy.

CEO Patel, says, “As the Smarten product evolves, it is truly exciting to see the ways in which business users, data scientists, IT staff and business managers have embraced and adopted advanced analytics as part of the day-to-day and strategic business decision process.”

The Smarten self-serve advanced analytics solution includes smart data visualization, assisted predictive modeling and self-serve data preparation, all of which support the transformation of business users into Citizen Data Scientists. The product roadmap includes Smarten Auto Insights, to free business users and reduce the time and skills required to produce accurate, clear results, quickly and dependably. Auto Insights leverages machine learning allowing the user to collect and analyze data with the guided assistance of a ‘smart’ solution. The Smarten roadmap also includes Natural Language Processing (NLP) based ‘Clickless Analytics‘ that further refine and simplify the search analytics process to create tools that are truly self-serve and enable creativity, innovation and user empowerment and accountability.

The Smarten Team of India-based experts combines business intelligence, business analysts, data scientists, engineers and data warehouse (DWH) professionals, working together in a dedicated team environment to re-imagine advanced analytics and support data democratization across the entire enterprise.

Join the Smarten Team at the Gartner Data & Analytics Summit 2019 June 10 through June 11, 2019 at the Renaissance Mumbai Convention Centre Hotel in Mumbai, India, Booth No. S2 and find out how Smarten augmented analytics can engender and simplify analytics throughout the organization.

Read More: Smarten Advanced Analytics is a Silver Sponsor for Gartner Data & Analytics Summit, June 2019 in Mumbai, India

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Augmented Data Discovery Offer Guidance to Business Users! https://www.smarten.com/blog/advanced-data-discovery-does-not-have-to-be-difficult-to-use/ Thu, 23 May 2019 09:36:57 +0000 https://www.smarten.com/blog/?p=6341 Continued]]> Advanced Data Discovery Does Not Have to Be Difficult to Use!

Smart Data Discovery Takes the Guesswork Out of Advanced Analytics!

If you are implementing a data democratization project and you want the most sophisticated, easiest advanced data discovery solution so your business users can get the most out of the initiative and add the most value to the enterprise, you definitely want to look at a data discovery tool that provides augmented analytics.

Augmented Data Discovery tools offer guidance, auto-suggestions and recommendations to business users do not have to guess at what techniques to use for data visualization, or predictive analytics. Smart data discovery allows users to leverage sophisticated tools, algorithms and analytical techniques without the expertise of an IT staff member or a data scientist, so users are empowered and more likely to adopt the tools and use them to benefit the organization and their teams.

Advanced analytics tools DO have to be founded on comprehensive analytical standards and practices but they DO NOT have to be difficult to use! Today’s data discovery tools are flexible, intuitive, mobile and designed to bring Advanced Analytics to every user in the organization.

If you would like to find out how you can successfully adopt Advanced Analytics Tools, Contact Us today.

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Successful Augmented Analytics Initiatives Do Not End with Implementation! https://www.smarten.com/blog/successful-augmented-analytics-initiatives-do-not-end-with-implementation/ Tue, 21 May 2019 09:48:03 +0000 https://www.smarten.com/blog/?p=6337 Continued]]> Successful Augmented Analytics Initiatives Do Not End with Implementation!

The successful implementation of an augmented analytics solution for business users is not just about choosing a cost-effective tool and completing a timely deployment, nor does the process stop with training. In order to get business users to embrace and adopt self-serve augmented data discovery tools, the enterprise must approach the implementation with appropriate change management processes.

If you want a business user or a team to align with the Citizen Data Scientist philosophy, you must engage the enterprise at every level. Senior managers and executives must understand why it is important to build a data-driven business environment and how that environment can help the organisation to succeed. The enterprise must require that analysis and data is used in presentations and decision-making, and it must reward this evolution appropriately. Creating Analytics Translator roles within the organisation is also helpful. Recognise and organise around this liaison role by allowing power users and those who can build a bridge between data scientists, IT and the business user base.

If the organisation is to get the most out of data democratization and augmented analytics, it must not ignore the importance of change management in rolling out the tools and the process. Making Data Analytics part of the day-to-day and strategic decision process is key. Making sure that business users have access to the data they need to successfully gather and analyse data and to share, report and publish data is a necessity, and the IT staff, data analysts and managers must be available to encourage and support the use of these tools and to answer questions and clarify usage and processes as necessary.

When data analysis is presented, team managers and executives should take the next step by establishing metrics and assigning actions to staff members to address and improve results, tackle issues or capitalise on the opportunities identified the analytics. In this way, the organisation can ensure follow through and embed the analysis process and the data focus within every team goal and objective.

To successfully implement and leverage Augmented Analytics in the organisation, the enterprise and its managers and executives must commit to the daily use of these tools and must assess workflow and processes to incorporate the data-driven philosophy into every process and goal. It must encourage and grow data literacy by transforming business users into Citizen Data Scientists and Analytics Translators so that all team members are comfortable with the tools and the concepts.

Original Post: Successful Augmented Analytics Initiatives Do Not End with Implementation!

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Data Science and Predictive Analytics Made Simple! https://www.smarten.com/blog/the-importance-of-data-analysis-cannot-be-overstated/ Mon, 20 May 2019 09:20:33 +0000 https://www.smarten.com/blog/?p=6332 Continued]]> The Importance of Data Analysis Cannot be Overstated!

Augmented Analytics Tools to Support Business Users and the Organization!

The importance of data analysis cannot be overstated, but if the enterprise does not choose the right data analysis tool, it will not achieve its potential and it is likely to frustrate the business users who are now expected to participate in the analytical process.

Data Science and Predictive Analytics must be made simple, but based on sophisticated features that will enable data modeling and predictive analytics technique. Data exploration tools that capitalize on the use of Natural Language Processing allow users to submit a query using natural language and to receive results in the same manner so there is no need for interpretation of data by a data scientist.

Business Analytics tools should also recognize the importance of data visualization and help business users to choose from among the many data visualization types so that each user receives recommendations for how best to visualize the data, considering the type of data being analyzed.

Contact Us today to find out Augmented Analytics Tools can help your business users analyze data and receive results in a way that is meaningful to them and that will help them to make the best business decision to support a positive outcome.

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Predictive Analytics Use Cases: Envision Success with Comprehensive Planning and Forecasting! https://www.smarten.com/blog/predictive-analytics-use-cases-envision-success-with-comprehensive-planning-and-forecasting/ Fri, 17 May 2019 12:03:58 +0000 https://www.smarten.com/blog/?p=6321 Continued]]> Predictive Analytics Use Cases: Envision Success with Comprehensive Planning and Forecasting!

When it comes to using Predictive Analytics and a self-serve augmented analytics environment, businesses often want to sell the management team on these tools by suggesting real-world use cases that reflect the needs of the organization and illustrate how advanced analytics can help business users and the organization at large with accurate, efficient insight into the planning and forecasting process, and the ability to identify trends and patterns, understand target custom buying behavior, predict fraud and loan default, combine internal and external data analytics, manage quality, improve demand planning and marketing processes and manage human resource attrition.

These are just some of the examples of use cases that effectively illustrate how your business can benefit from predictive analytics in real-world scenarios. Whether you need to anticipate and plan for equipment maintenance, target online customers, control customer churn, or identify ways to cross-sell and upsell customers on existing and new products and services, these predictive analytics tools can help you to optimize your marketing budget and your resources and mitigate risk and market missteps.

The benefits of advanced analytics and assisted predictive modeling are too numerous to provide a complete list here. Whether you are using these tools for day-to-day or strategic planning, self-serve augmented analytics provides easy-to-use tools for business users and analysts alike and these tools can be used to achieve a competitive advantage and make better, more educated business decisions.

Gain insight into customers, competition, resource allocation, investments, new product and service offerings, supply chain and production issues and more.

Learn More: Augmented Analytics Use Cases

Explore these use cases and discover how predictive analysis, and self-serve, advanced analytics tools can help you achieve your goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to find out how you can create your own success story with assisted predictive modeling and augmented analytics.

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Predictive Analytics Use Case: Online Target Marketing! https://www.smarten.com/blog/predictive-analytics-use-case-online-target-marketing/ Fri, 17 May 2019 09:54:59 +0000 https://www.smarten.com/blog/?p=6314 Continued]]> Predictive Analytics Use Case: Online Target Marketing!

To make the most out of online marketing, every organization must target the customers with the most promising profile. Businesses must understand what makes customers click on an ad, and what will compel them to make a purchase. Who is most likely to purchase your product or service? Where do they live, how old are they, how many children do they have (if any), what is their annual income, and what interest or driving force is the best fit for what you have to offer?

Predictive analytics can help the business to understand online buying behavior, and when, where and how to serve ads, market products and offer discounts or other incentives.

Assisted predictive modeling and advanced analytics incorporates data from social media, email marketing campaigns, Google analytics, apps and web sites, ecommerce channels, sales data and more to analyze products, pricing, customer geography, preferences, demographics and other data. It factors in seasonality, competitive positioning and many other considerations to help the business design campaigns that better target the ideal customer with a concise message to achieve sales conversion.

Whether you enterprise is engaged in online retail, media services, subscription services, entertainment, health insurance, financial services, life, home or auto insurance or professional services like IT consulting or event planning, predictive analytics can help you understand your clients and gain insight into what will make your customers more loyal, what types of campaigns will bring in the most new clients and more!

Use Assisted Predictive Modeling and Predictive Analytics to paint a clear picture, to test theories and hypotheses, to optimize the cost per click and improve customer target and sales conversion. Predictive analytics will help you optimize your marketing budget and improve brand loyalty.

Learn More: Online Target Marketing Use Case

View the Online Target Marketing Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Contact Us today to find out how your industry or business function can apply Predictive Analytics to improve results and forecasting.

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Predictive Analytics Use Case: Predictive Analytics Using External Data! https://www.smarten.com/blog/predictive-analytics-use-case-predictive-analytics-using-external-data/ Fri, 17 May 2019 07:18:58 +0000 https://www.smarten.com/blog/?p=6310 Continued]]> Predictive Analytics Use Case: Predictive Analytics Using External Data!

There is a lot of information within your enterprise, and being able to analyze that information is crucial to decision-making and to managing your business and predicting results with efficiency and accuracy. But, when it comes to information, the average business may have quite a bit of data within its walls but, it often falls short of painting a complete picture because the data it needs is not maintained within its internal systems.

With the right advanced analytical tools, a business can combine internal and external data to understand and anticipate trends, patterns and factors that will affect the bottom line, the supply chain, resource and location planning and other aspects of business success.

If you can easily integrate data from sources outside the business, you can provide a more comprehensive picture for predicting and forecasting results and anticipating the needs of the market.

External data may come from industry or government sources but your business does not have the time or the resources to integrate this data manually and analyze that data. But, with the right augmented analytics solution, this process is simple. External data can be integrated with internal data sources and analyzed efficiently and quickly.

Where external factors like GDP can affect the success of the organization, the enterprise should consider these issues in the planning process and augmented analytics can help you by merging internal data and external data. These techniques can be beneficial for infrastructure planning, construction, highway planning and management, government, agriculture, weather, travel and city planning, and can help the business to plan for resources, locations, supply chain, marketing, inventory, pricing, risk management, maintenance and other planning activities.

Your business can leverage Assisted Predictive Modeling and Predictive Analytics to integrate and analyze internal and external data and identify key issues, risks, opportunities, scheduling and seasonal issues and resource management.

Learn More: Predictive Analytics Using External Data

View the Predictive Analytics Using External Data Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to explore the potential of predictive analytics using external data.

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Predictive Analytics Use Case: Marketing Optimization! https://www.smarten.com/blog/predictive-analytics-use-case-marketing-optimization/ Thu, 16 May 2019 12:31:24 +0000 https://www.smarten.com/blog/?p=6306 Continued]]> Predictive Analytics Use Case: Marketing Optimization!

Using marketing and advertising dollars to target the general market is not a wise use of funding. Every business today is challenged to do more with less and marketing budgets are no exception. In order to reach the right target and convert prospects into sales, a business must create and distribute messaging to the right target audience, in the right way, at the right tie.

Without a fundamental understanding of how a customer makes a buying decision and how customers choose a product or service, the marketing and advertising process is based only on guesswork, and that guesswork is bound to result in lost revenue and poor optimization of the marketing budget.

Augmented analytics supports the identification of a target audience and identifies buying behaviors. It helps the business to determine what type of marketing and marketing channels will be the most effective to reach the target customer. This insight allows the business to customize targets for a region or market and identify appropriate messaging and marketing vehicles for seasonality and other factors.

Predictive analytics can help a business develop a profile for a target customer and segment, combine external and internal data like macroeconomics and competitive data, and define marketing and advertising messages and techniques for each segment to optimize the marketing budget and improve the competitive advantage.

Use Assisted Predictive Modeling and Predictive Analytics to gain critical insight into target customers and define the marketing and channels best suited for those targets.

Learn More: Marketing Optimization

View the Marketing Optimization Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to discover the benefits of predictive analytics and marketing optimization.

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Predictive Analytics Use Case: Loan Approval! https://www.smarten.com/blog/predictive-analytics-use-case-loan-approval/ Thu, 16 May 2019 09:58:15 +0000 https://www.smarten.com/blog/?p=6302 Continued]]> Predictive Analytics Use Case: Loan Approval!

Perhaps the greatest risk to a lending organization is that presented by loan applicants who are unprepared to fulfill the long-term obligation of paying off a loan. Banks and other lenders spend a lot of time and energy trying to identify the perfect profile for a borrower so they can make the right decision and avoid costly loan defaults and the expense and resources required to take legal action.

Making the right decision and choosing the perfect customers is crucial. To achieve consistent results, the business must have a dependable process for attracting the right clientele and reviewing, approving and managing loans.

Predictive analytics can be a crucial piece of the puzzle in supporting the loan approval process and monitoring and managing loans throughout the life cycle of the contract.

Augmented analytics helps the business to refine these processes using algorithms and analytical techniques so the company can see the big picture and target the right demographics with marketing messages. These techniques also help the business to identify common risk factors across multiple variables and anticipate loan defaults or issues long before the customer defaults on the loan. Advanced analytics solutions are perfect for credit unions, banks, insurance businesses, auto and real estate loan processes.

When applied to the loan approval process, predictive analytics can improve productivity and optimize resources and it can decrease loan defaults and help the business to capitalize on available funds.

Use Assisted Predictive Modeling to define the perfect loan applicant profile and to monitor and manage existing loans to anticipate and mitigate loan defaults.

Learn More: Loan Approval

View the Loan Approval Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to explore the benefits of predictive analytics and find out how your business can optimize the loan approval process.

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Predictive Analytics Use Case: Human Resource Attrition! https://www.smarten.com/blog/predictive-analytics-use-case-human-resource-attrition/ Thu, 16 May 2019 07:02:53 +0000 https://www.smarten.com/blog/?p=6298 Continued]]> Predictive Analytics Use Case: Human Resource Attrition!

You probably know how much it costs to recruit and hire a team member but, do you know how long that team member is likely to stay in your employ and what factors will cause them to stay with the company or seek greener pastures? If you can’t anticipate the need for resources, you are likely to be short-handed at the wrong time, or to experience frequent turnover that will significantly impact your business success and your ability to get the job done.

When a team member leaves the company, the business must find, hire and train a new individual and, allow for ample transition time to get that new person up to speed. In the interim, there is loss of productivity and the risk of crucial mistakes.

Advanced analytics can help you to identify areas of dissatisfaction and understand the activities, processes, benefits, training and the work environment that encourages productivity and ensures employee satisfaction. Augmented analytics can also identify the need for training, the types of jobs that are most at risk of frequent turnover, the key skills for a particular position and the probability of advancement.

With predictive analytics the business can develop a clear picture of the ideal profile that will result in a good fit for a position and the types of training and mentoring required to assure that the team member will add the most value to the organization. These tools can help the organization to improve the recruiting and hiring process, and ensure that the enterprise mitigates attrition and increases employee loyalty and job satisfaction.

You can use Assisted Predictive Modeling to create a clear picture, and to develop effective benefits and training packages and attract and retain the right team members.

Learn More: Human Resource Attrition

View the Human Resource Attrition Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to find out how your business can leverage predictive analytics to plan and manage resources.

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Predictive Analytics Use Case: Customer Targeting! https://www.smarten.com/blog/predictive-analytics-use-case-customer-targeting/ Wed, 15 May 2019 11:53:49 +0000 https://www.smarten.com/blog/?p=6291 Continued]]>  

Predictive Analytics Use Case: Customer Targeting!

Even with an unlimited budget, it would not be a wise decision for a business to target every customer in the market. If an enterprise is to succeed, it must understand its products and services and it must know the profile of the customer it is targeting.

If an organization is going to successfully target customers and make optimal use of its marketing budget, it must understand customer buying behavior, and categorize its products and services to target the right customer segments and preferences. If you can identify the reasons a certain customer buys a product or service, you can create products, marketing messages, and advertising to target those customers that are most likely to buy and to return and buy your products again.

Augmented analytics provides easy-to-use tools so business users can identify buying frequency, and understand the variables that influence a customer and cause them to buy a product or service. The business can develop a clear picture of the demographics (age, income, interests, geography, gender and education) that inform product and service purchases and the most effective marketing messages for a particular customer segment.

Predictive analytics can identify a trend or pattern so that the organization can anticipate that the market, or buying behavior is changing. Whether your business is real estate, retail, auto sales, hospitality, or entertainment, understanding your customer and why and when they buy is imperative and creating a clear profile of your target customer will allow you to directly, and effectively address their needs.

You can use Assisted Predictive Modeling and Predictive Analytics to paint a clear picture of your customers and to optimize resources, marketing budgets and inventory.

Learn More: Customer Targeting 

View the Customer Targeting Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to find out how predictive analytics can help your industry or business function to improve results and plan and forecast more effectively and accurately.

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Predictive Analytics Use Case: Maintenance Management! https://www.smarten.com/blog/predictive-analytics-use-case-maintenance-management/ Wed, 15 May 2019 09:24:19 +0000 https://www.smarten.com/blog/?p=6287 Continued]]> Predictive Analytics Use Case: Maintenance Management!

Businesses do not want to be surprised by downtime, equipment failure or the cost of unanticipated equipment replacement. If the enterprise can anticipate the need for equipment maintenance and downtime, it can plan more effectively for product output, resource requirements and expenses.

For a business that must purchase, maintain and replace equipment, it is crucial to anticipate resource requirements and plan for related expenses for hours on the job as well as the training required to maintain existing and new equipment.

With predictive analytics, the business can leverage data from various systems and software to take the guesswork out of production equipment maintenance and anticipate routine maintenance. The enterprise can plan to order parts and schedule downtime for equipment. For a business that places and maintains equipment in a customer location, this type of planning is just as crucial. The enterprise does not want to risk its reputation with unanticipated downtime or the loss of revenue for its customers.

Augmented analytics tools can be very beneficial for planning in the manufacturing and production environment, in utility and infrastructure businesses and in service industries. Forecasting necessary maintenance and equipment replacement and ensuring that the business is ready with resources and funding is critical and will help the business to optimize parts and inventory management, schedules and training, costs, customer satisfaction and revenue.

Your business can leverage Predictive Analytics to plan for and anticipate maintenance, investment, changes in resources and training requirements and supply chain management for parts, shipping, etc.

Learn More: Maintenance Management

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to explore the benefits of predictive analytics for maintenance management and other crucial planning and processes.

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Predictive Analytics Use Case: Product and Service Cross-Selling and Upselling! https://www.smarten.com/blog/predictive-analytics-use-case-product-and-service-cross-selling-and-upselling/ Wed, 15 May 2019 06:43:29 +0000 https://www.smarten.com/blog/?p=6282 Continued]]> Predictive Analytics Use Case: Product and Service Cross-Selling and Upselling!

If you want to make the most of your product and service portfolio, you must look for opportunities to cross-sell and upsell your clients and to bundle services to attract new clients. With the right information, business managers can leverage customer satisfaction to cross-sell and upsell products and services, and to increase revenue and brand loyalty.

Every organization needs a comprehensive understanding of why, when and how their customers buy and what makes a customer decide to try another product, switch brands or accept an offer to add a product or service to an existing purchase.

Predictive analytics and assisted predictive modeling solutions make it easy for business users to perform customer and market analysis and identify specific customer characteristics or specific products and services that will most likely result in bundled purchases, repeat purchases or purchases of products or services that specifically target their buying behaviors and decisions. If the business can market and advertise and reach out to customers to get them to make that purchasing decision at the right time and in the right way, the business is more likely to succeed.

Augmented analytics can help the enterprise to develop a better understanding of customers and their buying behavior and expand that knowledge to identify opportunities for new products and services. It can also help to develop appropriate marketing messages to target specific customer segments and demographics.

Use Predictive Analytics to test theories and hypotheses, and identify opportunities for cross-selling and upselling in your product and service portfolio.

Learn More: Product and Service Cross-Sell and Upsell

View the Product and Service Cross-Selling and Upselling Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to find out how Predictive Analytics can help your business to capitalize on cross-selling and upselling opportunities in your market and customer base.

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Predictive Analytics Use Case: Demand Planning! https://www.smarten.com/blog/predictive-analytics-use-case-demand-planning/ Tue, 14 May 2019 12:21:14 +0000 https://www.smarten.com/blog/?p=6277 Continued]]> Predictive Analytics Use Case: Demand Planning!

If a business wishes to optimize inventory, production and supply, it must have a comprehensive demand planning process; one that can forecast for customer segment growth, seasonality, planned product discounting or sales, bundling of products, etc. In order to predict and forecast and assure product or service availability, the business must also look at the dependability of suppliers, shipping and the purchasing of parts that make up the products. It must also plan for appropriate resources and training and, where necessary, plan for new products.

Predictive analytics uses data integrated from appropriate data sources, and augmented analytics allows the business to anticipate production demands, plan for new locations and markets and predict targeted customer buying behavior and changes in product demand across multiple market segments. Assisted predictive modeling is easy enough for every business user and will allow team members to plan at the department, divisional, unit and company level for manufacturing, logistics, travel and hospitality, and product and service sales and marketing.

Businesses invest significant funding in inventory and warehousing and shipping and every step along the supply chain. Where demand can be optimized, the business will alleviate the issue of inventory shortages or overages and will optimize production and distribution to improve the bottom line and ensure that customers are satisfied.

Use Augmented Analytics and Assisted Predictive Modeling to accurately anticipate demand and optimize resources.

Learn More: Demand Planning

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to explore the benefits of Augmented Analytics and Assisted Predictive Modeling for demand planning.

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Predictive Analytics Use Case: Quality Control! https://www.smarten.com/blog/predictive-analytics-use-case-quality-control/ Tue, 14 May 2019 08:52:41 +0000 https://www.smarten.com/blog/?p=6273 Continued]]> Predictive Analytics Use Case: Quality Control!

It is easy to understand the importance of producing quality products and services and the impact of poor quality on business reputation. If your business does not have adequate quality controls in place, it will lose market share and may even expose the enterprise and its team to legal liability.

Anticipating quality issues and monitoring and managing quality is crucial to business success. Having solid processes in place will optimize resources and budgets and ensure swift and accurate execution of new product rollout, product and service delivery and the consistency and quality of the business offerings.

Businesses work hard to acquire customers and to sustain that customer base and compete in the market of choice but quality issues will negatively impact the business reputation and cause loss of revenue and erosion of the customer base.

Using advanced analytics to identify quality issues will improve production processes, protect the business against liability claims and allow the organization to more easily upgrade products, create new products and services and anticipate issues along the way. Advanced analytics and predictive analysis can be used to achieve these goals in an IT consulting business, in telecommunication, in manufacturing and in many other industries.

Your business can use Augmented Analytics and Predictive Analysis to predict quality issues, monitor and manage quality standards and improve and optimize performance.

Learn More: Quality Control

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today and find out how Augmented Analytics can improve quality and decrease the incidence of issues, and improve the consistency of products and services.

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Predictive Analytics Use Case: Fraud Mitigation! https://www.smarten.com/blog/predictive-analytics-use-case-fraud-mitigation/ Mon, 13 May 2019 11:34:27 +0000 https://www.smarten.com/blog/?p=6269 Continued]]> Predictive Analytics Use Case: Fraud Mitigation!

Fraud costs businesses millions of dollars per year, and the resources and effort required to mitigate fraud can take precious focus way from core business initiatives. But enterprises must address this critical issue, if they are to protect their interests and the interests of their partners and customers. While one may think of fraud most commonly associated with financial and banking organizations or IT functions or networks, industries like healthcare, government and public sector are also at risk. And fraud can take many forms and affect the supply chain in numerous ways.

The good news is that a business can more readily and effectively anticipate fraud by identifying the signs and signals that indicate a problem and creating strategies and processes to monitor and manage risk so that the incidence of fraud is significantly decreased.

An enterprise can leverage predictive analytics to identify the most likely areas and actors that will be involved in fraudulent activities and by developing fraud detection models, the enterprise can reduce the cost and the negative impact to the business reputation and to the bottom line. Businesses that are proactive in identifying these risks can better optimize resources and respond to changing trends and patterns.

With the right tools, algorithms and assisted predictive modeling techniques, your business can create fraud behavior models and monitor ongoing activity to get ahead of the problem and to identify critical gaps or holes in processes, systems and activities.

Use Predictive Modeling and Predictive Analytics to create a profile of fraud risk and to manage and monitor fraud.

Learn More: Fraud Mitigation

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today and find out how Predictive Analytics can increase the accuracy of predictions and improve risk avoidance and fraud monitoring processes.

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Predictive Analytics Use Case: Customer Churn Analysis! https://www.smarten.com/blog/predictive-analytics-use-case-customer-churn-analysis/ Mon, 13 May 2019 08:15:41 +0000 https://www.smarten.com/blog/?p=6263 Continued]]> Predictive Analytics Use Case: Customer Churn Analysis!

Every business understands the importance of acquiring and sustaining its customer base. In a competitive, global economy, even the smaller local businesses can lose customers to competitors on the other side of the world. The cost of acquiring a new customer includes marketing and advertising, resources and personnel, customer support, search engine optimization and more. The cost to sustain a customer relationship is equally important and encompasses customer care, online and personal support, the cost of introducing new products and services, the cost of targeting specific customers with discount, offers and outreach designed to keep the customer engaged and interested.

Customer churn is the bane of business success and, while you may think that losing customers is inevitable, you can mitigate this problem by understanding which customers are likely to leave and go to the competition, and where you may have reached ‘saturation’ with products and services.

Customers don’t usually tell you that they are leaving or, more importantly, they don’t tell you WHY they are leaving. They simply close their account or stop visiting your site. Self-serve, assisted predictive modeling and predictive analytics can help you to identify the customers who are most likely to leave and allow you to develop processes and strategies, as well as new marketing, new products and services, and other strategies that will improve customer retention and reduce customer churn.

Predictive Analytics can help you identify those customers who are most likely to leave and improve marketing messaging and marketing campaigns to get to the issues your customers have and to create new services and products that will help you retain a customer.

Use Predictive Analytics to identify at risk customers and issues that will impact customer churn and customer retention.

Learn More: Customer Churn

View the Online Customer Churn Use Case Slide Share

We invite you to explore other use cases and discover how predictive analytics, and assisted predictive modeling can help your business to achieve its goals.

Learn more about Augmented Analytics, its uses, techniques and applications.

Contact Us today to find out how your business users can leverage Predictive Analytics to increase the accuracy of predictive analysis and forecasting.

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Elegant MicroWeb (Smarten SSDP Solution) Named as a Representative Vendors in Gartner April 2019 ‘Market Guide for Data Preparation Tools’ https://www.smarten.com/blog/elegantmicroweb-smarten-ssdp-solution-named-as-a-representative-vendors-in-gartner-april-2019-market-guide-for-data-preparation-tools/ Fri, 10 May 2019 08:18:32 +0000 https://www.smarten.com/blog/?p=6259 Continued]]> Elegant MicroWeb (Smarten SSDP Solution) Named as a Representative Vendors in Gartner April 2019 'Market Guide for Data Preparation Tools'

Elegant MicroWeb is included in the Gartner Market Guide for Data Preparation Tools, published on April 17, 2019. The Elegant MicroWeb (Smarten SSDP solution) is listed as a Representative Vendor in the Gartner Market Guide for Data Preparation.

The CEO of Elegant MicroWeb, Smarten, Kartik Patel says, “We are truly honored to be recognized by Gartner. We believe that our focus on self-serve data preparation and our product roadmap assures the continued evolution and advancement of data democratization, and business user data literacy. Smarten offers numerous Advanced Analytics Benefits to an organization, as well as providing a solid foundation for fact-driven business analysis and decisions that will result in a positive outcome for the businesses using these tools.”

The Smarten Self-Serve Data Preparation (SSDP) Tool is a crucial tool for business users who want and need to make sense of augmented analytics and a central factor in advancing business empowerment and accountability for all team members. The Smarten SSDP solution allows users to transform, shape, reduce, combine, explore, clean, sample and aggregate data, without the need for SQL skills, ETL or other programming language.

“Self-Service Data Prep empowers every business user,” says Patel. “These tools allow business users with average skills to prepare data for analysis and enable data extraction transformation and loading, so users can quickly move data without waiting for the assistance of a data scientist or an IT team member.”

Smarten SSDP is part of the innovative Smarten suite that includes Smart Data Visualization, Assisted Predictive Modeling and a roadmap to Natural Language Processing (NLP) including machine learning and other sophisticated features and functionality.

More information on the data preparation tools market is available in the Gartner report: Market Guide for Data Preparation Tools, Published: 17 April 2019 ID: G00386354, Analyst(s): Ehtisham Zaidi, Sharat Menon

Disclaimer: Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization, and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Read More: Elegant MicroWeb (Smarten SSDP Solution) Named as a Representative Vendors in Gartner April 2019 ‘Market Guide for Data Preparation Tools’ 

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Augmented Analytics is Key to User Adoption! https://www.smarten.com/blog/2019/05/07/may-07-2019/augmented-analytics-for-all/ Tue, 07 May 2019 06:40:29 +0000 https://www.smarten.com/blog/?p=6255 Continued]]>  

Advanced Analytics for Every Business User in Your Biz!

Augmented Analytics: The Advanced Analytics Solution to Ensure User Adoption!

The concept of advanced analytics can seem out of reach for many businesses. Business execs and managers often picture a team of data scientists and IT staff busily analyzing data and, included in that picture, they imagine the bags full of cash required to fund that team of professionals.

The reality is that every business needs Advanced Analytics AND that reality is not out of reach for any organization. You don’t need a team of data scientists and IT staff. You DO need advanced analytics tools that include augmented analytics. These tools make it possible for business users with average skills to leverage sophisticated features, algorithms and analytical techniques to make business decisions and to accurately plan, forecast and manage the business.

There is no reason to limit access to data preparation software or advanced predictive analytics to data scientists, analysts and IT staff. The evolution of Augmented Analytics provides guidance and recommendations to help users decide how to visualize data, how best to prepare data and what predictive analytical techniques to use to achieve the best, clearest, results.

If you want to encourage the adoption of advanced analytics tools, a solution with augmented analytics capability is the best way to increase Data Literacy and engender data democratization within your enterprise. Users can easily share data and create innovative views of data so that other business users will learn from them and the organization can encourage power users and data popularity.

If you want to enable the adoption of advanced analytics and improve the value of every team member within your organization, you should select an augmented analytics tool that is designed for use by all business users. Augmented Analytics Benefits

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Advanced Analytics Platform for Business Users! https://www.smarten.com/blog/augmented-analytics-platforms-can-encourage-business-growth/ Thu, 02 May 2019 08:20:55 +0000 https://www.smarten.com/blog/?p=6251 Continued]]> Augmented Analytics Platforms Can Encourage Business Growth!

Advanced Analytics for Business Users is Key to Business Success!

Most businesses are choosing advanced analytics for business users and there is a good reason for that! Even the largest businesses rarely have an unlimited budget for data scientists and the trend toward data democratization has revealed the value in enabling business users with a data and analytics platform that is easy and mobile and can provide clear, concise results for planning, issue resolution and management of results at every level within the organization.

An Advanced Analytics Platform should include self-serve data preparation, smart data visualization and assisted predictive modeling with natural language processing and machine learning that will support users with simple search analytics.

An Augmented Analytics Vendor provides tools that make it easy for the average business user to develop reports, analyze data and share that data with confidence. Predictive Analytics for Business Users allows team members to forecast and predict and to create and test hypotheses before introducing changes into the market or the organization.

Contact Us to find out how an Augmented Analytics Platform can provide the foundation for your business growth and for confident business decisions.

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Business Analytics and NLP Search for Business Users! https://www.smarten.com/blog/application-of-nlp-and-advanced-analytics-for-every-business/ Fri, 26 Apr 2019 10:51:34 +0000 https://www.smarten.com/blog/?p=6247 Continued]]> Application of NLP and Advanced Analytics for Every Business!

Natural Language Processing Makes Advanced Analytics Accessible to Every User!

Whether you know it or not, you are familiar with Natural Language Processing. Applications of NLP include search techniques used in Google and other popular tools. The use of natural language processing in self-serve advanced analytics solutions can mean the difference between a simple, yet sophisticated, business user tool and a tool that is only suitable for a data scientist or professional analyst.

When a user leverages Natural Language Processing applications, the search itself can be entered using normal language and the results will be returned using natural language as well. Search Analytics using an NLP search are far easier to use and ensure that the business user gets the information they need easily and quickly. In a business environment where every organization must democratize data and encourage data literacy to improve competitive advantage, the Business Analytics techniques and business analytics tools that are the simplest and yet provide the most sophisticated underpinning will be the tools and techniques that are dependably adopted and used by every team member.

With the right tools, your business users can leverage natural language processing algorithms to achieve their goals and increase the agility and flexibility of the organization and its competitive response.

If you would like to find out how Natural Language Processing can help your users to leverage advanced analytics tools, Contact Us today.

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What is Augmented Analytics and Can it Add Value to Business Intelligence? https://www.smarten.com/blog/what-is-augmented-analytics-and-can-it-add-value-to-business-intelligence/ Wed, 24 Apr 2019 10:43:57 +0000 https://www.smarten.com/blog/?p=6243 Continued]]> What is Augmented Analytics and Can it Add Value to Business Intelligence?

The Merriam-Webster dictionary defines the word ‘augment’ this way: ‘to make greater, more numerous, larger or more intense’. If you are wondering how this applies to the term ‘augmented analytics’, you are not alone. Let’s take a closer look at Augmented Analytics and talk about why it has gotten so much attention in the business intelligence world.

What is Augmented Analytics?

Think of it this way: Augmented Analytics automates data insight using machine learning and natural language generation. It automates data preparation and allows for easy data sharing. By leveraging sophisticated analytics techniques and algorithms in an automated environment, these solutions allow for advanced use, manipulation and presentation of data and simplify the analytical process for the average user, so that users are presented with clear results. As platforms and solutions have matured, augmented analytics has evolved into a self-serve environment, so that business users can leverage these sophisticated tools to get answers to questions, identify problems and opportunities and share data with other users.

The augmented analytics approach provides tools for better clarity and insight into and, as this approach evolves, it continues to change and disrupt the more traditional forms of analysis.

Augmented Analytics allows organizations to integrate data from numerous data sources and to use that data to analyze and display results in a clear manner so the business can make unbiased decisions and establish objective metrics. Users can compare results against plans and forecasts. By exploring and presenting data and using Data Science Modeling Techniques and auto-suggested data displays, the organization can identify key data points and trends. Users can leverage crucial business insight to build and sustain a competitive advantage, make decisions about market and product entries and assess team and project results, financials, HR and resources, partners and suppliers, industry and government compliance, data security and other critical business factors. Augmented Analytics Tools provides clear results in context so that users can drill down to find the root cause of a problem. These techniques allow users to identify a logical direction, analyze historical results and plan for the future. This approach supports user/data interaction by removing restrictive boundaries and enabling flexibility for analysis and reporting.

How Can Augmented Analytics Add Value to Business Intelligence?

There are numerous Benefits to Augmented Analytics approach to business intelligence and, with the right self-serve analytics and data discovery tool, the average enterprise can expand these benefits and add even more value. Here are just a few of the ways in which self-serve augmented analytics can add value to business intelligence.

  • A self-serve augmented analytics solution allows analysts, data scientists and internal IT staff to focus on critical projects and to provide support for strategic issues, freeing them from focusing on the day-to-day analytical needs of the business community.
  • Augmented analytics empowers business users and provides critical information that will allow them to focus on goals, provide objective metrics and enable data sharing to advance the interests of the business.
  • Augmented Analytics provides complex, sophisticated techniques and tools in an easy-to-use interface to bring together data from disparate data sources and allow for confident, accurate decisions.
  • Augmented analytics provides immediate, objective results and improves ROI and TCO.
  • This approach to analytics ensures accurate business forecasting and predictions and provides metrics to ensure that appropriate decisions are made and that the business takes appropriate action regarding products and services, pricing, competition and other crucial business factors.
  • Simplified augmented analytics tools improve user adoption, data sharing, the advancement of data popularity, the integration of social BI within the organization and data and metrics literacy.

A July 27, 2017 Gartner Report, entitled, ‘Augmented Analytics Is the Future of Data and Analytics’ (ID G00326012) predicts that, ‘by 2020, organizations that offer users access to curated catalog of internal and external data will derive twice as much business value from analytics investments as those that do not.’

This report provides insight into the future of the analytics market and the foundation of next-generation analytics as a crucial foundation for business success.

The world of data analytics is no longer restricted to IT, data scientists and analysts. If an organization is going to be productive and successful today it must allow its business users to access easy-to-use tools with sophisticated features and functionality so that the entire team can work from the same roadmap and stay on track.

Original Post: What is Augmented Analytics and Can it Add Value to Business Intelligence?

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Data Analytics Software is Easy Enough for Business Users! https://www.smarten.com/blog/self-serve-data-analytics-can-work-for-you/ Thu, 18 Apr 2019 13:29:19 +0000 https://www.smarten.com/blog/?p=6236 Continued]]> Self-Serve Data Analytics Can Work for You!

Citizen Data Scientists are Not Born, They are Created!

Data analytics software used to be reserved for data scientists, analysts and IT staff but not today! Talk to any business colleague or pick up any technology analyst article and you will find plenty of discussion about the current use of data analytics tools and impressive predictions about the growth of this market. There is a reason for that popularity and growth! Businesses have discovered the value of business analytics and the benefit of taking the guesswork out of planning, problem solving and decision-making.

Business is moving too fast today to make the wrong decision and then have to try to catch up to the market or to miss a problem that may harm your reputation or an opportunity that may help you grow faster and stronger in your market of choice. Data Analytics is not just for data scientists! Today’s tools are designed with guidance and auto-recommendations to help business users choose the right type of data visualization, to help users leverage self-serve data preparation tools and to allow users to choose the right algorithm or analytical technique for predicting and forecasting results and choosing the right direction.

If you want to make the most of the knowledge your team possesses and transform business users into Citizen Data Scientists, you should select a self-serve Business Analytics Tools that will help you implement these tools quickly with minimal training requirements and swift, positive results.

If you would like to find out how you can successfully adopt Data Analytics Tools within your enterprise, Contact Us today.

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NLP and Clickless Analytics: The Perfect Pairing! https://www.smarten.com/blog/2019/04/10/april-10-2019/nautral-language-processing-clickless-analytics/ Wed, 10 Apr 2019 06:44:07 +0000 https://www.smarten.com/blog/?p=6232 Continued]]> Clickless Analytics with Natural Language Processing (NLP)!

Search Analytics with Natural Language Processing Makes Advanced Analytics Easy!

Whether you are a consumer or a business user, today’s technology users are savvy and they are used to having easy-to-use tools and features that make them more productive and allow them to quickly complete tasks. The same holds true for users of advanced analytics users. These users are also consumers when they are off the job and they are used to simple search technology like Google and other search methodologies that allow them to think, search and get results in a way that is fitting for normal speech and language.

This expectation has driven the integration of Natural Language Processing (NLP) as part of Advanced Analytics products so business users with average skills can leverage Search Analytics that employ machine learning, NLP and ‘clickless analytics’ to quickly perform searches and get results that will help them make business decisions and help them to better understand their data.

If you are a sales manager, you might use this NLP feature to ask your Augmented Analytics ‘which Southwestern region sales person sold the most units of tea products in September of 2018?’.

With natural language-processing-based search capability, users can avoid menus and scrolling and get right to the heart of the question (and answer). Results will be returned in natural language and displayed in an appropriate format with appropriate visualization to illustrate numbers, content or tables.

The concept of Clickless Analytics provides seamless augmented analytics, in a self-serve environment that is easy enough for every business user resulting in increased user adoption, improved data democratization, and return on investment (ROI).

If your users need a simpler, more intuitive way to perform search analytics, you can find the solution here: NLP and Clickless Analytics

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Smarten Advanced Analytics – Getting Started https://www.smarten.com/blog/getting-started-advanced-analytics/ Fri, 05 Apr 2019 11:39:35 +0000 https://www.smarten.com/blog/?p=6240 Continued]]>

The video describes the key features of Smarten and guides new users to start using the platform. It gets you started by connecting the data, using assisted predictive analytics, smart visualization, and analytics. A dataset with sales data and macroeconomic data is built in this session and predictive analytics applied to these. Sample sales data and dashboards and which is installed on your computer with the evaluation version is also explored to demonstrate the ease of building KPI and dashboards. This same data is used for smart visualization, where Smarten detects the best graph for the set of data. By the end of the video, you will be equipped run predictive analytics, prepare dashboard and visualize data in Smarten.

Download and Evaluate Smarten Augmented Analytics!

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What Are the Necessary Components of an Advanced Analytics Solution? https://www.smarten.com/blog/what-are-the-necessary-components-of-an-advanced-analytics-solution/ Tue, 02 Apr 2019 10:00:15 +0000 https://www.smarten.com/blog/?p=6227 Continued]]> What Are the Necessary Components of an Advanced Analytics Solution?

Business markets and competition are moving much more quickly these days and predicting, planning and forecasting is more important than ever. It is also important to ensure that every team member is a real asset to the organization and can contribute their knowledge and skill with full Insight into the effects and outcome of activities and processes and the ability to correct the course and make recommendations using clear, concise information. Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals.

But, when an organization is ready to consider the implementation of an Advanced Analytics solution, it is difficult to know what it needs to ensure that it can satisfy current and future requirements and ensure user adoption. The current course of advanced analytics is taking businesses into the area of augmented analytics – tools that allow business users to leverage sophisticated algorithms and analytical techniques without the help of data scientists, analysts or IT staff.

If a business wants to assure that it has full coverage for its Advanced Analytics needs and can leverage all the benefits of advanced analytics, it should consider a solution with the following capabilities:

  • Assisted Predictive Modeling provides predictive analytics capability assisted by auto-recommendations and auto-suggestions so users can apply predictive analytics to any use case using forecasting, regression, classification, clustering and other algorithms to analyze an infinite number of use cases and address customer targets, cross-sales opportunities, pricing, risk assessment and promotional targets and buying behavior.
  • Smart Data Visualization allows users to view and analyze data to identify a problem and clarify a root cause and to interact easily with data discovery tools and analytics software to build a view that will tell a story using guided visualization and recommended data presentation so there is no need for assistance or delays. Guided recommendations are made based on data type, volume, dimensions, patterns and nature of data.
  • Self-Serve Data Preparation allows users with average skills to perform data prep and transform, shape, reduce, combine, explore, clean sample and aggregate data without advanced skills In other words business users can perform data extraction, transformation and loading (ETL) without help – ETL for business users!
  • Advanced analytics with Natural Language Processing (NLP) gives users a familiar Google-type interface to compose and enter a question using common human language, so they don’t need to scroll through menus and navigation. Clickless search analytics allow users to enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

The augmented analytics advantages far outweigh the considerations for time and cost of implementation and the right advanced analytics tool will provide timely, cost-effective implementation and data integration to get the organization up and running quickly and efficiently.

Original Post: What Are the Necessary Components of an Advanced Analytics Solution?

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Assisted Predictive Modeling for All Users! https://www.smarten.com/blog/predictive-modeling-for-every-business-user/ Mon, 01 Apr 2019 06:51:34 +0000 https://www.smarten.com/blog/?p=6223 Continued]]> Predictive Modeling for Every Business User!

Predictive Analytics Techniques That Are Easy Enough for Business Users!

There are a myriad of predictive analytics techniques and predictive modeling algorithms and you can’t expect your business users to understand and use them. If you are taking on an advanced analytics initiative and you want to leverage data democratization to cascade data analytics and data literacy throughout the organization, you will need a data modeling solution that takes the guesswork out of the process.

Business users do not need to know how Predictive Analytics works to achieve their goals. What they need are simple, sophisticated tools that help them analyze data, create reports and share data with other users without knowing the data science behind the process. Business users need Assisted Predictive Modeling that can make suggestions on which algorithms and techniques to use for a certain type of data.

These self-serve Predictive Analytics Tools allow the organization to apply predictive analytics to any use case using forecasting, regression, clustering and other methods to analyze an infinite number of use cases including customer churn, and planning for and target customers for acquisition, identify cross-sales opportunities, optimize pricing and promotional targets and analyze and predict customer preferences and buying behaviors. The enterprise can provide the tools needed at every level of the organization with tools and data science for business users that are sophisticated in functionality and easy-to-use for users at every skill level.

Contact Us to find out how Predictive Analytics Software can help your business and your business users to plan, predict and forecast results.

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Advanced Analytics Tools and Visual Analytics! https://www.smarten.com/blog/visual-analytics-is-important-to-advanced-analytics/ Tue, 26 Mar 2019 10:31:15 +0000 https://www.smarten.com/blog/?p=6219 Continued]]> Visual Analytics is Important to Advanced Analytics!

The Importance of Data Visualization Cannot be Overstated!

Data visualization may not seem important, but the way you see data can provide additional insight or it can muddle the picture to the point where you will miss critical issues or opportunities. The importance of data visualization is even more evident when that data is being analyzed by business users who are not likely to see data in an analytical way and probably do not have the knowledge or skill required to change visualization techniques to accommodate a particular type of data.

But, with the right tools, Visual Analytics can tell a tale in a way that no other technique can. If you expect your business users to leverage advanced analytics tools you can provide data visualization examples or you can implement an augmented analytics solution that offers Smart Data Visualization. The best interactive data visualization provides auto-recommendations and suggestions so that users can select the best method for visualizing the data.

Data Visualization Tools enable users to identify relationships, patterns, trends and opportunities and to explore detailed data with simple drill down and drill through capabilities and make sense of data from all sources, with a guided approach that allows users to identify patterns and trends, and quickly complete analysis with clear results.

Contact Us today to find out how Smart Data Visualization Tools can help you and your business users to make better decisions and achieve the best results.

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Proven, Rapid ROI Assures Project Funding for Augmented Analytics Projects https://www.smarten.com/blog/proven-rapid-roi-assures-project-funding-for-augmented-analytics-projects/ Tue, 19 Mar 2019 10:59:46 +0000 https://www.smarten.com/blog/?p=6214 Continued]]> Proven, Rapid ROI Assures Project Funding for Augmented Analytics Projects

As business organizations fight for competitive advantage, funding for projects and large expenditures can fall by the wayside. In today’s competitive business market, every senior executive looks at risk, value and calculations like return on investment (ROI) and total cost of ownership (TCO) before approving a budget.

When it comes to Advanced Analytics, businesses large and small may hesitate to invest the time and effort in choosing and implementing a solution. Although Data Democratization and Data Literacy are topics of earnest discussion, the average enterprise may feel that the expense and time required will not provide enough impact to the bottom line. Nothing could be further from the truth!

The Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics, published on October 31, 2018, includes the following strategic assumptions:

By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI as well as data science and machine learning platforms, and of embedded analytics.

When a business chooses a self-serve advanced analytics solution, the benefits go beyond cost-effective, collaborative tools. With the right solution, business users can leverage features like Self-Serve Data Preparation, Smart Data Visualization and Assisted Predictive Modeling to produce reports, share data and make decisions using data integrated from multiple sources in an environment that allows for auto-suggestions and recommendations.

When an advanced analytics project is brought before a budget or funding committee, it is far easier to gain approval when that project is one that will provide numerous benefits across the enterprise, e.g., business users can leverage core knowledge and skill to produce and share meaningful reports and data, and data scientists and analysts can optimize time for more strategic projects that require 100% accuracy. These are just two of the benefits provided by an augmented analytics environment.

These auto-suggestion and guided recommendation tools ensure that users are not left on their own to decide how to prepare data or what techniques to use to forecast and predict. Smart visualization allows users to see data in the right way for the type of data they are analyzing so the conclusions they draw are clear and concise.

These tools encourage adoption and create power users and an environment of creativity where users can share and learn from each other and from the data and results produced. The organization will gain valuable insight into the types of reporting and data that are helpful in making decisions. The enterprise can move from error-prone spreadsheet analysis and guesswork to uniform, flexible tools that allow for comprehensive analysis.

Augmented Analytics allows for sophisticated analytics, using algorithms and analytical techniques in an easy-to-use environment that is not intimidating and does not require advanced analytical skills so every business user will add more value and become a real asset to the organization. By combining knowledge and skill in their area of expertise with simple analytical tools, each business user can contribute to the bottom line.

Because these augmented analytics tools pay such rapid and certain dividends, funding for projects is easier to secure. By implementing these advanced analytical tools, the enterprise will achieve rapid ROI and low TCO and the organization can improve data literacy and transition business users into Citizen Data Scientists.

Original Post: Proven, Rapid ROI Assures Project Funding for Augmented Analytics Projects

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Visual Analytics Software and Predictive Analytics Made Easy! https://www.smarten.com/blog/predictive-analytics-and-visual-analytics-for-business-users/ Mon, 18 Mar 2019 08:04:51 +0000 https://www.smarten.com/blog/?p=6210 Continued]]> Predictive Analytics for Business Users!

Data Science and Predictive Analytics Made Simple!

Imagine a world where data science and predictive analytics tools are created for business users! That world exists today with the evolution of sophisticated, yet easy-to-use tools that include predictive analytics for business users, visual analytics software and tools, and self-serve data preparation.

These tools are supported by Natural Language Processing (NLP) and simple Search Analytics supplemented by machine learning, so business users can take advantage of complex Predictive Modeling Algorithms, smart data visualization and data prep that includes data from disparate sources, all without the skills of a professional data scientist or IT staff member.

Visual Analytics and Predictive Analytics provide a full suite of advanced analytical tools to support business users with the foundation of sophisticated and complex analytical techniques in an easy-to-use, worry free environment so users can gather, analyze and share data to forecast, plan, report and problem solve.

Contact Us if you want an Advanced Analytics Solution that will support business users and enhance business results.

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Augmented Analytics Makes Advanced Analytics Simple! https://www.smarten.com/blog/data-preparation-tools-and-advanced-analytics-for-all/ Wed, 13 Mar 2019 10:46:07 +0000 https://www.smarten.com/blog/?p=6206 Continued]]> Data Preparation Tools and Advanced Analytics for All!

Augmented Analytics Tools and Data Prep Tools Make Life Easy for Business Users!

In today’s world, advanced analytics is for everyone. There is no reason to limit access to data preparation software or advanced predictive analytics to data scientists, analysts and IT staff. The evolution of augmented analytics and intelligence augmentation has created some impressive tools and solutions that allow business users with average skills to use self-serve data prep and complete data preparation using auto-recommendations and suggestions to work through the process easily and with no confusion.

Augmented Analytics Tools include Smart Data Visualization with recommendations on how best to visualize data based on data type, etc., as well as Assisted Predictive Modeling with recommendations on which techniques and algorithms to use to get the best outcome for the data a user is trying to analyze.

Data Scientists who are used to working in R scripting, can perform data preparation in R, as well, so your analysts and data scientists will never be left out! IT and data scientists can also leverage these tools to supplement other techniques and tools. Data Analysis, data blending, and data preparation tools for all!

If you would like to know more about these Advanced Analytics and Data Preparation Tools, Contact Us today.

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Citizen Data Scientists and Data Literacy! https://www.smarten.com/blog/2019/03/07/march-07-2019/data-literacy-citizen-data-scientists/ Thu, 07 Mar 2019 12:32:01 +0000 https://www.smarten.com/blog/?p=6201 Continued]]> Data Literacy Improves Resource Optimization!

Encourage Data Literacy and Create Citizen Data Scientists!

Data literacy is a very popular idea these days. As business users adopt and embrace data and advanced analytics, features like predictive analytics for business users make it easier for a user with average skills to leverage data to make decisions and share information and, in so doing, to become more literate about data analytics.

The goal of many businesses is to transform their business users into Citizen Data Scientists who will be an even greater asset to the organization by optimizing professional knowledge and training and using data in a way that is meaningful to them in their role. Power users will help other users understand the data better and raise the general level of data literacy across the organization.

The Advantages of Augmented Analytics are too numerous to mention and the addition of Natural Language Processing (NLP) to enable users to ask questions and get information using natural language has really helped businesses to grow and to plan and forecast more efficiently and accurately Users can enjoy the benefits of simple search analytics and without advanced training and utilize auto-recommendations and suggestions to choose appropriate visualization techniques and select the right algorithms and analytical techniques to get the results they need and to produce clear, concise reports.

The evolution of these Advanced Analytics Tools has made it easier than ever to achieve and sustain a competitive advantage and to build data literacy within the organization with quick implementation and easy-to-use tools that will help you to optimize your resources and knowledge across the business.

If you want to improve the efficiency of your organization and leverage the skills and knowledge of every user by increasing data literacy and data democratization, start here: Benefits of Augmented Analytics

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Is Advanced Analytics Right for Business Users? https://www.smarten.com/blog/can-business-users-easily-adopt-data-discovery-solution/ Mon, 04 Mar 2019 12:03:11 +0000 https://www.smarten.com/blog/?p=6197 Continued]]> Can Business Users Easily Adopt a Data Discovery Solution?

Are Data Discovery Tools Suitable for Business User Skills?

If you want your business users to function and perform at their highest level and you want to optimize all resources, your data discovery solution must provide the guidance and ease-of-use that a business user requires to analyze and share data and make confident decisions that will improve the business bottom line.

A great data discovery solution allows business users to quickly and easily prepare and analyze data and to visualize and explore data, notate and highlight data and share data with others. Business users can use advanced predictive analytics to identify patterns and trends and better predict results. The right Smart Data Discovery tool is designed for business users with average skills can do all of this without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

An Augmented Analytics supports the business user with tools that automates data insight by utilizing machine learning and natural language to automate data preparation and enable data sharing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately.

Advanced Analytics uses sophisticated techniques and algorithms in an automated environment to simplify the analytical process for the average business user, so users are presented with clear results to use in making decisions and analyzing problems. Users can perform data profiling, discover data lineage, perform data exploration using easy-to-use data exploration tools and easily perform data discovery and classification all without the help of a programmer.

If you would like to find out how Advanced Analytics and Data Discovery solution can help your business, Contact Us today to get started.

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What is Augmented Data Science and Why is it Important to My Business? https://www.smarten.com/blog/what-is-augmented-data-science-and-why-is-it-important-to-my-business/ Thu, 28 Feb 2019 08:30:35 +0000 https://www.smarten.com/blog/?p=6193 Continued]]> What is Augmented Data Science and Why is it Important to My Business?

If Data Science was once the sole domain of analysts and data scientists, Augmented Data Science represents the democratized view of this domain. With Augmented Data Science, the average business user can engage with advanced analytics tools that allow for automated machine learning (AutoML) and leverage sophisticated analytical techniques and algorithms in a guided environment that uses auto-recommendations and suggestions to lead users through the complex world of data science with ease and intuitive tools.

Augmented Data Science is integrated into the average enterprise, the domain of data science proliferates as does the knowledge and understanding of analytical techniques. Citizen X roles, like the much-discussed Citizen Data Scientist, Analytics Translator, Data Translator, Citizen Integrators and Citizen Developers will emerge, cascading knowledge and leveraging power users as liaisons with IT and data science staff. The propagation of these tools throughout the enterprise will improve decisions, planning, and competitive advantage.

Augmented Analytics includes Assisted Predictive Modeling, Smart Data Visualization, Self-Serve Data Preparation, Clickless Analytics, NLP Search Analytics, Automated Machine Learning (AutoML), which enables faster, or accurate analysis across the organization, optimizes resources and improves the value of each team member. Business users can merge core business knowledge and skill with critical analysis and share, collaborate and advance ideas, innovations and issue resolution.

The popularity and availability of intuitive, guided, Advanced Analytics allows for new analytic insights. As business users adopt and become comfortable with advanced data discovery and advanced analytics tools, that no longer require the skills of IT or a data scientist. This democratization of data analysis tools frees the data scientist and IT teams to focus on core tasks and on those strategic analytical projects that require 100% accuracy and refinement. Business users can perform analysis and use this analysis on a daily basis without delay, thus increasing the return on investment, and the accuracy of decisions and supporting data.

In a report published by Gartner on October 31, 2018, and entitled ‘Augmented Analytics Is the Future of Data and Analytics’, Gartner analysts provide the following strategic assumption: ‘By 2020, automation of data science tasks will enable citizen data scientists to produce a higher volume of advanced analysis than specialized data scientists’.

Augmented data science automates and simplifies analysis with machine learning so implementation, training and adoption of these tools is rapid and successful. Users do not need the skills or knowledge of a data scientist. Instead, they can leverage the easy-to-use augmented analytics tools that provide guidance, suggestions and auto-recommendations to ensure that the data retrieved, analyzed and presented is the right data in the right format to produce clear, concise results.

To put it simply, augmented data science lends a helping hand to users and to the enterprise that wishes to streamline the analytical process and provide every team member with access to the sophisticated tools they need to produce results without delays, complication or advanced skills.

Original Post: What is Augmented Data Science and Why is it Important to My Business?

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White Paper – Organizational Readiness Is Crucial to the Success of an Advanced Analytics Initiative https://www.smarten.com/blog/white-paper-organizational-readiness-is-crucial-to-the-success-of-an-advanced-analytics-initiative/ Thu, 21 Feb 2019 09:19:31 +0000 https://www.smarten.com/blog/?p=6188 Continued]]> White Paper - Organizational Readiness Is Crucial to the Success of an Advanced Analytics Initiative

There are many variables to the success of an advanced analytics solution but the primary pitfalls for this type of project can be traced back to poor planning. If the organization is not ready for self-serve advanced analytics, the initiative will fail. Organizational readiness is, in fact, one of the most overlooked issues in any type of software, system or technology introduction. If the enterprise wishes to succeed and achieve its goals for self-serve advanced analytics it must have a clear vision of what it wants to accomplish. It needs to have a comprehensive understanding of its requirements (technical, infrastructure, processes, tasks, locations, team members, etc.) The enterprise also needs to assess its need for external vs. internal assistance in implementation and training.

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Data Visualization Tools Make Sense of Confusing Data! https://www.smarten.com/blog/smart-data-visualization-is-important-to-advanced-analytics/ Tue, 19 Feb 2019 14:17:37 +0000 https://www.smarten.com/blog/?p=6184 Continued]]> Smart Data Visualization is Important to Advanced Analytics!

What is Smart Data Visualization?

Smart data visualization is a crucial part of advanced analytics. Smart visualization allows business users to view and analyze data to identify a problem, clarify a root cause, identify a business opportunity, and to make confident decisions. Business users can interact easily with visual analytics tools to build a view that will tell a compelling story using guided visualization tools so there is no need for assistance from data scientists or delay in finding, analyzing and understanding the data. Guided data visualization recommendations are provided to the user, based on the data type, volume, dimensions, patterns and nature of data.

With Smart Data Visualization, business users can find those elusive pieces of information that will have the most impact on business results.

Data Visualization Tools combine cutting-edge technology and machine learning on the backend, with an intuitive user experience on the front end, business users can easily leverage sophisticated tools with suggestions and recommendations that allow users to personalize data displays and create meaningful views and collaboration.

Machine Learning Algorithms provides guidance to determine the data visualization types and techniques that will be the best fit for the data business users want to analyze. It allows for better understanding of data, and identifies unusual patterns in data, and achieves the best output and results.

If you would like to find out more about Smart Data Visualization and how it can support your business and users, Contact Us today to get started.

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Advanced Analytics to Increase Productivity! https://www.smarten.com/blog/data-analytics-that-will-improve-your-bottom-line/ Thu, 14 Feb 2019 11:40:18 +0000 https://www.smarten.com/blog/?p=6179 Continued]]> Data Analytics That Will Improve Your Bottom Line!

Can Citizen Data Scientists Support Advanced Analytical Needs?

No business, large or small, has unlimited funds and resources. In a world where data analytics is more important than ever to the business bottom line and competitive position, the typical business cannot afford to hire dozens of data scientists but it absolutely must have access to detailed, clear data analysis that will drive the bottom line and ensure success.

The domain of Business Analytics does not have to be limited to data scientists. With the right augmented data discovery tools that are suitable for business users but sophisticated enough to produce professional-level results, the organization can get what it needs without hiring teams of analysts, data scientists and IT professionals.

Self-Serve Data Analytics Software includes dynamic, cutting-edge business intelligence and data discovery tools with self-serve data preparation, smart visualization, assisted predictive modeling, and search analytics supported by Natural Language Processing. Solid foundation in which business users can become Citizen Data Scientists.

By applying core business skills to analytical tasks with guidance, recommendations and auto-suggestions from the Data Analytics Tools, business users can function at a higher level and increase their value to the organization, making decisions that support goals and objectives and sharing data and information with other business users to collaborate, increase productivity and swiftly and efficiently complete tasks.

Contact Us if you want an Advanced Analytics that will transform your business users to Citizen Data Scientists.

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Are Analytics Translators & Citizen Data Scientists Critical? https://www.smarten.com/blog/2019/02/11/february-11-2019/analytics-translator-citizen-data-scientist/ Mon, 11 Feb 2019 11:58:33 +0000 https://www.smarten.com/blog/?p=6175 Continued]]> Succeed with Citizen Data Scientists & Analytics Translators!

Transform Your Culture with Analytics Translators and Citizen Data Scientists!

As business becomes more competitive, as markets get tighter, there is a need to leverage and optimize your resources to the greatest extent possible. Using the knowledge and skill of each individual on your team and allowing these skills to inform business decisions and business analysis is key. As businesses democratize data and look for ways to improve collaboration and ensure that information makes it to everyone who needs it, a new business role has emerged.

This role is known as an ‘Analytics Translator’. By combining domain, organizational and industry skills with self-serve analytical tools, the Analytics Translator can help the enterprise to achieve low total cost of ownership (TCO) and rapid return on investment (ROI) for its Advanced Analytics initiatives and can encourage and nurture data democratization and optimal analytical business results within the organization. Analytics Translators bridge the gap between IT, data scientists and business users, and move initiatives forward by acting as a liaison and topic expert to help the organization focus on the right things to achieve its goals.

Gartner defines a Citizen Data Scientist as ‘a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.’ A Citizen Data Scientist is different from a true Data Scientist in one crucial way; namely, they do not have the skills or training to be an analyst or a programmer but, with the right tools, they are capable of generating reports, analyzing data and sharing data to make decisions. Citizen Data Scientists play a crucial role in day-to-day analysis and decision-making, using augmented analytics tools.

The importance of the Analytics Translator and the Citizen Data Scientist is undeniable to the average enterprise. Acting as liaisons between the business user population and IT, and enhancing and leveraging analytical skills combined with crucial core business knowledge, these roles and individuals can connect, translate and analyze issues, problems and opportunities and ensure that critical information is not lost or misunderstood, while minimizing the frustration and delay within the organization. By establishing and optimizing these resources, the organization can make the most out of its data and its team members.

Explore the possibilities and transform your culture to take advantage of Augmented Analytics with Analytics Translators and Citizen Data Scientists.

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Can My Business Achieve Optimal Analytics Without Hiring Dozens of Data Scientists? https://www.smarten.com/blog/can-my-business-achieve-optimal-analytics-without-hiring-dozens-data-scientists/ Thu, 07 Feb 2019 10:26:13 +0000 https://www.smarten.com/blog/?p=6171 Continued]]> Can My Business Achieve Optimal Analytics Without Hiring Dozens of Data Scientists?

As the need for advanced analytics increases in organizations, enterprises large and small struggle to find and sustain the professional resources they need to meet their requirements for data, analysis and strategic direction.

In some businesses, Data Scientists, professional analysts and IT staff are often buried under requests for analysis and data and, as a result, these teams are unable to focus on strategic issues and on crucial questions that require 100% accuracy to drive the direction of the business.

For other businesses, the issue is more pressing. When a company cannot afford to hire data scientists or analysts because of budgets and financial constraints, the problem is clear. There is a need for Advanced Analytics but there is no funding to hire the required resources.

The Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics, published on October 31, 2018, includes the following strategic assumptions:

  • By 2025, a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organizations.
  • By 2020, automation of data science tasks will enable citizen data scientists to produce a higher volume of advanced analysis than specialized data scientists.

In this rapidly changing, increasingly competitive business landscape, the wise enterprise will focus, not on adding dozens of data scientists, but on capitalizing on the time and skills of those data scientists and analysts by giving business users the ability to tools they need to make the day-to-day decisions and to produce data-driven analytics that are accurate and timely without the assistance of a data scientist.

As businesses incorporate self-serve advanced analytics into their technology landscape and business users adopt these tools and begin to share and learn from Data Analysis, the business can transition to a more balanced environment that allows data scientists and analysts the time and focus to perform critical activities.

Augmented Analytics, Assisted Predictive Modeling, Smart Data Visualization, Self-Serve Data Preparation and Search Analytics are designed to help the average business user with auto-suggestions and recommendations on how to prepare and view data to achieve the best outcomes and make analysis easy and clear.

When business users can transition to Citizen Data Scientists, the organization will enjoy numerous benefits, including:

Business Users:

  • Support for day-to-day business decisions
  • Insight, perspective and analysis
  • Quick hypothesis and prototyping
  • Improved agility for business development
  • Timely and accurate decision-making
  • Emergence of power users and data popularity
  • Transformation to citizen data scientists

Data Scientists:

  • Reduction in day-to-day requests
  • Ability to focus on strategic projects
  • Focus on projects that require 100% accuracy
  • Ability to achieve mature modeling goals

Original Post: Can My Business Achieve Optimal Analytics Without Hiring Dozens of Data Scientists?

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Clickless Analytics: Search Analytics That is Easy and Fast! https://www.smarten.com/blog/advanced-analytics-with-natural-language-processing-nlp/ Tue, 05 Feb 2019 09:59:51 +0000 https://www.smarten.com/blog/?p=6166 Continued]]> Advanced Analytics with Natural Language Processing (NLP)!

Natural Language Processing (NLP) and Clickless Analytics: Advanced Analytics for All!

Natural Language Processing (NLP) takes the shackles off business users and provides advanced search analytics that is as easy as using a search engine. When advanced analytics incorporates natural language processing, business users can formulate questions and receive answers in a way that mirrors human language and writing. They do not need code or specific formats. They can just enter a question and receive an answer.

Users may want to leverage Advanced Analytics to find out which region sold the most units of a particular product last quarter, or they may want to find out how many units of a certain product are available in a certain warehouse, or which suppliers delivered products in the shortest time from order to delivery. Those are just some of the questions a business user might ask. Natural language search analytics can be used in advanced analytics to uncover valuable nuggets of information in the financial arena, in sales, marketing, HR, IT, manufacturing, purchasing, payroll and every other aspect of the organization.

Natural Language Processing search analytics allows business users to create complex searches without endless clicks and complex navigation and commands. Using this type of Search Analytics, users can access and view clear, concise answers and analysis quickly and easily. With natural language-processing-based search capability, users do not need to scroll through menus and navigation.

Clickless Analytics enhances this process further by providing simplified search analytics with seamless Augmented Analytics so users can work through searches and produce results with guidance, auto-recommendations and suggestions in an environment that uses machine learning and natural language processing (NLP) so the process is simple, swift and worry-free. Users work in a self-serve environment that is easy enough for every team member and will result in increased user adoption, improved data democratization, and return on investment (ROI).

If you would like to know more about Clickless Analytics, Contact Us today.

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What is Self-Service Data Preparation? https://www.smarten.com/blog/self-serve-data-prep-is-key-to-data-democratization/ Mon, 04 Feb 2019 11:03:15 +0000 https://www.smarten.com/blog/?p=6162 Continued]]> Self-Serve Data Prep is Key to Data Democratization!

Data Literacy and Self-Serve Data Prep Encourage Citizen Data Scientists!

In years past, data preparation was the domain of IT professionals and data scientists. In order to prepare data for analysis, one had to find, gather, and prepare the data and that preparation included cleaning, combining, reduction and shaping of the data.

For the average manager, executive or business user, this complex process meant that they could not receive analysis or reports in a timely manner and that results were often difficult to interpret.

Self-Serve Data Preparation is a process by which business users can view data integrated from disparate sources and easily prepare that data for analysis with ease and clarity. Self-serve data preparation is a crucial piece of the puzzle for business users who want and need to make sense of data.

Self Service Data Preparation allows business users with average skills to perform data preparation (AKA data extraction, transformation and loading or ET). These easy-to-use tools allow business users to transform, shape, reduce, combine, explore, clean, sample and aggregate data, without the need for SQL skills, ETL or other programming language – and all without the assistance of data scientists or IT staff.

Self-Serve Data ETL empowers every business user and allows them to prepare data for their analytics using tools that enable data extraction transformation and loading – ETL for business users! In other words, business users can quickly move data into the analytics system without waiting for IT. Machine learning capability provides guidance to determine the best techniques and the best fit transformations for the data business users want to analyze, allowing for better understanding of data.

If you would like to explore the possibilities provided by Self-Serve Data Prep, Contact Us today to get started.

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Is Advanced Analytics the Next Logical Step Beyond Self-Serve Business Intelligence? https://www.smarten.com/blog/advanced-analytics-next-logical-step-beyond-self-serve-business-intelligence/ Thu, 31 Jan 2019 11:35:17 +0000 https://www.smarten.com/blog/?p=6158 Continued]]> Is Advanced Analytics the Next Logical Step Beyond Self-Serve Business Intelligence?

Many organizations have grown comfortable with their business intelligence solution, and find it difficult to justify the need for advanced analytics. The advantages of advanced analytics are numerous and those advantages are based on the ability to further improve the business, increase user adoption (and therefore user empowerment and accountability) and, best of all, improve the bottom line and the accuracy of predictions and forecasts that will dictate the success of the business in the future.

How is Advanced Analytics Different from Business Intelligence?

Put simply, business Intelligence uses historical data to reveal where the business has been, and managers can use this data to predict competitive response and discover what is changing in customer buying behavior and in sales. Advanced analytics goes beyond history by leveraging predictive analytics to give businesses insight into the future, and allows for the testing of theories and hypotheses in a risk-free environment so businesses can plan, predict and forecast for things like product pricing changes, new locations, changes in customer buying behavior, competitive response, etc.

Today’s Advanced Analytics Tools allow business users to leverage features like self-serve data preparation, smart data visualization and assisted predictive modeling. These tools allow for auto-recommendations and suggestions to guide users through the choices and options that will allow for the best visualization and produce the best predictions. Business users with average skills can explore and share data and produce reports with better, clearer results (all without the skills or knowledge of an analyst or data scientist). These augmented analytics tools use sophisticated algorithms and analytical techniques, married with natural language processing (NLP) so users can ask questions using normal human language and get results in the same way. The addition of Clickless Search Analytics makes it easier to bring advanced analytics to the organization and engage business users with full confidence in user adoption.

A comprehensive, self-serve advanced analytics solution Incorporates computational linguistics, analytical algorithms and data mining into a self-serve environment and provides an easy-to-use NLP search capability for swift, accurate data analysis. It gives the organization insight into previously hidden data so it can explore and ‘discover’ crucial patterns, trends, issues and opportunities to improve productivity and improve decision making across the organization.

The Components of Self-Serve Advanced Analytics:

Advanced analytics takes the organization and its users beyond BI tools by providing comprehensive functionality with an underpinning of sophisticated algorithms and tools in an easy-to-use environment.

Assisted Predictive Modeling provides predictive analytics capability assisted by auto-recommendations and auto-suggestions so users can apply predictive analytics to any use case using forecasting, regression, classification, clustering and other algorithms to analyze an infinite number of use cases and address customer targets, cross-sales opportunities, pricing, risk assessment and promotional targets and buying behavior.

Smart Data Visualization allows users to view and analyze data to identify a problem and clarify a root cause and to interact easily with data discovery tools and analytics software to build a view that will tell a story using guided visualization and recommended data presentation so there is no need for assistance or delays. Guided recommendations are made based on data type, volume, dimensions, patterns and nature of data.

Self-Serve Data Preparation allows users with average skills to perform data prep and transform, shape, reduce, combine, explore, clean sample and aggregate data without advanced skills In other words business users can perform data extraction, transformation and loading (ETL) without help – ETL for business users!

Advanced Analytics with Natural Language Processing (NLP) gives users a familiar Google-type interface to compose and enter a question using common human language, so they don’t need to scroll through menus and navigation. Search Analytics allow users to enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

Business markets and competition are moving much more quickly these days and predicting, planning and forecasting is more important than ever. It is also important to ensure that every team member is a real asset to the organization and can contribute their knowledge and skill with full Insight into the effects and outcome of activities and processes and the ability to correct the course and make recommendations using clear, concise information. Advanced Analytics is the logical tool to help a business optimize its investments and achieve its goals.

Original Post: Is Advanced Analytics the Next Logical Step Beyond Self-Serve Business Intelligence?

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Augmented Analytics Learning for All Users! https://www.smarten.com/blog/enable-learning-for-citizen-data-scientists/ Wed, 30 Jan 2019 11:00:11 +0000 https://www.smarten.com/blog/?p=6154 Continued]]> Enable Learning for Citizen Data Scientists!

Can Augmented Analytics Tools Improve Business User Analytics Adoption?

When a business commits to data democratization and to improving data literacy, it must add advanced analytics tools that will support these initiatives. The education of business users is crucial if these projects are to be successful, but no business has the time or the money to schedule intensive training.

If a business wants to encourage adoption of advanced Data Analysis tools, it must provide Learning for Citizen Data Scientists, and nothing is better than learning by doing. With Augmented Analytics Learning, business users can enjoy the advantages of auto-recommendations and guidance to perform self-serve data preparation, smart data visualization and assisted predictive modeling.

While they are performing Advanced Analytics, they are learning and becoming power users. They can share data and create innovative views of data so that other business users will learn from them as well. Augmented Analytics Education does not have to take place in a classroom. It can take place during the analytical process. With the right tools, users will be willing to adopt the tools and, because they do not experience frustration, they will continue to learn as they work.

Techniques such as time series forecasting, decision tree analysis, linear regression, binary logistic regression, ARIMAX forecasting and ARIMA forecasting, and many other algorithms and analytical techniques are accessible in an easy-to-use environment, and users are offered recommendations to select the appropriate techniques, so no guesswork is necessary.

Contact Us if you want an advanced analytics solution that will encourage and support business users and sophisticated augmented analytics tools that will provide comprehensive planning, forecasting and analytical tools for use by every team member. Data Literacy

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Citizen Data Scientists Can Leverage Business Analytics! https://www.smarten.com/blog/improve-data-literacy-and-create-citizen-data-scientists/ Tue, 29 Jan 2019 06:38:02 +0000 https://www.smarten.com/blog/?p=6150 Continued]]> Improve Data Literacy and Create Citizen Data Scientists!

Citizen Data Scientists Improve Productivity and Innovation in the Enterprise!

A business that does not optimize its resources is doomed to fail. In this rapidly changing business environment and market, every organization must make the best of precious human resources. No one has enough funding to hire additional resources to get the job done and, when there are extra funds, those funds are quickly earmarked for new products, marketing and other crucial activities.

Just as markets benefit from disruption, the enterprise too can benefit from disruption. Implementing initiatives to democratize data and improve Data Literacy is a good way to disrupt the culture and transform business users into Citizen Data Scientists. When business analytics is brought to the team member level it can provide crucial support and an environment where team members can leverage their core skills and knowledge and combine that knowledge with Data Analytics to innovate, solve problems and support organizational goals.

Data Scientists play a critical role in some organizations and can continue to focus on strategic initiatives while business users are free to make confident day-to-day decisions, share data and perform Advanced Analytics, all without the assistance of IT professionals or analysts.

A Citizen Data Scientist can add significant value to the organization and, as business users adopt Business Analytics tools, and data is shared, power users will emerge and the enterprise will gain insight into the type of data and reporting that is crucial to the organization and can help to plan, forecast and identify opportunities.

If you would like to know more about how Advanced Analytics Tools can improve your enterprise planning and how your business users can graduate to Citizen Data Scientist roles, Contact Us today.

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What is Automated Machine Learning (AutoML)? https://www.smarten.com/blog/what-is-automated-machine-learning-automl/ Thu, 24 Jan 2019 10:37:46 +0000 https://www.smarten.com/blog/?p=6145 Continued]]> What is Automated Machine Learning (AutoML)?

What is Automated Machine Learning? Quite simply, it is the means by which your business can optimize resources, encourage collaboration and rapidly and dependably distribute data across the enterprise and use that data to predict, plan and achieve revenue goals.

With the right tools, today’s average business user can become a Citizen Data Scientist, using data integrated from various sources to learn, test theories and make decisions. AutoML comes into play as business users leverage systems and solutions that are designed with Machine Learning capabilities to predict outcomes and analyze data.

Take for example, the task of performing predictive analytics. Business users can leverage machine learning and assisted predictive modeling to achieve the best fit and ensure that they use the most appropriate algorithm for the data they wish to analyze. Business users can take advantage of AutoML tools to explore patterns in data and receive suggestions to help them gain insight – all without dependence on IT or data scientists.

This Predictive Modeling capability is combined with auto-recommendations and auto-suggestions to simplify use so that business users can work with sophisticated predictive algorithms in an intuitive, easy-to-use environment and apply advanced analytics to use case using forecasting, regression, clustering and other methods to assess customer churn, target customers for acquisition, identify cross-sales opportunities, optimize pricing and promotional targets and analyze and predict customer preferences and buying behaviors.

AutoML is, quite simply, the automated process of features and algorithm selection that supports planning, and allows users to fine tune, perform iterative modeling, and allows for the application and evolution of machine learning models.

In its report entitled, ‘Augmented Analytics Is the Future of Data and Analytics’, published on October 31, 2018, Gartner provided the following strategic planning assumption: ‘By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI as well as data science and machine learning platforms, and of embedded analytics.’

Machine learning takes the heavy lifting away from business users and allows them to leverage their core business knowledge and skills to engage in predictive analytics, while allowing the flexibility and sophistication of machine learning to offer the guided assistance of a ‘smart’ solution. The system interprets the dataset, selects important columns, analyzes categories, types and other parameters and uses intelligent machine learning to automatically apply the best algorithm and analytical technique and provide data insights.

Machine Learning Algorithms allows the system to understand data and applies correlation, classification, regression, or forecasting, or whichever technique is relevant, based upon the data the user wishes to analyze. Results are displayed using visualization types that provide the best fit for the data, and the interpretation is presented in simple natural language. This seamless, intuitive process enables business users to quickly and easily select and analyze data without guesswork or advanced skills.

Not so long ago, this type of Advanced Analytics would have demanded the services of a full-time, trained data scientist. Today, augmented data science and machine learning automates and democratizes key aspects of data science, predictive analytics and machine learning, and reduces the need for analysts and data science skills to generate, manage and collaborate using advanced analytic models. As AutoML and assisted predictive modeling evolves, business users, and the organizations they support, will benefit with increased productivity, improved knowledge management and more refined planning, predictions and outcomes.

Original Post: What is Automated Machine Learning (AutoML)?

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How Can Clickless Analysis Help Business Users? https://www.smarten.com/blog/clickless-analytics-nlp-search-analytics-for-business-users/ Wed, 23 Jan 2019 11:30:13 +0000 https://www.smarten.com/blog/?p=6141 Continued]]> Clickless Analytics & NLP Search Analytics for Business Users!

What is Clickless Analytics and How Can It Help My Business?

Natural Language Processing Search Analytics (NLP) is a crucial component of search analytics and smart data discovery in today’s online and technology environment. Consumers and business users are very familiar with the search techniques used by online giants like Google and they expect and demand that same ease-of-use in apps and software designed for business user. NLP search allows business users to create complex searches without endless clicks and complex navigation and commands. Using this type of search analytics, users can access and view clear, concise answers and analysis quickly and easily without the knowledge.

What is Search Analytics? With natural language-processing-based search capability, business users can perform research and analytics using data integrated from disparate data sources across the enterprise to get information, identify trends and patterns, forecast and plan and perform other analytics. They do not need to scroll through menus and navigation. They can simply enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

What is Clickless Analysis? Clickless analysis provides seamless search analytics and augmented analytics with advanced data discovery, using Machine Learning Modelling and Natural Language Processing (NLP), in a self-serve environment that is easy enough for every business user resulting in increased user adoption, improved data democratization, and return on investment (ROI). Clickless Analytics democratizes advanced analytics so business users can enjoy the benefits of smart data discovery using natural language searches. Simply enter the query in natural language and let the system do the rest. No advanced training is required!

If you would like to enjoy the ease and convenience of Search Analytics, Contact Us today to get started.

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Advanced Analytics Vendor with ETL for Business Users! https://www.smarten.com/blog/advanced-analytics-for-business-users/ Tue, 22 Jan 2019 12:02:09 +0000 https://www.smarten.com/blog/?p=6137 Continued]]> Advanced Analytics for Business Users!

A Data and Analytics Platform to Support Business Users and Data Scientists!

A cutting-edge advanced analytics vendor takes an innovative approach to the data and analytics platform by focusing on Technology Leadership, Team Environment and a Customer and Partner Focus. A modern analytics platform should include predictive analytics for business users with cutting-edge business intelligence and data discovery tools.

Augmented Analytics represents the evolution of self-serve data preparation, including ETL for business users, smart visualization, assisted predictive modeling, and Clickless Analytics powered by Natural Language Processing.

An augmented analytics vendor should also provide tools that are suitable for data scientists for those times when 100% accuracy and strategic initiatives are at stake. Advanced Analytics for business users creates a solid foundation of self-serve, auto-recommendation tools in which Business Users can become Citizen Data Scientists.

Contact Us if you want a modern analytics vendor that can help your organization discover the benefits of the Smarten approach to Advanced Analytics Tools.

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Citizen Data Scientists and Augmented Analytics! https://www.smarten.com/blog/data-literacy-for-business-users-with-augmented-analytics/ Mon, 21 Jan 2019 10:04:32 +0000 https://www.smarten.com/blog/?p=6133 Continued]]> Data Literacy for Business Users with Augmented Analytics!

Learning for Citizen Data Scientists and Data Literacy Across the Enterprise!

So you want to transform your business users and encourage learning for Citizen Data Scientists to enable data literacy across your enterprise? If your business is like most, your average business user doesn’t know (or need to know) the details of sophisticated algorithms and analytical techniques. But, you DO want to encourage data literacy and provide your business users with the tools they need to perform analytics.

Business users do not have the time or the inclination to get lost in complex algorithms or endure the frustration of trying to decide what technique to use to analyze a particular type of data. Consider the myriad of techniques used by data scientists and analysts!

  • Time Series Forecasting
  • Holt-Winters Forecasting
  • Exponential Smoothing
  • Descriptive Statistics
  • K-Means Clustering Algorithm
  • Multinomial Logistic Regression
  • ARIMA Forecasting
  • Frequent Pattern Mining Algorithm
  • Chi Square Test
  • Decision Tree Analysis
  • Outlier Analysis
  • Support Vector Machine Algorithm
  • SVM Algorithm
  • Hierarchical Clustering Algorithm
  • Karl Pearson Correlation
  • ARIMAX Forecasting
  • Linear Regression
  • Paired Sample T Test
  • Dependent Sample T-Test
  • Binary Logistic Regression
  • Spearman’s Rank Correlation
  • Simple Random Sampling
  • Stratified Random Sampling
  • Independent Samples T Test
  • Multiple Linear Regression
  • K-Nearest Neighbors Algorithm
  • KNN Classification
  • Naive Bayes Classifier

These are just some of the possible choices for analytical work. But, the mere mention of these techniques may strike fear into the hearts of your users. What you want is a way for your users to compile the data they want to analyze and receive some guidance and recommendations on how best to analyze this data based on the data type and other factors. THEN, all the user has to do is let the system do the work for them, using the best technique and the analysis is complete!

Discover how easy it is to capitalize on Augmented Analytics and Predictive Modeling! Business Users are the lifeblood of the organization. Encourage data sharing and Data Literacy and give business users what they need to the best asset possible.

If you would like to give your organization a competitive advantage, and discover how Augmented Analytics Learning and simplified use of sophisticated Predictive Modeling Algorithms and Analytical Techniques can help your business users and your organization, Contact Us today.

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Why is Natural Language Processing Important to Enterprise Analytics? https://www.smarten.com/blog/why-is-natural-language-processing-important-to-enterprise-analytics/ Thu, 17 Jan 2019 12:00:08 +0000 https://www.smarten.com/blog/?p=6129 Continued]]> Why is Natural Language Processing Important to Enterprise Analytics?

The impact of natural language processing (NLP) on the productivity and decision quality within an organization cannot be overstated. As simplified search analytics expands into all corners of the enterprise, the business can expect business users to embrace advanced analytics and, in so doing, to become more of an asset to the organization.

The Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics, published on October 31, 2018, includes the following strategic assumptions:

  • By 2021, conversational analytics and natural language processing (NLP) will boost analytics and BI adoption from 32% of employees to over 50% of an organization’s employees, to include new classes of users particularly in front offices.
  • By 2020, 50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.

Consumers and business users alike have used natural language processing to search for and find information and, in the case of business users, there is an increasing expectation that search analytics will be simple and as easy to use as the search mechanisms provided by Google and other social networking companies and search engines.

So, what is search analytics today? It is a process that allows a business user with average technical skills to leverage sophisticated algorithms and techniques in a simple environment. With natural language-processing-based search capability, users do not need to scroll through menus and navigation. They can simply enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

An enterprise that commits to these types of advanced data analytics tools can enjoy the benefits of a shared understanding of data and goals, improved decision-making, fact-based analysis that avoids guesswork and allows for refined planning and forecasting at every level of the organization. Data Scientists and professional analysts can focus on strategic issues and analytical projects that require 100% accuracy while business users enjoy the benefits of increased accuracy, improved productivity, and ease of data sharing and collaboration.

Users ask a simple question and get a simple answer. For example, a business user might create the following search using natural language: ‘how did John Smith’s product sales in 2017 compare to his product sales in 2016?’

Conversational analytics and natural language processing (NLP) will advance the knowledge and skill of every business user and educate each user in the importance of metrics and analysis, so every business user will become a crucial business asset.

By simplifying search analytics, businesses will simplify and improve planning, forecasting, and capitalize on competitive opportunities and human capital and resources will be optimized.

Original Post: Why is Natural Language Processing Important to Enterprise Analytics?

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Assisted Predictive Modeling for Business Users! https://www.smarten.com/blog/2019/01/15/january-15-2019/assisted-predictive-modeling/ Tue, 15 Jan 2019 09:34:22 +0000 https://www.smarten.com/blog/?p=6124 Continued]]> Assisted Predictive Modeling Improves Forecasting!

How Can I Leverage Assisted Predictive Modeling to Benefit My Business?

Some people hear the term ‘assisted predictive modeling’ and their eyes cross. They immediately presume that we are talking about something complex and certainly NOT for them. Nothing could be further from the truth. This seemingly complex term actually describes a technique that is designed to be suitable for business users with average technical skills and, with these tools, the average user can enter the age of advanced analytics and make educated, confident business decisions about forecasts and predicted results.

Assisted Predictive Modeling provides the user with auto-suggestions and recommendations so the user can leverage the best analytical technique for a particular type of data. These tools allow the organization to apply Predictive Analytics to any use case using forecasting, regression, clustering and other methods to analyze an infinite number of use cases including customer churn, and planning for and target customers for acquisition, identify cross-sales opportunities, optimize pricing and promotional targets and analyze and predict customer preferences and buying behaviors.

And, there are many benefits to the user and to organization!

  • No complex algorithms or data manipulation
  • Auto-recommendations for algorithms to explore underlying data without advanced knowledge
  • No advanced data science skills required
  • Analyze, share and optimize business potential
  • Business users can prototype and hypothesize without professional assistance
  • Support for day-to-day business decisions
  • Insight, perspective and analysis
  • Improved agility for business development
  • Timely and accurate decision-making
  • Emergence of power users and data popularity
  • Transformation to citizen data scientists

If you are looking for a way to leverage Advanced Analytics in a self-serve environment and to improve the knowledge and value-add of every team member, assisted predictive modeling tools can help you to achieve a more consistent understanding of targets and goals across the organization and a more fact-based forecasting and planning process.

Explore Assisted Predictive Modeling and find out how it can benefit your organization.

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The Smarten Advanced Analytics Team Will Participate in the Vibrant Global Summit, Jan 18-22, 2019 https://www.smarten.com/blog/smarten-advanced-analytics-team-will-participate-in-vibrant-global-summit-jan-18-22-2019/ Thu, 10 Jan 2019 14:36:23 +0000 https://www.smarten.com/blog/?p=6120 Continued]]> The Smarten Advanced Analytics Team Will Participate in the Vibrant Global Summit, Jan 18-22, 2019

ElegantJ BI is pleased to announce that it will participate in The Vibrant Gujarat Global Summit 2019, where it will engage with partners and clients and demonstrate its Smarten product and innovative approach to advanced analytics. The Summit will take place at the Mahatma Mandir Exhibition cum Convention Centre, Gandhinagar, Gujarat, India January 18 through January 22, 2019.

Smarten clients, partners and technology innovators are invited to visit the Smarten team at Stall No 6n, in Hall No 5 to explore the Smarten product and its dynamic features and value-add.

Kartik Patel, CEO of ElegantJ BI, says, “The Smarten product continues to evolve in exciting and productive ways with Natural Language Processing (NLP) and Clickless Search Analytics that allow every business user to ask questions, receive answers and perform analysis without the specialized skills of a data scientist.”

The Smarten approach to augmented data discovery and advanced analytics tools is founded on technology leadership, customer and partner focus and a team environment that enables creativity, innovation and exciting advances in Advanced Analytics.

The ElegantJ BI Smarten Augmented Analytics solution represents the evolution of data analytics, and the Self-Serve Data Preparation, Smart Data Visualization, Assisted Predictive Modeling and Clickless Analytics powered by Natural Language Processing.

“We are pleased to participate in the 9th Vibrant Gujarat Global Summit,” says Patel. “And to share the latest Smarten product features with clients, partners and technology innovators.”

The Smarten Augmented Analytics team of India based experts combines business intelligence, business analysts, data scientists, engineers and data warehouse (DWH) professionals, working together in a dedicated team environment to redefine and re-imagine analytical tools and create a new dynamic where business users, data scientists and IT staff can thrive and business can engender data literacy and data democratization.

Join the Smarten Team at the Vibrant Gujarat Global Summit 2019, January 18 through January 22, 2019 at the Mahatma Mandir Exhibition cum Convention Centre, Gandhinagar, Gujarat, India, Stall No 6n, in Hall No 5 and explore the innovative evolution of augmented analytics and advanced data discovery, suitable for every member of the organization.

Read More: The Smarten Advanced Analytics Team Will Participate in the Vibrant Global Summit, Jan 18-22, 2019

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The Advantages of Augmented Analytics! https://www.smarten.com/blog/benefits-of-advanced-analytics-are-numerous/ Tue, 08 Jan 2019 14:30:48 +0000 https://www.smarten.com/blog/?p=6116 Continued]]> The Benefits of Advanced Analytics Are Numerous!

What Are the Advantages of Augmented Analytics?

Advanced analytics benefits are too numerous to mention! What if your business users could leverage self-serve advanced analytics to see and use data in a way that made their jobs easier and made them more of an asset to the business?

Augmented Analytics advantages support users by empowering them and encouraging user adoption with auto-recommendations and suggestions that help them visualize data in a way that makes sense for the type of data they are analyzing. Assisted predictive modeling suggests techniques to analyze data that will result in the right outcome for the goals of the analysis. Self-serve data preparation walks the user through the data preparation process so that they can easily prepare data for analysis without the assistance of IT or a data scientist.

The benefits of Advanced Analytics include data sharing and allow the organization to produce fast, dependable insights and improve the value of business analysis across the enterprise. Empower users with augmented analytics that include ETL for business users, smart data visualization and more!

Business users get support for day-to-day decisions and can quickly and easily test theories and hypotheses in a risk-free environment. The organization enjoys improved agility for business development and timely, accurate business decisions. The enterprise can transform business users into Citizen Data Scientists and optimize the resources of the skilled data scientist with a renewed focus on strategic projects and data analysis.

Data scientists can reduce involvement in day-to-day analysis and focus on projects that require 100% accuracy to achieve mature modeling goals. IT can use their time on more critical projects and avoid routine report and analytical requests.

If you would like to enjoy the Advantages of Advanced Analytics, Contact Us today to get started.

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What are Citizen Analysts and How Can They Improve Your Organization? https://www.smarten.com/blog/what-are-citizen-analysts-and-how-can-they-improve-your-organization/ Thu, 03 Jan 2019 12:04:15 +0000 https://www.smarten.com/blog/?p=6111 Continued]]> What are Citizen Analysts and How Can They Improve Your Organization?

The days of data silos and isolated teams of data analysts and IT staff are behind us. Today, every business user must incorporate accurate analysis into the decision-making process. Enter, the Citizen Analyst!

What is a Citizen Analyst?

Citizen Analysts (AKA Citizen Data Scientists) represent a new breed of business user. These business users have adopted business intelligence and advanced analytical tools to gather and analyze data from varied data sources and use that analysis to identify the root cause of problems, identify opportunities, solve problems and share crucial data to support business decisions. Citizen Analysts create and generate data models and use sophisticated analytics that are supported by easy-to-use interactive BI dashboards. By definition, Citizen Analysts are not data scientists, or professional analysts or IT staff. Rather, they hold varied positions within the business organization and use data analysis to support decisions made within their business role, team or responsibility.

In the past, business users would request reports and data analysis from an analyst or IT professional with defined report and analytical requirements and then wait for the results to be produced through data extraction, transformation and loading (ETL) or analytical expertise. Today, Citizen Analysts can leverage self-serve business intelligence, and advanced data discovery tools, to produce reports and analysis in a flexible, interactive environment, with drag and drop capability and guided, auto-recommendations for display formats and the type of analysis best suited for a particular purpose.

How Can Citizen Analysts Improve Your Organization?

By enabling data integration and ease of analysis through the organization, the business can cascade knowledge and skill and make it easier for every business user to complete tasks, make accurate decisions and perform with agility in a fast-paced business environment.

Business users can work with self-serve advanced data discovery and advanced analytical tools using a drag and drop interface, with no advanced skill requirement for statistical analysis, algorithms or technical knowledge. Users can gather, prepare, integrate and analyze data, find patterns and trends, share findings and apply to strategic, operational and tactical activities.

These tools take the user beyond data monitoring and simple alerts and thresholds to help them discover subtle and important factors and avoid missteps in projects, markets and customer satisfaction. Self-serve BI tools provide a truly intelligent solution that suggests relationships, identifies patterns, suggests visualization techniques and formats, highlights trends and patterns, and presents predictions, so the organization can promote and encourage Citizen Analysts and advance the objectives and goals of the organization.

The Gartner report entitled, ‘Citizen Data Science Augments Data Discovery and Simplifies Data Science,’ dated December 9, 2016 (ID G00314599) states that, ‘ Through 2017, the number of citizen data scientists will grow five times faster than the number of highly skilled data scientists.’ Clearly, Citizen Analysts are here to stay!

Original Post: What are Citizen Analysts and How Can They Improve Your Organization?

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What is Clickless Analytics? You Will LOVE it! https://www.smarten.com/blog/advanced-analytics-with-natural-language-processing/ Wed, 02 Jan 2019 11:34:50 +0000 https://www.smarten.com/blog/?p=6107 Continued]]> Advanced Analytics with Natural Language Processing!

Search Analytics that Works the Way Users Think!

Everyone knows how to ask a question using the Google search interface. It is easy because it allows the user to phrase the question using normal, human language. Imagine how few users Google would have if they required the user to know and use complex search queries to find a restaurant or locate a movie review!

Now, think about how much easier your Advanced Analytics solution would be if it engaged the user in the same way, allowing them to leverage natural language processing and ask questions in a way that makes sense to them. Your business users could take advantage of sophisticated analytical tools in an easy-to-use environment and get results in a way that makes sense to them.

This Search Analytics technique opens the door wide to data democratization and allows your business to get the most out of the knowledge and skill of every team member by enabling them to access and analyze data and make accurate decisions. Think of this technique as Clickless Analysis.

What is Clickless Analysis? Clickless analysis allows the user to leverage seamless augmented analytics and advanced data discovery, using machine learning and natural language processing (NLP), in a self-serve environment resulting in increased user adoption, improved data democratization, and return on investment (ROI).

Clickless Analytics democratizes advanced analytics so business users can enjoy the benefits of smart data discovery using natural language searches. Simply enter the query in natural language and let the system do the rest. No advanced training is required and no clicking, use of drop down menus or other ‘technical’ tricks and tools is required. Just type the question and you are done. The system will return the results in the same, easy-to-understand, natural language.

Contact Us if you want to discover the benefits of Natural Language Processing, Clickless Analytics and Search Analytics designed to support the way your users think and work.

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Software for Data Visualization to Improve Your Results! https://www.smarten.com/blog/smart-data-visualization-makes-your-users-smarter/ Mon, 31 Dec 2018 10:20:08 +0000 https://www.smarten.com/blog/?p=6103 Continued]]> Smart Data Visualization Makes Your Users Smarter!

Data Visualization Tools that are Smarter Can Make Users Smarter!

Visual Analytics tools enable users to identify relationships, patterns, trends and opportunities and to explore detailed data with simple drill down and drill through capabilities and make sense of data from all sources, with a guided approach that allows users to identify patterns and trends, and quickly complete analysis with clear results.

Think about this for a moment and imagine what this approach to analytics could do for your users and for your company. Data Visualization should tell a story. It should paint a picture! If your business users are adopting self-serve advanced analytics tools, they want these tools to help them with their everyday tasks and reveal answers and ideas that will help them support the goals of the organization and succeed in their jobs.

Smart Data Visualization combines cutting-edge technology and machine learning on the backend, with an intuitive user experience on the front end, business users can easily leverage sophisticated tools with suggestions and recommendations on how to personalize data displays to create meaningful views and collaboration. Smart Data Visualisation will support your data democratization initiative and make it easier for your team members to contribute to the overall success of the business with easy to use, auto-suggestions that recommend visualization techniques for the type of data the user is analyzing.

If you would like to give your organization a competitive advantage, and discover how Data Visualization Software can help your organization, Contact Us today.

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Discover Advanced Analytics Advantages! https://www.smarten.com/blog/benefits-of-augmented-analytics-are-many/ Mon, 24 Dec 2018 09:50:19 +0000 https://www.smarten.com/blog/?p=6098 Continued]]> The Benefits of Augmented Analytics Are Many!

What Are the Advantages of Advanced Analytics?

If you are considering an advanced analytics solution, you are probably wondering what you might get from the solution. Are there real benefits to this type of solution and to the democratization of data and the implementation of a self-serve augmented analytics solution that is meant for business users?

The answer to all those questions is a resounding, ‘yes’! Augmented Analytics Advantages are numerous! By implementing this type of advanced analytics solution in your organization, you can empower business users and hold them accountable for decisions. You can plan and forecast with more confidence, and you can make accurate, timely business decisions that are dependable. You can identify opportunities, solve problems and share data to ensure that everyone in the organization is informed, and working with the latest data.

With the right, self-serve tools, you can provide a sophisticated environment that is easy to use and allows for guidance and recommendations to ensure the appropriate type of smart data visualization, and provide support for self-serve data preparation and assisted predictive modeling to help users through the planning process. Advanced Analytics Benefits include advantages for business users AND for data scientists:

For Business Users

  • Support for day-to-day business decisions
  • Insight, perspective and analysis
  • Quick hypothesis and prototyping
  • Improved agility for business development
  • Timely and accurate decision-making
  • Emergence of power users and data popularity
  • Transformation to citizen data scientists

For Data Scientists

  • Reduction in day-to-day requests
  • Ability to focus on strategic projects
  • Focus on projects that require 100% accuracy
  • Ability to achieve mature modeling goals

If you would like to give your organization a competitive advantage, and discover the Advantages of Augmented Analytics, Contact Us.

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Business Users and Data Scientists Get Predictive Analytics! https://www.smarten.com/blog/2018/12/17/december-17-2018/assisted-predictive-modeling-r-integration/ Mon, 17 Dec 2018 13:25:36 +0000 https://www.smarten.com/blog/?p=6094 Continued]]> Assisted Predictive Modeling with R Integration

Biz Users Get Plug n’ Play Analytics, Data Scientists Get R Integration!

When someone says ‘plug n’ play’, a lot of people think of the idea of plugging in an electrical appliance and having it run instantly. I think plug n’ play analysis should be that simple as well!

My business users don’t have the skill, the patience or the time to become data scientists. But, they don’t have to be data scientists to use augmented analytics. Tools like Assisted Predictive Modeling allow the average business user to become a Citizen Data Scientist with tools that offer guidance and auto-suggestions to help the user arrive at the outcome they need without being frustrated or having to call in an army of analysts and IT staff to help them complete their analysis.

Predictive Analytics for business users leverages machine learning and assisted predictive modeling to help users achieve the best fit and ensure that they use the most appropriate algorithm for the data they wish to analyze. With these tools, users can explore patterns in data and receive suggestions to help them gain insight on their own without dependence on IT or data scientists. The enterprise can provide the tools needed at every level of the organization with tools and data science for business users that are sophisticated in functionality and easy-to-use for users at every skill level.

Predictive modeling capability and auto-recommendations simplify use and allow business users to leverage Predictive Analytics Algorithms without the expertise and skill of a data scientist. The plug n’ play predictive analytics and predictive modeling platform is suitable for business users. These tools allow the organization to apply predictive analytics to any use case using forecasting, regression, clustering and other methods to analyze an infinite number of use cases including customer churn, and planning for and target customers for acquisition, identify cross-sales opportunities, optimize pricing and promotional targets and analyze and predict customer preferences and buying behaviors.

In addition to all of these benefits, you can sell your organization on this type of easy-to-use, sophisticated augmented analytics tool by pointing out that data scientists who want to leverage R Script can use these tools to capitalize on their expertise and on enterprise investments in R open source platform. They can perform statistical and predictive algorithms, and complex analysis to provide the depth of detail and advanced analytics and reporting the organization needs for strategic decision-making.

So EVERYONE is happy. Business users have the plug n’ play predictive analytical capacity they need to function at peak performance and data scientists can leverage sophisticated tools to add value to strategic initiatives.

What could be better? If this sounds good to you, I can tell you how to get started. Assisted Predictive Modeling with R Integration

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What is Advanced Analytics and What Are the Benefits of Advanced Analytics? https://www.smarten.com/blog/what-is-advanced-analytics-and-what-are-the-benefits-of-advanced-analytics/ Wed, 12 Dec 2018 12:01:11 +0000 https://www.smarten.com/blog/?p=6089 Continued]]> What is Advanced Analytics and What Are the Benefits of Advanced Analytics?

Many businesses are just discovering the benefits of self-serve business intelligence and establishing data democratization initiatives but, as every business manager and team member knows, business markets and competition move rapidly and yesterday’s business intelligence initiatives are morphing into advanced analytics efforts. As businesses consider the transition, it is important to understand the advantages of advanced analytics.

What is Advanced Analytics?

Advanced analytics is a comprehensive set of analytical techniques and methods designed to help businesses discover trends and patterns, solve problems, accurately predict the future and drive change using data-driven, fact-based information. It takes the enterprise beyond business intelligence by offering sophisticated algorithms and analytical techniques that allow for more refined, detailed answers and more creative, educated decisions.

Business Intelligence uses historical data to reveal where the business has been, and managers can use this data to predict competitive response, and ongoing changes in buying behaviour. Advanced analytics tools allow for better predictive analytics and provide insight into change as it is taking place, so businesses can be more responsive and forecasts and plans will be more accurate.

As the analytical solutions market evolves, the advent of self-serve tools provides business users with the ability to leverage self-serve data preparation, smart data visualization and assisted predictive modeling and operate at a level that was not possible before. Without the assistance of a data scientist, business users with average skills can explore data and enjoy the advantages of augmented analytics with guidance and recommendations that will help them get better, clearer results without the skills or knowledge of an analyst or data scientist. These tools use sophisticated algorithms and analytical techniques, married with natural language processing (NLP) so users can ask questions using normal human language and get results in the same way. The addition of Clickless Search Analytics makes it easier to bring advanced analytics to the organization and engage business users with full confidence in user adoption.

A self-serve advanced analytics solution Incorporates computational linguistics, analytical algorithms and data mining into a self-serve environment and provides an easy-to-use NLP search capability for swift, accurate data analysis. It suggests relationships and provides insight into previously hidden data so business users can explore and ‘discover’ crucial business results, patterns, trends, issues and opportunities and improve productivity and smart decision making across the organization.

What Are the Benefits of Advanced Analytics?

When an enterprise chooses to implement self-serve Advanced Analytics, it encourages user empowerment and user adoption. It also enables data sharing and allows the organization to produce fast, dependable insights and improve the value of business analysis across the enterprise, democratizing the use of advanced analytics and augmented predictive tools among business users. As the business world discovers the benefits of smart data discovery, these tools have evolved, making it easier for business users and data scientists to gather, integrate and analyze data.

Advantages of Augmented Analytics for Business Users:

  • Support for day-to-day business decisions
  • Insight, perspective and analysis
  • Quick hypothesis and prototyping
  • Improved agility for business development
  • Timely and accurate decision-making
  • Emergence of power users and data popularity
  • Transformation to citizen data scientists

Advanced Analytics Benefits for Data Scientists:

  • Reduction in day-to-day requests
  • Ability to focus on strategic projects
  • Focus on projects that require 100% accuracy
  • Ability to achieve mature modeling goals

In short, if an organization selects an advanced analytics solution that supports augmented data discovery with tools that are suitable for business users and data scientists alike, it can provide maximum results, quickly and easily, with minimal training requirements, minimum implementation time and minimal support to achieve rapid ROI and low TCO.

Original Post: What is Advanced Analytics and What Are the Benefits of Advanced Analytics?

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Why is Natural Language Processing Important? https://www.smarten.com/blog/do-i-need-clickless-search-analytics/ Wed, 05 Dec 2018 14:48:51 +0000 https://www.smarten.com/blog/?p=6085 Continued]]> Do I Need Clickless Search Analytics?

What is Clickless Analysis and Why Should I Care?

What is Clickless Analysis, and why is it important to your Advanced Analytics solution? Clickless Search Analytics provides seamless augmented analytics and advanced data discovery, using machine learning and natural language processing (NLP), in a self-serve environment that is easy enough for every business user resulting in increased user adoption, improved data democratization, and return on investment (ROI).

Natural Language Processing Search Analytics (NLP) is a crucial component of Search Analytics and smart data discovery today. NLP search allows business users to create complex searches without endless clicks and complex navigation and commands. Using this type of search analytics, users can access and view clear, concise answers and analysis quickly and easily.

Advanced analytics with Natural Language Processing (NLP) provides a familiar Google-type interface where a user can compose and enter a question using common human language so users do not need to scroll through menus and navigation. No clicking required! They simply enter a search query in natural language and the system translates the query, and returns the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

What is Clickless Analytics? Smarten Auto Insights Has It! If you want to explore the possibilities, and take smart data discovery to the next level, Contact Us.

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Does Data Democratization Result in Data Anarchy and Bad Business Decisions? https://www.smarten.com/blog/does-data-democratization-result-in-data-anarchy-and-bad-business-decisions/ Thu, 29 Nov 2018 12:12:36 +0000 https://www.smarten.com/blog/?p=6080 Continued]]> Does Data Democratization Result in Data Anarchy and Bad Business Decisions?

One of the most common excuses used to avoid data democratization and self-serve augmented data discovery is that the organization cannot guarantee data integrity and that, if business users have access to dated, incorrect or incomplete data, the resulting decisions will not be better but rather worse than the decisions made today. Further, this theory says that the distribution of this inaccurate data will have a systemic effect on truth and promote disastrous business decisions. There are several mistaken assumptions in this theory, and several ways an organization can guarantee success in a data democratization initiative.

An augmented data discovery solution is not an argument for data anarchy:

Rather, with the tools provided, an organization can put in place the appropriate data governance to address concerns regarding user access and the data that is integrated and accessed by business users. Data governance must encompass the ways in which data is used, shared AND edited. The right self-serve Augmented Data Discovery tools allows for a balance of data governance and data democratization for easy access and allows the IT team to control appropriate user access and data security at the individual, team, department, division and corporate level, without sacrificing data democratization.

Every person in the organization should be accountable:

We have become so used to the idea that IT is responsible for IT that we don’t think of data as one of the aspects of accountability we expect of each team member.

Your analytics strategy should empower business users AND make them more accountable and more of an asset in building business success. And, you can improve and refine the quality and interpretation of data to enhance decision-making. With integrated, mobile access, users may still need context to properly interpret and analyze data and share and present it with clarity and precision. Promote Data Literacy, and train and hold your business users accountable for using and presenting data appropriately to ensure clarity and appropriate decision-making.

Provide curated data with clear provenance and assignation:

You can establish the IT team as curators of validated data, and identify and highlight data, images with tags or ‘watermarks’. This process is not necessarily required for every type of data but by establishing priorities for when and how these labels are applied; the organization can significantly improve user comprehension and appropriate analysis. This kind of labeling can include a ‘data hash’. A Data Hash is a string of numbers and/or letters. If a file is modified, the ID of the modified file will note that it is not the original, so the user understands the source of the data and whether it has been edited. A ‘watermark’ can also be used to allow users to drill down to the original data provenance and see the details of changes and edits to determine whether the data has been substantively altered.

These are just a few of the considerations and methods you can use to ensure appropriate data governance, data democratization and a better understanding of the source of data and allow the organization to take full advantage of self-serve Advanced Data Discovery without fear of data anarchy or loss of data integrity.

Original Post: Does Data Democratization Result in Data Anarchy and Bad Business Decisions?

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Advanced Analytics Tools Can Be Your Secret Weapon! https://www.smarten.com/blog/can-advanced-analytics-software-help-my-business-planning/ Wed, 28 Nov 2018 13:48:33 +0000 https://www.smarten.com/blog/?p=6076 Continued]]> Can Advanced Analytics Software Help My Business Planning?

What is Advanced Analytics, and How Can it Help My Business?

What is advanced analytics? Where to start? Advanced analytics takes analysis to the next level by providing sophisticated techniques designed to get to the heart of data and offer insight and answers with which businesses can make more accurate decisions and develop more dependable, accurate plans and forecasts. Advanced Analytics includes predictive analytics, smart data visualization, and other components.

In today’s business environment, the key to Advanced Analytics lies with tools that are simple enough for business users and will encourage data democratization, user empowerment and accountability and data sharing. That means that Data Discovery Tools must include self-serve features and modules like Smart Data Visualization, with auto-recommended suggestions for how to best visualize data for clarity, as well as self-serve data preparation to allow the average business user to prepare data for analysis without the assistance of an IT staff member or a data scientist.

The solution should also include assisted predictive modeling that provides augmented analytics with natural language processing (NLP) so users can ask questions using normal language and receive answers in the same way. Simplified search analytics are key to user adoption and understanding.

These augmented data discovery tools are crucial to data democratization and to enhancing and supporting business decisions and competitive advantage.

If you would like to explore the potential of Advanced Data Discovery, Contact Us today!

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Predictive Analytics for Every Skill and Use! https://www.smarten.com/blog/assisted-predictive-modeling-that-is-suitable-for-all-users/ Mon, 26 Nov 2018 13:44:06 +0000 https://www.smarten.com/blog/?p=6072 Continued]]> Assisted Predictive Modeling That is Suitable for All Users!

Your Business Users Will LOVE Predictive Analytics Tools!

Predictive Analytics used to involve a crystal ball but, today, there are other options and they are more widely accepted in the business community! With the right predictive analytics tool, your business can hypothesize, test theories, discover the effects of a possible price increase, discover and address changing buying behavior and develop appropriate competitive strategies.

And, with Assisted Predictive Modeling, your business users can leverage sophisticated tools, algorithms and techniques in a simple, intuitive environment to predict future results. Predictive modeling provides auto-recommendations and auto-suggestions to simplify use so business users can take advantage of predictive algorithms without the expertise and skill of a data scientist.

Assisted predictive modelling tools allow the organization to apply Predictive Analytics to any use case using forecasting, regression, clustering or other methods. You can analyze an infinite number of use cases including customer churn, and planning for and targeting of customers for acquisition, and you can identify cross-sales opportunities, optimize pricing and promotional targets, and analyze and predict customer preferences and buying behaviors.

If you would like to give your business users these sophisticated, easy-to-use Predictive Analytics Tools, Contact Us.

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Smart Data Visualization Gets it Done! https://www.smarten.com/blog/2018/11/19/november-19-2018/what-is-smart-data-visualization/ Mon, 19 Nov 2018 14:07:08 +0000 https://www.smarten.com/blog/?p=6066 Continued]]> Want Smarter Users? Get Smart Data Visualization!

Smart Data Visualization Makes Your Users Smarter!

What on earth is smart data visualization? Is it a computer that actually ‘sees’ data for you and does the analysis? Not quite. Sorry to say that even with advanced analytics that provides smart data visualization, you will have to do SOME work, but fortunately, you will not have to guess!

Smart Data Visualization is a method of Visual Analytics that takes the data you want to analyze and allows business users to easily an intuitively analyze, share and present information without waiting for assistance from visualization experts or programmers. With augmented data discovery tools, business users can cut through that mountain of data to find those elusive nuggets of information that have the most impact on business results. The user can perform analysis with ‘augmentation’ or help from the analytics solution to suggest the best way to visualize the data based on data type, volume, dimensions, patterns and nature of data.

So, the system doesn’t exactly do the thinking for you but it does make the analysis and visualization much easier! Business users can interact easily with data discovery tools and analytics software and build a view that will tell a story using guided visualization and recommended data presentation so there is no need for assistance or delays.

It is not magic! The solution uses cutting-edge technology and machine learning on the backend, and combines it with an intuitive user experience on the front end, so business users can leverage sophisticated tools with suggestions and recommendations on how to personalize data displays to create meaningful views and collaboration.

Machine learning provides guidance to determine the visualization technique that will be the best fit for the data business users want to analyze. That allows for a better understanding of data so users can identify relationships, patterns, trends and opportunities and explore detailed data.

If smart data visualization sounds like something you could use, you can explore the benefits here: Smart Data Visualization

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What is Predictive Analytics and Can it Help You Achieve Business Objectives? https://www.smarten.com/blog/what-is-predictive-analytics-and-can-it-help-you-achieve-business-objectives/ Mon, 19 Nov 2018 11:45:26 +0000 https://www.smarten.com/blog/?p=6062 Continued]]> What is Predictive Analytics and Can it Help You Achieve Business Objectives?

The process of predictive analytics has come far in the past decade. No longer is this process the sole responsibility of data scientists or IT staff. Today’s self-serve predictive analytics and forecasting tools are designed to support business users and data analysts alike.

What is Predictive Analytics?

Predictive analytics is the process of forecasting or predicting business results for planning purposes. Predictive analytics employs various analytical and modeling techniques, leveraging historical data and business results to identify crucial relationships, opportunities and risks so that business managers can more accurately predict growth, and competitive and market changes and identify trends and patterns.

A self-serve predictive analytics tool allows the average business user to access sophisticated predictive algorithms without the expertise and skill of a trained data scientist, so users who are not statisticians or predictive algorithm experts, can leverage self-service plug n’ play predictive analytics tools to confidently make business decisions.

Can Predictive Analytics Help You Achieve Business Objectives?

If an organization wishes to be successful in the market and in its competitive efforts, it must accurately forecast and predict the future of its business, plan for new locations and products or services, and optimize internal operations. Predictive analytics can help a business understand the buying behavior of its customers and prospects and plug n’ play predictive and forecasting tools help businesses to create Citizen Data Scientists and establish metrics and goals across the enterprise for uniform execution and understanding of business objectives.

A Gartner report published July 27, 2017 (ID G00326012), entitled, ‘Augmented Analytics Is the Future of Data and Analytics’, predicts that ‘By 2020, the number of users of modern business intelligence and analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate – and deliver twice the business value – of those that are not.’

The benefits of these augmented data discovery and predictive analytic tools is undeniable. Self-Serve, Plug n’ Play Predictive Analysis allows the organization to identify which customers the organization may be at risk of losing, based on purchasing patterns, demographics, geographic and other macro parameters. These predictive tools can identify sets of prospects to be converted to customers, identify price point changes, new product opportunities or new features for existing products, and where to open a new location based on various parameters. Predictive analytics can help the organization to develop promotions, expand marketing channels, and identify high-performance, high-value targets for the business.

While self-serve predictive analytics tools are designed to satisfy the needs of business users, these tools can also be used by skilled data scientists and business analysts for statistical and predictive analytics algorithms, to perform more sophisticated and complex analysis, achieve clarity and provide detailed, meaningful advanced analytics and reporting for the organization.

When an organization employs self-serve predictive analytics tools to forecast and plan business results, it enables users at every level of the organization to participate in, and understand the impact of, activities, tasks and objectives designed to achieve dependable results.

Original Post: What is Predictive Analytics and Can it Help You Achieve Business Objectives?

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Gartner ‘Other Vendors to Consider for Modern Analytics and BI’, October, 2018 Includes Elegant MicroWeb (Smarten) in Category; Other Modern: Asia-Focused https://www.smarten.com/blog/gartner-other-vendors-to-consider-for-modern-analytics-and-bi-october-2018-includes-elegantmicroweb-smarten-in-category-other-modern-asia-focused/ Wed, 14 Nov 2018 11:16:27 +0000 https://www.smarten.com/blog/?p=6052 Continued]]> Gartner 'Other Vendors to Consider for Modern Analytics and BI', October, 2018 Includes Elegant MicroWeb (Smarten) in Category; Other Modern: Asia-Focused

ElegantJ BI, an innovative vendor with its Smarten approach to Augmented Analytics and Self-Serve Data Preparation, is pleased to announce that Elegant MicroWeb (Smarten) is included as one of the Other Modern: Asia-Focused Vendors in the Gartner ‘Other Vendors to Consider for Modern Analytics and BI’ Report published on October 29, 2018.

Kartik Patel, CEO of ElegantJ BI says, “Our continued focus on data democratization and on business user access to modern BI tools has evolved in our product roadmap to include ‘Clickless Analytics’ or Natural Language Processing (NLP) Search Analytics. These product innovations provide the foundation for exciting, new, easy-to-use features and self-serve, deep dive functionality that free IT and data scientists to focus on strategic issues, while ensuring an integrated, collaborative view of metrics, activities and tasks that enable business success.”

The Smarten offering is powered by ElegantJ BI, and provides augmented data discovery tools that allow business users to perform early prototyping and hypotheses testing, without advanced skills. Users are supported by an end-to-end enterprise-reporting platform and can perform self-serve data preparation with auto-recommendations and guidance to prepare the data in a manner appropriate to the data type and other considerations.

“The Smarten solution establishes a solid foundation for advanced analytics,” says Patel. “The user has access to dashboards, reports and KPIs, and can perform analytical functions without the assistance of IT, using a browser-based solution that is suitable for all types of mobile devices.”

The Smarten product can be embedded in third-party application using web services and JavaScript APIs, and supports numerous, sophisticated predictive and analytical techniques and integration with R Scripting. The Smarten solution suite includes assisted predictive modeling, smart data visualization and self-service data preparation – all designed to transform business users into Citizen Data Scientists.

More information on the business intelligence and data preparation market is available in the Gartner report: Other Vendors to Consider for Modern Analytics and BI, Analyst(s): Shubhangi Vashisth, Rita Sallam, Cindi Howson, James Richardson, Carlie Idoine. Published: 29 October 2018 ID: G00369983

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include assisted predictive modeling, smart data visualization, self-serve data preparation and clickless analytics with natural language processing (NLP) for search analytics. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to advanced data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Research Market Guide to Self-Service Data Preparation, as a Niche BI and Analytics Vendor in the Gartner Report, Competitive Landscape in the BI Platforms and Analytics Software, Asia/Pacific, as a Representative Vendor in the Gartner Market Guide for Enterprise-Reporting-Based Platforms, and a Listed Vendor in the Other Vendors to Consider for Modern BI and Analytics, Gartner Report.

Read More: Gartner ‘Other Vendors to Consider for Modern Analytics and BI’, October, 2018 Includes Elegant MicroWeb (Smarten) in Category; Other Modern: Asia-Focused

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White Paper – Paving the Road to Success: From Spreadsheets to Advanced Analytics https://www.smarten.com/blog/white-paper-paving-the-road-to-success-from-spreadsheets-to-advanced-analytics/ Mon, 12 Nov 2018 14:32:54 +0000 https://www.smarten.com/blog/?p=6048 Continued]]> White Paper - Paving the Road to Success: From Spreadsheets to Advanced Analytics

The ‘culture’ of an organization includes habits and processes that are familiar and comfortable. There are many aspects to the success of a culture change within a business organization. When an enterprise decides to move from the silo spreadsheet culture to Advanced Analytics it can go beyond simple spreadsheet analysis and BI reporting with a comprehensive analytical environment that identifies business opportunities, and provides metrics and measurements, predictive modeling and forecasting, and self-serve data preparation tools for business users. By understanding how and why team members employ spreadsheets, the enterprise can develop a strategy to move to a fact-based, secured environment with tools that users will want to adopt.

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Using Smarten’s Assisted Predictive Modeling in the FMCG industry https://www.smarten.com/blog/presentation-of-using-predictive-analytics-in-consumer-products-industry/ Tue, 06 Nov 2018 10:33:39 +0000 https://www.smarten.com/blog/?p=6044 Continued]]>

This is a video of a presentation which outlines how a predictive analytics model can be set up for the sales department of a consumer product company.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Smarten’s Assisted Predictive Modeling factors impacting sales https://www.smarten.com/blog/demonstration-of-using-smarten-assisted-predictive-modeling/ Tue, 06 Nov 2018 10:23:01 +0000 https://www.smarten.com/blog/?p=6041 Continued]]>

This video demonstrated Self-Service Data Preparation and Assisted predictive modeling using practical data which combines data from within the organisation and data from external sources to make effective prediction and analysis. This video focuses on consumer products used in the building industry.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Smarten’s Assisted Predictive Modeling manpower impact on sales https://www.smarten.com/blog/predictive-analytics-for-determining-criteria-for-manpower-effectiveness-in-sales/ Tue, 06 Nov 2018 10:11:42 +0000 https://www.smarten.com/blog/?p=6038 Continued]]>

This video looks at a scenario where data on workforce comprising of education, years with the organisation, number of wholesale stockiest managed by the individual, CAGR in sales for individual and value of sales is analysed to determine which factor impacts the sales and to what extent. This would allow an organisation to plan a workforce strategy for sales development. More criteria can be added to this model.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Smarten’s Assisted Predictive Modeling in Consumer Product Industry https://www.smarten.com/blog/presentation-and-demonstration-of-using-smarten-assisted-predictive-modeling/ Tue, 06 Nov 2018 07:39:25 +0000 https://www.smarten.com/blog/?p=6035 Continued]]>

This video has a presentation on how one can use data from multiple systems across the organisation along with data from external sources to run predictive analytics using Assisted Predictive Modeling. The video has a complete presentation which is followed by a demonstration. Each of these videos is available independently.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Advantages of Augmented Analytics! https://www.smarten.com/blog/benefits-of-advanced-analytics/ Wed, 31 Oct 2018 13:12:49 +0000 https://www.smarten.com/blog/?p=6031 Continued]]> Benefits of Advanced Analytics!

What Are the Benefits of Augmented Analytics?

Augmented Analytics may sound complicated but the goal is to use all those complicated analytical techniques and algorithms as an underpinning for an advanced analytics solution that is suitable for the skills o an average business user. In other words, the system may be complicated, but the interface and process to gather and analyze information is easy!

Augmented Analytics is designed to encourage user empowerment and user adoption. It enables data sharing and allows the organization to produce fast, dependable insights and improve the value of business analysis across the enterprise, thereby democratizing the use of advanced analytics.

Did you know that the renowned technology analyst firm, Gartner predicts that the number of Citizen Data Scientists will grow five times faster than the number of expert data scientists through 2020? They also predicted 2019 that the amount of advanced analytics produced by citizen data scientists will surpass the data produced by data scientists, and that by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage of these tools by citizen data scientists.

That’s a lot of data, and a major transition! With the right augmented analytics support, every enterprise can provided guidance and recommendations to make business users more productive and more of an asset to the organization. Advanced Analytics Advantages include strong support for day-to-day business decisions, improved insight and perspective for planning, forecasting and decision-making, quite hypotheses and prototyping (without the risk of a misstep in business), business development agility, timely and accurate support at every level of the organization, and the emergency of power users and citizen data scientists.

And, that’s just the beginning. Your IT and data science teams will also benefit by optimizing their time for critical activities and allowing them to focus on strategic initiatives.

If Augmented Analytics Benefits sounds like the right approach for your organization, Contact Us.

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Explain NLP, Search Analytics and Clickless Analytics! https://www.smarten.com/blog/search-analytics-nlp-and-clickless-analytics/ Mon, 22 Oct 2018 11:17:52 +0000 https://www.smarten.com/blog/?p=6026 Continued]]> Search Analytics, NLP and Clickless Analytics

What Is: Search Analytics, NLP and Clickless Analytics Explained!

What is Search Analytics? When a business user or analyst employs search analytics, it is for the express purpose of gathering content and data from various sources to achieve an analytical outcome. The data is gathered by ‘searching’, often using natural language processing. The benefits of natural language processing are many.

What is Natural Language Processing? Natural language processing allows those who are not data scientists or programmers to create a search just as one might ask a question of a waiter at a restaurant, or an auto mechanic. Think of the ease with which one can search on a search engine. You might ask, ‘Who holds the world record in the long jump in the Olympics’. When you execute this search, the search engine or data analysis tool will look for the appropriate information and present it in the same natural language the person used to ask the question. So…no statistical mumbo jumbo or encrypted results!

What is Clickless Analytics? Clickless Analytics is a form of search analytics that provides seamless augmented analytics and advanced data discovery, using machine learning and the same natural language processing (NLP) we discussed above. It is presented in a self-serve environment that is easy enough for every business user. Because of the seamless, ease-of-use and guidance provided by the system, business users will adopt the solution, resulting in improved data democratization, and return on investment (ROI). Clickless Analytics democratizes advanced analytics so business users can enjoy the benefits of smart data discovery using natural language searches. Simply enter the query in natural language and let the system do the work, without the requirement for advanced training.

If your business can benefit from Natural Language Processing and Search Analytics, Contact Us.

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Self-Service Data Prep Empowers Business Users! https://www.smarten.com/blog/self-serve-data-prep-and-etl-for-business-users/ Mon, 15 Oct 2018 11:05:52 +0000 https://www.smarten.com/blog/?p=6022 Continued]]> Self-Serve Data Prep and ETL for Business Users!

What is Self-Service Data Preparation?

Self-serve data preparation allows business users with average technical skills to gather and prepare data for analysis without the help of an IT professional or a data scientist. So, why is that important? Data prep is often the forgotten step in advanced analytics but, without a self-service data preparation tool, the process can take a long time and it can result in incomplete data, data that is hard to analyze and, sometimes, a total work stoppage while IT or a data scientist attempts to sort through the issues and untangle the mess.

Data extraction, transformation and loading (AKA ETL) can be complicated but your business users don’t have the time or the patience for that complicated process. What they need is the information to make a decision, to share and to leverage to achieve goals and stay on track. Self-Serve Data ETL assures that business users can work through a few steps and engage in augmented data preparation, allowing the system to make recommendations and suggestions along the way so that the data is prepared in the right way for the type of data and the needs of the user, and the resulting analysis achieves the user goals.

These easy-to-use tools allow business users to transform, shape, reduce, combine, explore, clean, sample and aggregate data, without the need for SQL skills, ETL or other programming language.

Self-Serve Data Preparation empowers every business user and allows them to prepare data for their analytics using tools that enable data extraction transformation and loading – ETL for business users! In other words, business users can quickly move data into the analytics system without waiting for assistance.

If your business users can benefit from Self-Service Data Prep, Contact Us.

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Natural Language Processing for Advanced Analytics! https://www.smarten.com/blog/2018/10/10/october-10-2018/nlp-search-analytics-advanced-analysis/ Wed, 10 Oct 2018 10:26:41 +0000 https://www.smarten.com/blog/?p=6015 Continued]]> Simple Search Analytics to Simplify Advanced Analytics

Advanced Analytics That Speaks Your Language!

Imagine you are trying to search for something in a store, but there is no one there to help you and all of the products are mixed up. You have to walk up and down every aisle, trying to find what you want and if you want to compare two things you will have to remember where you found that other product or drag it with you and then try to remember where to put it when you are ready to put it back. Not fun!

Now, imagine you are trying to analyze some data. For this analysis, you need to know some very specific things but you don’t have time to plow through a lot of irrelevant results in a search. Let’s go back to that store. What if there was someone standing at the entrance who could tell you exactly where to go to find what you wanted.

And, for that analytics projects, imagine that, instead of having to use drop down menus, drag and drop or other techniques to pull together what you want – imagine that you could just ask the system a question. Ask a simple question, using natural language. For example, you might ask ‘What is the best performing product in all states for 2018 as compared to 2017’ Simple, right? You just type in the question and the system does the analysis using natural language processing.

How much easier will it be to achieve data democracy and to get your business users to adopt advanced analytics in a self-serve environment? How much easier will it be to achieve the ROI and TCO you need and to gain clarity for business decisions?

Advanced Analytics with Natural Language Processing provides a familiar Google-type interface where a user can compose and enter a question using common human language. For example, a business user might ask, ‘who sold the most bakery products in 2017 in the Southwest region?’

With natural language-processing-based search capability, users do not need to scroll through menus and navigation. They can simply enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.

If this sounds good to you, I can tell you how to get started. Search Analytics

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Predictive Analytics for Business Users Will Jump Start Business Growth https://www.smarten.com/blog/predictive-analytics-for-business-users-will-jump-start-business-growth/ Tue, 09 Oct 2018 11:12:03 +0000 https://www.smarten.com/blog/?p=6011 Continued]]> Predictive Analytics for Business Users Will Jump Start Business Growth

Predictive Analytics is no longer limited to data scientists. Today, predictive analytics is, and must be, accessible to business users, if your enterprise is to grow and respond to the need for data democratization and increased productivity within the enterprise and to the rapid changes in the market, competition, resource and supplier needs and customer buying behavior. Every business user must have the tools to analyze data and make accurate, timely predictions and decisions.

When an organization is considering an investment or upgrade to support advanced analytics, it must offset the investment and implementation with ample benefit and advantages. The benefits of augmented analytics and, specifically, of predictive analytics and assisted predictive modeling, are numerous, so there are plenty of reasons to embrace this approach and plenty of advantages of advanced analytics.

With a self-serve analytical environment, business users can leverage predictive analytics features to plan, forecast and predict outcomes, and to anticipate the organizational needs for new locations, price changes, new products and customer segments and needs – all without the assistance of a data scientist or IT staff resource.

Benefits of Advanced Analytics and Predictive Analytics:

  • Sophisticated analytical techniques in an easy-to-use interface with no need for complex algorithms or data manipulation
  • Easy-to-use auto-recommendations for algorithms so users can explore underlying data without advanced knowledge
  • No advanced data science skills required
  • Business users can analyze, share and optimize business potential
  • Business users can prototype and hypothesize without professional assistance

These are just some of the benefits of providing predictive analytics to your business users. Advanced analytics benefits go well beyond ease-of-use for business users and extend into the organization to relieve data scientists and IT of the day-to-day responsibility of producing reports and statistics to support forecasts and planning. These benefits also improve productivity among senior executives and those whose responsibilities include strategic planning, operational planning and tactical planning.

In short, every part of the organization can benefit from advanced analytics advantages. A self-serve, data democratization approach allows the enterprise to leverage predictive analytics. You can learn more about the numerous algorithms and analytical techniques available for business users to leverage. The use of these algorithms and techniques is suggested, based on the best fit for the data to be analyzed.

There are more than twenty algorithms and analytical techniques available for business users to leverage. The use of these algorithms and techniques is suggested, based on the best fit for the data to be analyzed.

But, your users do not need to know and understand these predictive analytics techniques, because they will enjoy the convenience and ease of machine learning based auto-suggestions and recommendations for analysis based on data type and other factors. That is a true benefit!

Your organization can truly benefit from predictive analytics and from the ease-of-use and sophistication of these self-serve tools. Don’t get left behind with static tools that are little better than guesswork or tools that provide only a small piece of the entire picture. Plan, forecast and predict with confidence!

Original Post: Predictive Analytics for Business Users Will Jump Start Business Growth

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ElegantJ BI and Smarten Advanced Analytics at The Vibrant Gujarat StartUp & Technology Summit, Oct 11-13, 2018 https://www.smarten.com/blog/elegantjbi-and-smarten-advanced-analytics-at-the-vibrant-gujarat-startup-technology-summit-oct-11-13-2018/ Thu, 04 Oct 2018 14:08:11 +0000 https://www.smarten.com/blog/?p=6007 Continued]]> ElegantJ BI and Smarten Advanced Analytics at The Vibrant Gujarat StartUp & Technology Summit, Oct 11-13, 2018

ElegantJ BI announces its participation in The Vibrant Gujarat StartUp & Technology Summit 2018, October 11 through October 13 at the Helipad Exhibition Centre in Gandhinagar, Gujarat, India. Clients, partners and technology innovators are invited to visit Stall No 36, in Hall #1 to experience the Smarten approach to advanced analytics.

Kartik Patel, CEO of ElegantJ BI says, “The Smarten advanced analytics tools allow business users to leverage augmented analytics that provide machine learning-based guides and suggestions to help the business user more quickly and effectively produce clear advanced analytics. Smarten requires little to no training, thereby transforming the average business user into a Citizen Data Scientist.”

The Vibrant Gujarat Technology Summit will offer theme-based pavilions and innovative zones with exhibitions and demonstrations to highlight disruptive technologies and explore the exciting transformations in technology today. This summit provides the perfect backdrop to showcase and demonstrate Smarten Predictive Analytics Tools, Self-Serve Data Preparation for Business Users, Smart Data Visualization and its Natural Language Processing (NLP) Search Analytics features.

“The Smarten roadmap to advanced analytics includes Clickless Analytics,” says Patel. “Smarten features Natural Language Processing, and the product roadmap also includes Auto Insights, to free business users and reduce the time and skills required to produce accurate, clear results, quickly and dependably, using machine learning to collect and analyze data with the guided assistance of a ‘smart’ solution.”

Join the ElegantJ BI Team at the Vibrant Gujarat StartUp & Technology Summit, October 11 through October 13 at the Helipad Exhibition Centre in Gandhinagar, Gujarat, India, Stall #36, Hall #1 and experience the exciting evolution of business user-friendly, self-serve advanced data discovery, suitable for every member of the organization.

Read More: ElegantJ BI and Smarten Advanced Analytics at The Vibrant Gujarat StartUp & Technology Summit, Oct 11-13, 2018

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Augmented Analytics Benefits are Numerous! https://www.smarten.com/blog/the-benefits-of-augmented-analytics/ Wed, 03 Oct 2018 10:42:45 +0000 https://www.smarten.com/blog/?p=6003 Continued]]> The Benefits of Augmented Analytics!

Is Augmented Analytics Easy Enough for the Average Business User?

Augmented Analytics benefits a business, its users and its customers, partners and stakeholders. The advantages of augmented analytics are as numerous, varied and unique as the organization itself but, no matter the industry or type of business, one of the greatest benefits of augmented analytics is the availability and access to sophisticated analytical techniques, algorithms and processes for the average business user – WITHOUT training or skills in data science or analysis.

Each team member in your business has a particular role and skill set and you want to leverage those and optimize the positive effects of these resources. You don’t have the time, the budget or the inclination to make every business user a data scientist. You just want them to do their job to the best of their ability and that is what they want as well.

The Advantages of Advanced Analytics are well known. Analysis of available data can help your business to solve problems, identify market and competitive opportunities, establish new price points, introduce new products, understand trends and patterns, anticipate changes in customer buying behavior, identify new business locations and plan for business investment (just to name a few areas of business focus).

With augmented analytics, business users can employ computational linguistics, analytical algorithms and data mining in a self-serve environment with easy-to-use natural language search capability for swift, accurate data analysis to support data democratization and enhance the value of every team member.

Augmented Analytics performs the analysis and suggests relationships with insight into previously hidden data so business users can explore and ‘discover’ crucial business results, and improve productivity and smart decision making across the organization. The system requires no specialized skills or guesswork. Results are clear and decisions are dependable and accurate.

If your business would like to explore the Benefits of Augmented Analytics, Contact Us today.

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Advanced Analytics and Natural Language Processing! https://www.smarten.com/blog/natural-language-processing-for-the-common-good/ Fri, 28 Sep 2018 11:26:17 +0000 https://www.smarten.com/blog/?p=5998 Continued]]> Natural Language Processing for the Common Good!

Search Analytics is Not Rocket Science Anymore!

No business, large or small, can afford to employ the services of dozens of data scientists or professional analysts. Budgets are tight and there are so many places we need to spend our money to help the business success and grow.

Business users and executives are used to the tools they see every day when they search the internet and it would be great to give them the same type of tools so they could search for and analyze data within the data structures and repositories of their business solutions and systems. This kind of search technology is called ‘natural language processing’ or NLP and it has revolutionized and greatly simplified queries and searches on Google and other sites.

Today’s Search Analytics need not be difficult and business users no longer need to wait for a data scientist or other skilled professional to gain insight into problem resolution, to identify trends or patterns or to get and share information and analysis across the enterprise. Instead of clicking through endless columns and screens and trying to complete advanced analytics with frustrating results, business users can leverage sophisticated analytical techniques using clickless analytics.

What is Clickless Analytics? Clickless Analytics provides seamless augmented analytics and advanced data discovery, using machine learning and natural language processing (NLP), in a self-serve environment that is easy enough for every business user resulting in increased user adoption, improved data democratization, and return on investment (ROI). Clickless Analytics democratizes advanced analytics so business users can enjoy the benefits of smart data discovery using natural language searches. Simply enter the query in natural language and let the system do the rest.

So, users might ask, ‘which sales person sold the most red t-shirts in 2017 in the Southwest region?’, and they can get an answer returned using natural language. So, there is no need to scroll through menus and navigation.

Contact Us to find out how Natural Language Processing and Clickless Analytics can simplify advanced analytics and help your business users become more of a business asset and achieve more dependable results.

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Predictive Analysis Isn’t Just for Data Scientists! https://www.smarten.com/blog/predictive-analytics-for-business-users-can-help-you-plan/ Wed, 26 Sep 2018 10:18:29 +0000 https://www.smarten.com/blog/?p=5994 Continued]]> Predictive Analytics for Business Users Can Help You Plan!

How Can My Business Use Assisted Predictive Modeling to Optimize Resources?

There was a time, not so long ago, when predictive analysis, business forecasting and planning for results involved guesswork and lots of unscientific review of historical data. But, today’s competitive business landscape and rapidly moving markets demand more than guesswork.

As businesses attempt to keep pace, it has become clear that data scientists and other analytical professionals are an expensive and overworked resource in the effort to accurately predict and forecast results. If you are fortunate enough to have such a staff, these professionals are probably up to their ears in critical strategic analysis.

If your business is to thrive, you must optimize all resources and knowledge at every level within the organization. Those with industry, functional or business skills must have predictive analytics for business users so that they can combine their divisional, department and group knowledge with predictive analysis tools. By providing Assisted Predictive Modeling tools to business users, the organization can allow for day-to-day planning, problem solving, and testing of hypotheses and theories to avoid missteps and allow for the sharing of data and analysis across the enterprise.

With the sophisticated, easy-to-use Predictive Analytics Tools on the market today, there is no reason that business users should not be part of the results equation! Every team member can use apply predictive analytics to any use case using forecasting, regression, clustering and other methods to analyze an infinite number of use cases including customer churn, and planning for and target customers for acquisition, identify cross-sales opportunities, optimize pricing and promotional targets and analyze and predict customer preferences and buying behaviors.

Contact Us if you want to find out how Assisted Predictive Modeling can help your business succeed.

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Self-Service Data Prep for Every Business User! https://www.smarten.com/blog/is-self-serve-data-prep-easy-enough-for-all-users/ Mon, 24 Sep 2018 11:29:26 +0000 https://www.smarten.com/blog/?p=5990 Continued]]> Is Self-Serve Data Prep Easy Enough for All Users?

Augmented Data Preparation for Business Users Offers Many Benefits!

If your enterprise is entangled in complex data preparation and manipulation, and you want to simplify and expand the use of data preparation to leverage data integration and self-service data prep, you need to explore the potential of augmented data preparation. Data extraction, transformation and loading (ETL) can be a complex, time-consuming process, but self-serve data prep is ETL for business users.

Smart data discovery tools and augmented data preparation allows business users with average skills to perform data preparation activities and to transform, shape, reduce, combine, explore, clean, sample and aggregate data, without the need for SQL skills, ETL or other programming language.

Self-Serve Data Preparation allows every business user to prepare data for analytics using tools that enable data extraction transformation and loading and quickly move data into the analytics system without waiting for assistance from IT or data scientists.

This simplified data preparation for analytics ensures clear, meaningful access to sophisticated, intuitive tools to compile and prepare data for use in analytics so business users can test hypotheses, visualize data and create and share reports with other users.

Sophisticated, yet easy-to-use machine learning capability provides guidance to determine the best techniques and the best fit transformations for the data business users want to analyze, allowing for better understanding of data.

Contact Us now to find out how Augmented Data Preparation and Self-Service advanced analytics tools can help your business succeed.

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Augmented Data Discovery is Easy Enough for Every User! https://www.smarten.com/blog/advanced-data-discovery-can-benefit-every-team-member/ Mon, 17 Sep 2018 14:00:46 +0000 https://www.smarten.com/blog/?p=5985 Continued]]> Advanced Data Discovery Can Benefit Every Team Member

Is Advanced Analytics Right Easy Enough for My Business Users?

The benefits of advanced data discovery do not have to be limited to data scientists or IT staff. If you choose the right data discovery tool, your business users can enjoy the benefits of confident business decisions, shared analytics and a common approach to, and understanding of, data-driven, fact-based metrics and results.

The key to data democratization and data literacy is augmented analytics. When an advanced analytics tool is designed for business users, it allows every team member to leverage advanced analytics and helps the organization to create Citizen Data Scientists.

Advanced Data Discovery allows business users to quickly and easily prepare and analyze data and to visualize and explore data, notate and highlight data and share data with others. Business users can use Advanced Data Discovery to identify the important ‘nuggets’, buried in traditional data, and to connect the dots, find exceptions, identify patterns and trends and better predict results. The right smart data discovery tool is designed for business users with average skills can do all of this without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

An Augmented Data Discovery solution supports the business user with tools that automates data insight by utilizing machine learning and natural language to automate data preparation and enable data sharing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately.

If you want to explore the benefits of Advanced Data Discovery, Contact Us today.

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Assisted Predictive Modeling for Simple Business Analytics! https://www.smarten.com/blog/predictive-analytics-for-business-users/ Fri, 14 Sep 2018 10:48:57 +0000 https://www.smarten.com/blog/?p=5968 Continued]]> Predictive Analytics for Business Users

No Guesswork! Just Simple, Assisted Predictive Modeling for Every Business User!

No matter the market or type of business, there is no room in today’s business landscape for guesswork. You can’t get a business loan, join with a business partner, successfully bid on a project, open a new location, hire the right employees or plan for the future without predictive analytics.

The right Predictive Analytics Tool will allow every team member to be a real asset to the organization by allowing them to analyze, monitor and share results and forecast and predict everything from new product success to pricing changes, customer buying behavior, sales and investment and risk results and market opportunities.

And, with Assisted Predictive Modeling, you can make these tasks even easier. Predictive analytics for business users should allow the average business user to capitalize on sophisticated tools and get recommendations and auto-suggestions. Predictive analytics for business users leverages machine learning and assisted predictive modeling to help users achieve the best fit and ensure that they use the most appropriate algorithm for the data they wish to analyze.

With these tools, users can explore patterns in data and receive suggestions to help them gain insight on their own without dependence on IT or data scientists. The enterprise can provide the tools needed at every level of the organization with tools and data science for business users that are sophisticated in functionality and easy-to-use for users at every skill level.

So…no guesswork, and no need for advanced skills or assistance from IT or data scientists! Contact Us to find out how Assisted Predictive Modeling can help your business succeed.

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GDP and Cement sales – Correlation Analysis https://www.smarten.com/blog/gdp-and-cement-sales-correlation-analysis/ Thu, 13 Sep 2018 11:41:27 +0000 https://www.smarten.com/blog/?p=5965 Continued]]>

This short video demonstrates how macroeconomic data for different states like GDP, Population or rainfall can be connected to sales of cement. This demonstration can be used to visualize how you can use a correlation with your data and macroeconomic data for your planning.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Augmented Analytics : Smarten https://www.smarten.com/blog/smarten-augmented-analytics-video/ Thu, 13 Sep 2018 11:33:06 +0000 https://www.smarten.com/blog/?p=5962

Smarten Augmented Analytics supports Citizen Data Scientists with NLP, Self-Serve Data Prep, Smart Data Visualization, Assisted Predictive Modeling and more!

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Smart Visualization | Smarten https://www.smarten.com/blog/smarten-smart-visualization/ Thu, 13 Sep 2018 11:24:18 +0000 https://www.smarten.com/blog/?p=5959 Continued]]>

Effectively visualization needs an understanding of large amount of data and the best graphical method to display this. As the data sizes get from large to enormous and more, one needs technology to support one in taking this decision. Smarten’s Smart Visualization module helps your imagination by interpreting the data and suggesting the right graphical representation. This video demonstrates the entire process.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Assisted Predictive Modeling | Smarten https://www.smarten.com/blog/smarten-assisted-predictive-modeling/ Thu, 13 Sep 2018 11:13:53 +0000 https://www.smarten.com/blog/?p=5956 Continued]]>

Assisted Predictive Modeling enables business users to take the role of Data Scientist with great ease. This video discusses the working of Assisted Predictive Modeling in Smarten Augmented Analytics and is followed by a demonstration for actual business cases. This video is intended to allow business users and data analysts to discover the ease and possibilities of Assisted Predictive Modeling.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Self-Serve Data Preparation | Google Analytics | Smarten https://www.smarten.com/blog/smarten-self-serve-data-preparation-google-analytics/ Thu, 13 Sep 2018 10:47:08 +0000 https://www.smarten.com/blog/?p=5953 Continued]]>

Self-Serve Data Preparation simplifies preparation of data from multiple sources for analytics. This module of Smarten Augmented Analytics is the engine which enables end users as well as data experts to easily extract, transform, merge and select data for effective data analysis. The resultant data gets loaded into Smarten for effective analytics. This video demonstrates the use of Self-Serve Data Preparation (SSDP) using data from Google Analytics. The connector connects to Google Analytics, takes the authentication details, extracts and loads the data into Smarten for instant analysis with defined refresh intervals.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Self-Serve Data Preparation | RDBMS | Smarten https://www.smarten.com/blog/smarten-self-serve-data-preparation-rdbms/ Thu, 13 Sep 2018 10:35:29 +0000 https://www.smarten.com/blog/?p=5950 Continued]]>

Self-Serve Data Preparation simplifies preparation of data from multiple sources for analytics. This module of Smarten Augmented Analytics is the engine which enables end users as well as data experts to easily extract, transform, merge and select data for effective data analysis. The resultant data gets loaded into Smarten for effective analytics. This video demonstrates the use of Self Serve Data Preparation (SSDP) when one has the data in an RDBMS. This demonstration uses data from MS SQL Server.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Self-Serve Data Preparation | CSV | Smarten https://www.smarten.com/blog/smarten-self-serve-data-preparation-csv/ Thu, 13 Sep 2018 10:23:42 +0000 https://www.smarten.com/blog/?p=5947 Continued]]>

Self-Serve Data Preparation simplifies preparation of data from multiple sources for analytics. This module of Smarten Augmented Analytics is the engine which enables end users as well as data experts to easily extract, transform, merge and select data for effective data analysis. The resultant data gets loaded into Smarten for effective analytics. This video demonstrates the use of Self Serve Data Preparation (SSDP) when one has the data in CSV files.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Augmented Analytics | Putting it together | Smarten https://www.smarten.com/blog/smarten-augmented-analytics-putting-it-together/ Thu, 13 Sep 2018 10:04:17 +0000 https://www.smarten.com/blog/?p=5944 Continued]]>

The key components of Smarten, Self-Serve Data Preparation, Assisted Predictive Analytics and Smart Visualization all come together offering immense possibilities for business users and data analyst. This video gives an overview of what was built and how it can come together.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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What is Search Analytics and Can it Improve Self-Serve Data Discovery? (Part 2 of 3 articles) https://www.smarten.com/blog/what-is-search-analytics-and-can-it-improve-self-serve-data-discovery/ Wed, 12 Sep 2018 08:45:45 +0000 https://www.smarten.com/blog/?p=5940 Continued]]> What is Search Analytics and Can it Improve Self-Serve Data Discovery? (Part 2 of 3 articles)

Search Analytics or search-based analytics marks the advent of a new era of business intelligence, in that it allows business users to ask a question using natural language and that question is translated by the system to produce results.

In Part II of our three-article series we discuss search analytics and how it can improve self-serve data discovery.

What is Search-Based Analytics?

One of the greatest obstacles to self-serve business intelligence was the need for a specialized skill set to use the solution. As self-serve business intelligence grew in popularity, vendors developed tools that could provide sophisticated features in a user-friendly environment so business users could leverage these tools to perform analysis and produce reports.

Search analytics takes this approach to the next level by offering an interactive environment wherein business users can obtain rapid, accurate results. These tools use natural language processing (NLP) to simplify the input and output so that users can ask questions and receive answers without programming or analytical knowledge, thereby enhancing user adoption and the clarity and usefulness of the analysis and reports the enterprise produces.

Search analytics and NLP allows for a conversational approach to business intelligence and Augmented Analytics solutions. Rather than the user scrolling through menus and navigation or using drag and drop, the user can enter a search query in natural language. The system will translate that search analytics language query into a query that the analytics platform can interpret, and return the most appropriate answer in an appropriate form such as visualization, tables, numbers or descriptions in simple human language.

Does Search-Based Analytics Improve Self-Serve Data Discovery?

The natural language processing (NLP) approach to Search Analytics allows users to process question in natural language, and presents relevant, easy-to-understand visualization reports, numbers, trends and key performance indicators (KPIs) to answer questions. The old, structured approach is gone; replaced by an expanded data environment where users can get information in a way that is meaningful to them and easy to interpret.

The enterprise can integrate business intelligence analytics with any internal and external data sources to create a foundation for fact-based, data-driven analysis that is easily accessible to business users and supports self-serve advanced data discovery. Search analytics produces clear results, and data is available in an intelligent adaptive user interface and users can access these tools from any desktop, tablet or mobile device, so users will WANT to use the solution.

Search Analytics can help your business to achieve rapid ROI and sustain low total cost of ownership (TCO) with meaningful tools that are easy to understand, and as familiar as a Google search. These tools require very little training, and provide interactive tools that ‘speak the language’ of the user.

Search analytics interprets natural language queries and presents results through smart visualization and contextual information delivered in natural language so every business users can capitalize on these tools, no matter their skill level or their analytical need.

By leveraging Natural Language Processing (NLP), Search Analytics allows for a true self-serve business intelligence environment in which data democratization can take place and wherein the enterprise can encourage the transformation of business users into Citizen Data Scientists.

For more information on this topic, read ‘What is Clickless Analysis? Can it Simplify Adoption of Augmented Analytics? (Part 1 of 3 articles)‘ and watch for the next article in the series, ‘What is Natural Language Processing & How Does it Benefit a Business? (Part 3 of 3 articles)’

Original Post: What is Search Analytics and Can it Improve Self-Serve Data Discovery? (Part 2 of 3 articles)

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Will My Business Users Embrace Augmented Analytics? https://www.smarten.com/blog/can-advanced-analytics-create-citizen-data-scientists/ Tue, 11 Sep 2018 07:50:45 +0000 https://www.smarten.com/blog/?p=5935 Continued]]> Can Advanced Analytics Create Citizen Data Scientists?

Advanced Analytics and Smart Data Discovery for ALL!

Don’t think that advanced analytics is too complicated for your business users. Nothing could be further from the truth! You can create Citizen Data Scientists and improve the analytical abilities and outcomes for your average business user if you give them the right tools.

What you need is Advanced Analytics Software that is designed with sophisticated analytical techniques and methods and also designed for business users, with simple navigation and tools that every user can leverage. Augmented analytics is a method of smart data discovery that guides users through the data discovery phase and allows every user to find, analyze, display and share data with others, all without the assistance of IT or a data scientist.

With the right Advanced Analytics Tools, your business users will adopt and use these tools with confidence and they can enjoy assisted predictive modeling, self-serve data preparation, smart data visualization and more!

Advance data discovery within your organization with great ROI and low TCO and a swift implementation. These tools are mobile, and will work seamlessly on any type or any size device.

Contact Us to find out how your business users and your organization can benefit from these easy-to-use Augmented Analytics.

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Augmented Analytics | Introduction | Smarten https://www.smarten.com/blog/smarten-augmented-analytics-introduction/ Fri, 07 Sep 2018 12:22:34 +0000 https://www.smarten.com/blog/?p=5923 Continued]]>

This video is a presentation and discussion on Smarten. The key components of Smarten, Self-Serve Data Preparation, Assisted Predictive Modeling and Smart Visualization are discussed in the context of applications and possibilities of putting it to effective use.

You can find other educational resources by browsing our Augmented Analytics Videos and Augmented Analytics Learning pages.

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Natural Language Processing for Advanced Analytics! https://www.smarten.com/blog/2018/08/29/august-29-2018/natural-language-processing/ Wed, 29 Aug 2018 06:43:34 +0000 https://www.smarten.com/blog/?p=5919 Continued]]> NLP and Advanced Analytics Go Hand-in-Hand

Does Natural Language Processing Work with Advanced Analytics?

Natural Language Processing (NLP) is a trend that has taken over technology. Vendors are applying this technique to all types of searching and Advanced Analytics is no exception.

Nearly every enterprise is working on data democratization and wants to transform its business users into Citizen Data Scientists with easy-to-use tools that offer sophisticated analytical techniques and simple access and analysis – no matter the skill level of the user. Imagine what you could do with advanced data discovery that allows users to use normal, natural language to ask questions and make queries and to get results in the same way, without advanced skills or knowledge of technical language or statistical terminology or techniques.

You really don’t have to imagine, because you CAN do that…right now!

Natural Language Processing fuels data democratization, and gives business users to tools to perform searches by asking a question, so they don’t have to use menus or drag and drop, they can just type in a natural query search much as they are used to doing on Google. For example, a user might ask ‘what is best selling product in Arizona’, or ‘show me the best performing product category compared to last year for all states’.

With natural language processing Search Analytics, users can enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, e.g., visualization, tables, numbers or descriptors.

With a seamless augmented analytics solution, and advanced data discovery, using machine learning and natural language processing the enterprise gets a solution that is easy enough for every business user resulting in increased user adoption, improved data democratization, and return on investment (ROI).

If you want to explore the opportunities provided by Advanced Analytics with NLP, you can find answers here: Smarten with NLP

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Establish a Solid Foundation for Advanced Analytics https://www.smarten.com/blog/establish-solid-foundation-for-advanced-analytics/ Fri, 17 Aug 2018 12:27:56 +0000 https://www.smarten.com/blog/?p=5912 Continued]]> Establish a Solid Foundation for Advanced Analytics

As advanced analytics and self-serve, augmented analytical tools make their way into the average enterprise, the average organization struggles to quantify the effects and, moreover, to understand and leverage the changes within the business.

Every company is different and the impact of these types of tools will be unique in some ways, but there are many ways in which the introduction of advanced analytics to business users will typically impact your business. It is always wise to establish metrics and to focus on major changes within the organization to be sure you can measure success and capitalize on new tools in as many ways as possible.

But, how do you begin to understand the implications of these changes? Here are a few ideas that will help you understand how augmented analytics can and does affect business results and the culture of the organization.

Do Your Homework: Use every opportunity at industry association conferences and among colleagues to explore how other businesses are researching and measuring their own results. Learn from those who are ahead of you on the curve to save time and frustration in your own research. Read articles on the subject to get ideas and apply the appropriate techniques to your own organization. There isn’t much out there yet in terms of conference topics or expert training. But, the idea is gaining ground as markets and organizations adjust to the idea that data analytics and self-serve access can benefit and change data literacy, as well as the approach to and foundation of business decisions, and the collaborative power and application of analytics within the business structure.

Focus on Culture: When you make major changes within your organization, there is always fallout. Changing access to data, and allowing business users to become more familiar with analytics and more knowledgeable about how to use them in everyday decisions will most certainly change the dynamic within your organization. More education, more knowledge, more access means will encourage balance and involvement. Trying to manage a team or organization, and the flow of information will definitely change in the brave, new world of self-serve analytics. Team members with more knowledge and skill will understandably expect to have more input on decisions and will be equipped with more information. That means they are likely to express more solid, well-conceived opinions everything from objectives to the tasks and skills required to achieve goals. Data sharing will also improve team collaboration and will engender data popularity and reveal power users and those with creative ideas about how to use data. The biggest mistake an enterprise can make is to try to manage in the same old way after introducing new tools and increased data access. An organization that has the vision to implement self-serve, augmented analytics must also have the vision to recognize the impact on the culture and to support team members as they explore these tools and evolve into Citizen Data Scientists. To ignore this transition will frustrate users, restrict the evolution of the corporate culture and impede business progress.

Look to the Data: Explore user adoption, data-driven decisions and the affects of integrated, uniform data access, and data governance. Use the data pool, user base and information about data access and reporting to measure usage, user satisfaction, and the impact on the culture and data integrity and literacy. Survey IT, data analysts and business managers about the optimization of time and resources and use that data to support further expansion of, and investment in, self-serve advanced analytical tools.

Expand Your Vision: Once you and your team have a better understanding of the impact of these tools, and your new data-driven, collaborative culture is on solid ground, you can explore the expansion of these tools to partner networks, suppliers, and other stakeholders.

Data is power and this power is no longer restricted to IT, data scientists or those with advanced skills. New, improved, self-serve, augmented analytics provide guidance and recommendations at every turn so that business users can function with confidence and optimize their own skills and knowledge to add value to the organization. As this industry changes and grows, businesses will gain a more comprehensive understanding of how the tools affect collaboration, business success and the corporate culture. Don’t wait for the formal research. Get started on your own now. If you can focus on the inherent changes and capitalize on the benefits to support the culture and work process changes as they evolve, you will be in a better position to compete in your market and to retain and optimize your human resources and competitive advantage.

Original Post: Establish a Solid Foundation for Advanced Analytics

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Augmented Analytics for Every Business and Industry! https://www.smarten.com/blog/can-self-serve-data-prep-really-work-for-business-users/ Tue, 07 Aug 2018 14:21:03 +0000 https://www.smarten.com/blog/?p=5906 Continued]]> Can Self-Serve Data Prep Really Work for Business Users?

Is An Augmented Analytics Solution Right for My Organization?

Self-Serve Data Preparation is a critical component of augmented analytics. If these terms seem foreign to you, just know that they represent the future of business analysis. As organizations adopt self-serve business analysis, the business user with average technology skills must be able to leverage tools that are sophisticated, yet easy to use.

If you want your business users to adopt advanced data discovery tools, you must give them a solution that provides smart data discovery options with auto-suggestions and recommendations that make it easier for a user to find, select, analyze and display data in an appropriate, clear manner in order to support business decisions.

A solution with Augmented Data Preparation features allows business users with average skills to perform data preparation and test theories and hypotheses through prototyping, without the assistance of business analysts, data scientists or programmers. With easy-to-use tools for data extraction, transformation and loading your organization can provide ETL for business users and take the mystery out of data integration and analysis.

Give your users data in a way that is meaningful to them and their role and puts the power in their hands so they can test hypotheses, visualize data and create and share reports with other users. Let your users mash up, manage and monitor data, share data and customize alerts. Self-Serve Data Preparation provide smart suggestions and auto-suggested relationships, JOINS, hierarchies, type casts and other suggestions so users do not need to know or understand data science. Instead, they can use sophisticated tools and algorithms for clustering, binning and regression and pattern and trend definition to get the results they need, quickly and easily.

Contact Us to find out how Self Service Data Preparation Tools can transforms business users into Citizen Data Scientists.

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Advanced Analytics Software for Every Team Member! https://www.smarten.com/blog/are-augmented-analytics-tools-too-difficult-for-my-business-users/ Fri, 03 Aug 2018 14:31:34 +0000 https://www.smarten.com/blog/?p=5902 Continued]]> Are Augmented Analytics Tools Too Difficult for My Business Users?

Can Advanced Data Discovery Help Your Business Achieve Data Democratization?

Terms like Advanced Data Discovery and Augmented Analytics can seem mysterious and daunting for the average organization. Managers, executives and IT staff may believe that business users cannot and will not adopt advanced analytics tools because these tools can only be used by data scientists, programmers or business analysts.

Nothing can be further from the truth! Today’s Advanced Analytics Software options are numerous and flexible. The right smart data discovery solution can encourage data democratization, social BI and enthusiastic user adoption across the enterprise at every level of the organization.

The right Advanced Data Discovery allows business users to leverage sophisticated analytics in an intuitive, easy-to-use environment and transforms business users with average technology skills into Citizen Data Scientists. These data discovery tools provide clear, concise results and allow the enterprise to quickly and easily prepare and analyze data and visualize and explore that data, notate and highlight data and share data across the organization in every division and location.

Advanced data discovery is not out of reach for your team. The right Advanced Analytics Tool allows every user to perform analysis without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

Contact Us to find out how easy Smart Data Discovery can be for your business users.

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Smart Data Visualisation to Simplify Analytics! https://www.smarten.com/blog/your-team-members-can-easily-use-smart-data-visualization/ Wed, 01 Aug 2018 13:01:07 +0000 https://www.smarten.com/blog/?p=5898 Continued]]> Your Team Members Can Easily Use Smart Data Visualization

Is Smart Data Visualization and Visual Analytics Right for My Business Users?

Smart Data Visualization is a crucial component of augmented data discovery. This critical feature enables sophisticated analysis with guided visualization tools that auto-recommend displays and data views based on data type, volume, dimensions, patterns and nature of data.

Software for Data Visualization allows business users to analyze, share and present information without the assistance of data scientists, business analysts or programmers. With smart data visualisation, business users can cut through mountains of data to identify and leverage critical business factors, results, issues and opportunities that will have the most impact on business results.

Smart Visualization supports data democratization and the empowerment of business users and allows team members at every level of the organization to interact easily with data discovery tools and analytics software. Users can present and share data that tells a story and provides clear, fact-based results for better decision-making.

Visual Analytics identify relationships, patterns, and trends in the market, in the industry and in customer buying behavior. Users can explore detailed data with easy-to-use drill down and drill through capabilities, and make sense of data from integrated data sources, using a guided approach that takes the guesswork out of analytics.

Contact Us to explore the benefits of Smart Data Visualization.

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Can Plug n’ Play Predictive Analytics Be Used in Hospitals? https://www.smarten.com/blog/predictive-analytics-tools-can-help-hospitals-plan/ Mon, 30 Jul 2018 13:02:55 +0000 https://www.smarten.com/blog/?p=5894 Continued]]> Predictive Analytics Tools Can Help Hospitals Plan

How Can Predictive Analysis Tools Help My Hospital or Healthcare Organization?

Hospitals and healthcare systems are turning to predictive analytics tools to plan and forecast and understand what, when and how to support patients.

Some hospitals use predictive analysis software to gather data and analysis the incidence of readmissions for patients presenting with critical health issues. For example, if a patient comes to the emergency room after having suffered a heart attack, how likely is it that the patient will be re-admitted with related issues within a certain period of time? When looking at the associated care (medication, exercise, follow-up appointments, age, risk factors, etc), hospitals and doctors can gain insight into methods, techniques and treatment plans and which will result in the best outcome and reduce the number of people readmitted for this type of medical issue.

Hospitals can also use Predictive Analytics Software to plan for future expansion of services, resources, parking lots, facilities, and certain types of care. For example, if a hospital has experienced a large increase in emergency room visits over a period of time, it would be important to know if this surge has resulted from more traumatic injuries, fewer available services in surrounding areas, fewer supportive non-emergency healthcare facilities like clinics, etc. The types of services, seasonality, and other factors can then be accommodated with resources, scheduling, training, increased budget, or preventive service offerings.

Assisted Predictive Modeling can support business users, IT staff and other users by providing recommendations in gathering, formatting and presenting data to improve the effectiveness and clarity of resulting data and reporting.

Plug n’ Play Predictive Analysis can help medical professionals and hospitals to plan by considering historical data and forecasting everything from the volume of insurance claims to the number of cardiac patients, elective surgeries, budgetary requirements for supplies, and resource complements.

A healthcare facility can use a Predictive Analysis Tool to better control budget, schedules, growth, risk, patient outcomes and other factors that affect success and employee and patient satisfaction.

Contact Us to find out how these easy-to-use Predictive Analytics Tool can help you effectively manage your healthcare enterprise.

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What is Smart Data Visualization and Can it Make Business Users Smarter? https://www.smarten.com/blog/what-is-smart-data-visualization-and-can-it-make-business-users-smarter/ Thu, 26 Jul 2018 14:24:17 +0000 https://www.smarten.com/blog/?p=5888 Continued]]> What is Smart Data Visualization and Can it Make Business Users Smarter?

Smart Data Visualization can radically improve your business intelligence, data discovery and analytics. It can streamline the work process of business users, improve the accuracy of planning and forecasting and ensure better, more timely, more accurate business decisions.

What is Smart Data Visualization?

Smart Visualization tools allow users to gather various data components and tell a story. Revealing results in this manner makes it easier for business users and the organization to identify the cause of a problem, see trends and patterns and find those elusive nuggets of information that will provide a competitive edge. The story is in there somewhere and smart visualization allows users to find and highlight that story!

Users can build and illustrate a compelling ‘story’ using disparate data sources with interactive, visual exploration that provides automated recommendations, suggested visualizations types and other guides to help the user produce an outcome that is clear and will support decisions and problem resolution.

A great Smart Visualization tool allows business users to work in a self-serve environment to interact with their analytics software and build that story through guided visualization and recommended data presentation. These auto-recommendations guide the user to use formats and tools that will best illustrate the underlying data and issues. Business users can use the solution to quickly produce the best possible visualization of underlying data based on data type, volume, dimensions, patterns and nature of data.

The old data visualization techniques provided options for graphs and charts but were not truly interactive so it was harder for users to analyze and understand data and it was more difficult to find the right method to display and report data so that the user or team could find problems and solutions. Smart Visualization allows users to select and display data in a way that assures accurate without the need for technical skill or lengthy, expensive training.

Can Smart Data Visualization Make Business Users Smarter?

Your business users are already smart. They use their skill and knowledge to pursue activities and tasks and make decisions appropriate to their role. What they often lack is a complete picture. There is so much data in the average organization but it isn’t always accessible or clear so the team has to make decisions based on opinion, guesswork or ‘what we have always done’. Smart Visualization can make your business users smarter by giving them the right information at the right time in the right way.

Gartner published a reported, dated September 12, 2017 (ID G00331857), entitled ‘Technology Insight for Modern Analytics and Business Intelligence Platforms’, in which it predicts that, ‘By 2020, smart/augmented, nonrelational, search- and visual-based data discovery capabilities will converge into a single set of next-generation data discovery capabilities as components of modern BI and analytics platforms.’

As these tools evolve, there will be more opportunities for creative analysis, increased data popularity and an improved business user understanding of analytics and the use of varied techniques, thereby ensuring that every business user can optimize knowledge and perform as a critical asset to the organization.

Smart Data Visualization goes far beyond simple, traditional data displays to guide and suggest visualization options for certain data types based on the nature, dimensions and trends inherent in that data. Smart Data Visualization lets users analyze, share and present information without waiting for technical assistance. It allows users to cut through mountains of data and find the more subtle information and elusive opportunities that make a difference in business results. By providing sophisticated cutting-edge technology on the backend and an intuitive front end, users can manage the analytical and visualization process with guides to make suggestions and recommendations on how to view certain types of data and users can personalize data displays to create views that are meaningful to them.

Smart visualization provides more options for display, going beyond basic bar and pie charts and allowing users to drill down and drill through data to get to the heart of the matter. This guided approach allows for swift completion of analysis and reduces the time required for data discovery. Perhaps most importantly, these tools do all of this without IT or analyst help.

Smart Visualization uses proven analytical and visualization techniques to present the right data in the right way, so that every user can find what they need without guesswork. In so doing, it assures more accurate business decisions and more timely, appropriate strategic, operational and tactical execution.

Original Post: What is Smart Data Visualization and Can it Make Business Users Smarter?

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Data Governance and Self-Serve Data Discovery Tools! https://www.smarten.com/blog/can-self-serve-advanced-analytics-support-data-governance/ Wed, 25 Jul 2018 13:21:33 +0000 https://www.smarten.com/blog/?p=5883 Continued]]> Can Self-Serve Advanced Analytics Support Data Governance?

Data Discovery Tools and Data Governance: A Dynamite Combination!

If your organization is planning to implement advanced analytics tools or to democratize the use of data discovery tools, your IT staff and senior management are probably concerned about losing control of data access and about data security. Data governance is a real concern and it should not be minimized but there is no reason to change course and decide against data democracy just to accommodate data governance.

The good news is that Advanced Analytics, and Augmented Analytics with guided recommendations and suggestions, can satisfy average user skills and be easily implemented across the organization – WITH appropriate data governance concerns addressed in the bargain.

With the right Augmented Data Discovery Tool, the organization can ensure business user adoption and provide each user with appropriate access to data in a uniform, intuitive environment. The enterprise can offer data-driven decision support, and allow the business to cascade objectives, share data across the enterprise and improve the value of every team member by giving them the right information at the right time.

It is true that your business must reckon with the reality of open access, multiple platforms, expanding user devices, integrated data repositories and data democratization and it is important to understand the self-serve concept, and the importance of data governance.

As Smart Data Discovery and business user empowerment and accountability grows in popularity, the wise enterprise will balance the need for data governance and data security with the organizational and business user need for solid, dependable information. The business that attempts to over-enforce data governance may end up creating a data dictatorship and thereby depriving business users to critical information needed to perform daily tasks and make appropriate decisions.

If you want to embrace Advanced Data Discovery for business users AND ensure appropriate data governance, Contact Us to find out how you can achieve both with one easy-to-use, sophisticated data discovery tool.

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Advanced Analytics Software for All Users! https://www.smarten.com/blog/augmented-analytics-that-will-ensure-business-user-adoption/ Mon, 23 Jul 2018 11:45:04 +0000 https://www.smarten.com/blog/?p=5879 Continued]]> Augmented Analytics That Will Ensure Business User Adoption

Self-Serve Data Discovery Tools Must be Sophisticated Yet Easy to Use!

Self-Serve advanced analytics and data discovery software is an important competitive tool in today’s rapidly changing environment. Data resides in a lot of places within the organization and access to that data in an intuitive, integrated environment is important.

Equally important is the democratization of that data so that business users can easily access the Advanced Analytics Software and use augmented data discovery to gather data, and analyze that data in an environment where they are offered auto-suggestions and recommendations for data presentation and guides that point out relationships.

If you want your data discovery tool to succeed within your business user ranks, you must provide a solution that goes beyond data monitoring and presentation to reveal crucial insights on the ‘whys’ and ”hows’ of issues, challenges and opportunities. Users need a sophisticated Smart Data Discovery Tool that can handle the most complex queries and provide all the support a business user will need, thereby making them self-sufficient. This augmented analytics tool is the key to providing effective data discovery, producing clear, reliable results and ensuring low total cost of ownership (TCO) and rapid return on investment (ROI).

Your users will gain valuable insight and find crucial ‘nuggets’ of information that will allow you to quickly resolve issues and capitalize on market and customer opportunities.

If you want to explore the opportunities of Advanced Analytics Tools, Contact us.

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Augmented Analytics Algorithms and Techniques: Learning for Citizen Data Scientists https://www.smarten.com/blog/augmented-analytics-algorithms-and-techniques-learning-for-citizen-data-scientists/ Tue, 10 Jul 2018 12:00:06 +0000 https://www.smarten.com/blog/?p=5872 Continued]]> Augmented Analytics Algorithms and Techniques: Learning for Citizen Data Scientists

This article summarizes our recent article series on the definition, meaning and use of the various algorithms and analytical methods and techniques used in predictive analytics for business users, and in augmented data preparation and augmented data discovery tools.

The article series is designed to help business users better understand the analytical techniques so that the average user can feel more confident in adopting, embracing and sharing these tools.

This twenty-four (24) article series includes:

Naïve Bayes Classification: What is Naïve Bayes Classification and How is it Used for Enterprise Analysis?

Use Case(s): Weather Forecasting, Fraud Analysis and more.

Frequent Pattern Mining (Association): What is Frequent Pattern Mining (Association) and How Does it Support Business Analysis?

Use Case(s): Market Basket Analysis, Frequently Bundled Products and more.

KNN Classification: What is KNN Classification and How Can This Analysis Help an Enterprise?

Use Case(s): Predicting Loan Default, Predicting Success of Medical Treatment and more.

Multiple Linear Regression: What is Multiple Linear Regression and How Can it be Helpful for Business Analysis?

Use Case(s): Impact of Product Pricing, Promotion on Sales, Impact of rainfall, humidity on crop yield an more.

Independent Samples T Test: What is the Independent Samples T Test Method of Analysis and How Can it Benefit an Organization?

Use Case(s): Are men more satisfied with their jobs than women? Does customer group A spend more on products than customer group B, and more.

Simple Random Sampling and Stratified Random Sampling: What Are Simple Random Sampling and Stratified Random Sampling Analytical Techniques?

Use Case(s): Average value of all cars in U.S. based on sample, sampling by age, gender, religion, race, educational attainment, socioeconomic status, and nationality and more.

Spearman’s Rank Correlation: What is Spearman’s Rank Correlation and How is it Useful for Business Analysis?

Use Case(s): Cluster various survey responders into groups, based on rank correlation, assess student rating by department chairs and by the faculty members and more.

Binary Logistic Regression Classification: What is Binary Logistic Regression Classification and How is it Used in Analysis?

Use Case(s): Predict if loan default based on attributes of applicant; predict likelihood of successful treatment of new patient based on patient attributes and more.

Paired Sample T Test: What is the Paired Sample T Test and How is it Beneficial to Business Analysis?

Use Case(s): Manufacturing unit manager analyzes statistical significance of cycle time difference, pre and post process change, determine whether sales increased following a particular campaign and more.

Simple Linear Regression: What is Simple Linear Regression and How Can an Enterprise Use this Technique to Analyze Data?

Use Case(s): Measure the impact of product price on product sales, measure the impact of temperature on crop yield an more.

ARIMAX Forecasting: What is ARIMAX Forecasting and How is it Used for Enterprise Analysis?

Use Case(s): Forecast product line growth based on data from the past 30 years based on yearly consumer inflation rate, yearly GDP data, target variables for user-specified time periods to clearly illustrate results for planning, production, sales and other factors and more.

Karl Pearson Correlation Analysis: What is Karl Pearson Correlation Analysis and How Can it be Used for Enterprise Analysis Needs?

Use Case(s): Correlation between income and credit card delinquency rate, identify negative, positive and neutral correlations between the age of a consumer and the color of shirt they might purchase and more.

Hierarchical Clustering: What is Hierarchical Clustering and How Can an Organization Use it to Analyze Data?

Use Case(s): Group loan applicants into high/medium/low risk based on attributes such as loan amount, installments, or employment tenure, organize customers into groups/segments based on similar traits, product preferences and expectations and more.

SVM Classification Analysis: What is SVM Classification Analysis and How Can It Benefit Business Analytics?

Use Case(s): Predict success of treatment success based on attributes of a patient, improve weather forecasting results and more.

Outlier Analysis: What is Outlier Analysis and How Can It Improve Analysis?

Use Case(s): Outliers are sometimes discounted, or in other cases, they will indicate that the organization should focus solely on those outliers; identify when a person recovered from a particular disease in spite of the fact that most other patients did not survive, and more.

Decision Tree Analysis: What is the Decision Tree Analysis and How Does it Help a Business to Analyze Data?

Use Case(s): Classify customers into those that will default and those that will not default. And assess the characteristics of customers that are likely to default, based on customer attributes and past online shopping behavioral data, one can predict the future purchases of customers and more.

Chi Square Test of Association: What is the Chi Square Test of Association and How Can it be Used for Analysis?

Use Case(s): Determine if a product sells better in certain locations, verify if gender has an influence on purchasing decisions, Identify if demographic factors influence banking channel/product/service preference or selection of a type of term insurance plan and more.

FP Growth Analysis: What is FP Growth Analysis and How Can a Business Use Frequent Pattern Mining to Analyze Data?

Use Case(s): Select items in a business catalog to complement each other so that buying one item will lead to buying another, analyze the association of purchased items in a single basket or single purchase and more.

ARIMA Forecasting: What is ARIMA Forecasting and How Can it Be Used for Enterprise Analysis?

Use Case(s): Predict sales of a drug for the next 2 months, based on drug sales from the past 12 months, suitable for forecasting when data is stationary or non-stationary, will produce accurate, dependable forecasts, when planning for short-term business results and more.

Multinomial-Logistic Regression Classification: What is the Multinomial-Logistic Regression Classification Algorithm and How Does One Use it for Analysis?

Use Case(s): Based on the attributes of a respondent e.g., demographics, marital status, gender, income, age, qualification etc., analysis can check the level of likely satisfaction with life/job/product/services, given a list of symptoms, one can predict if a patient is likely to be diagnosed with initial/intermediate/serious stages of a particular disease and more.

KMeans Clustering Algorithm: What is the KMeans Clustering Algorithm and How Does an Enterprise Use it to Analyze Data?

Use Case(s): Loan applicants grouped as low, medium, and high risk based on applicant age, annual income, employment tenure, a movie ticket booking website can group users into frequent ticket buyers, moderate ticket buyers and occasional ticket buyers, based on past movie ticket purchases, and more.

Descriptive Statistics: What is Descriptive Statistics and How Do You Choose the Right One for Enterprise Analysis?

Use Case(s): Average age and income for a particular type of product category purchased, Identify the most popular dish served in the restaurant or find out the most frequent rating given by customers for a given movie/restaurant or most frequent size or category of a sold product and more.

Holt-Winters Forecasting: What is the Holt-Winters Forecasting Algorithm and How Can it be Used for Enterprise Analysis?

Use Case(s): Forecasting number of viewers by day for a particular game show for next two months.

Input data: Last six months daily viewer count data, insurance claim manager can forecast policy sales for next month based on past 12 months data and more.

Trends and Patterns: What Are Data Trends and Patterns, and How Do They Impact Business Decisions?

Use Case(s): identify seasonality pattern when fluctuations repeat over fixed periods of time and where patterns do not extend beyond 1 year, analyze a stationary time series with statistical properties, where variances are all constant over time, or cyclical when fluctuations do not repeat over fixed periods of time, are unpredictable and extend beyond a year, and more.

Each of these techniques, methods and algorithms has a unique value in advanced analytics. Augmented Data Discovery tools allow business users to gather and analyze data using these techniques within a sophisticated, intuitive navigation that is designed to guide users through the processing of selecting the appropriate algorithm or analytical technique based on the type of data selected.

This article series will help business users understand the concepts and the benefits of each technique, as well as the logic behind the application of these techniques, and the value-added auto-recommendations and suggestions provided by comprehensive augmented analytics tools.

You can find more educational resources by browsing our Augmented Analytics Learning and Augmented Analytics Videos pages.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

Original Post: Augmented Analytics Algorithms and Techniques: Learning for Citizen Data Scientists

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Assisted Predictive Modeling https://www.smarten.com/blog/2018/07/09/july-09-2018/assisted-predictive-modeling-r-integration/ Mon, 09 Jul 2018 08:08:53 +0000 https://www.smarten.com/blog/?p=5867 Continued]]> Business Users Need Assisted Predictive Modeling

Create Citizen Data Scientists with Assisted Predictive Modeling!

If your business is looking for a comprehensive augmented advanced analytics solution, what are some of the critical factors to consider? OK, here goes!

  • You need Assisted Predictive Modeling (Plug n’ Play Predictive Analysis with auto-suggestions and recommendations)
  • You need to encourage business user transformation to create Citizen Data Scientists by implementing a self-serve data democratization environment that allows users to become a greater asset to the organization and to make more confident decisions that will produce more dependable results.
  • You need a solution that can accommodate R integration so data scientists can utilize R Script to capitalize on their expertise and leverage enterprise investments in R open source platform. This type of integration allows data scientists to perform statistical and predictive algorithms, and complex analysis to provide the depth of detail and advanced analytics and reporting the organization needs for strategic decision-making.

Assisted Predictive Modeling sounds complicated but it is designed to help business users gain insight into data without the skills of a data scientist. It offers auto-recommendations and auto-suggestions to simplify use and allow business users to leverage predictive algorithms without the expertise and skill of a data scientist. The Plug and Play Predictive Analytics and predictive modeling platform is suitable for business users. These tools allow the organization to apply predictive analytics to any use case using forecasting, regression, clustering and other methods to analyze an infinite number of use cases including customer churn, and planning for and target customers for acquisition, identify cross-sales opportunities, optimize pricing and promotional targets and analyze and predict customer preferences and buying behaviors.

If your organization also employs data scientists, you will want to accommodate their needs and allow for more advanced analysis and critical, strategic analytical initiatives by integrating advanced analytics with R scripting to provide a more comprehensive set of tools.

When an organization works to create an environment that will encourage Citizen Data Scientists and optimize the time and resources of professional data scientists, it can improve results, increase ROI and lower TCO, all while establishing a more competitive position in the market and ensuring more confidence in decisions across the organization. If that all sounds like something you want, you can start here: Assisted Predictive Modeling with R Integration

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What is Naïve Bayes Classification and How is it Used for Enterprise Analysis? https://www.smarten.com/blog/what-is-naive-bayes-classification-and-how-is-it-used-for-enterprise-analysis/ Fri, 29 Jun 2018 14:23:44 +0000 https://www.smarten.com/blog/?p=5843 Continued]]>

This article will focus on the Naïve Bayes Classification method of analysis.

What is Naïve Bayes Classification?

Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is a supervised classification technique used to classify future objects by assigning class labels to instances/records using conditional probability. In supervised classification, training data are already labeled with a class. For example, if fraudulent transactions are already flagged in transactional data and if we want to classify future transactions into fraudulent/non fraudulent, then that type of classification would be called supervised.

Let’s say we want to classify fruit. Fruit may be considered to be an apple if it is red, round, and about 3″ in diameter. If we have data on 1000 pieces of fruit including features or characteristics of each fruit, we can classify the 1000 pieces of fruit characteristics such as shape, length, color, sweet, sour, etc.

Naive Bayes Classification

When we look at the table above, we see that: 50% of the fruits are bananas, 30% are oranges, 20% are other types of fruits.

The Naive Bayes classifier assumes that every feature/predictor is independent, which is not always the case, so it is important to understand the type of data you are analyzing before choosing this, or any other, analytical technique.

In order to make the best use of the Naïve Bayes method, the training dataset should be adequate enough to represent the entire population – containing every combination of class label and attributes. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. For numerical variable, normal distribution is assumed which is a strong assumption.

How Can Naïve Bayes Be Used for Enterprise Analysis?

This technique can be useful in evaluating many applications.

  • Weather Forecasting – Based on temperature, humidity, pressure etc., an organization can predict if it will be rainy/sunny/windy tomorrow.
  • Fraud Analysis – Based on various bills submitted by an employee for reimbursement for expenditures on food, travel, etc., a business can predict the likelihood of fraud.

Use Case – 1

Business Problem: A bank loans officer wants to predict if a loan applicant will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installment, employment tenure, the number of times delinquent, annual income, debt to income ratio etc. Here the target variable would be ‘past default status’, and the predicted class would contain the values ‘yes or no’ representing ‘likely to default/unlikely to default’ class respectively.

Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and how much credit and interest rate each applicant is eligible to receive.

Use Case – 2

Business Problem: A doctor wants to predict the likelihood of successful treatment of a patient disease or condition based on various attributes of a patient such as blood pressure, hemoglobin level, blood sugar level, the name of a drug given to the patient, the type of treatment given to patient etc. Here the target variable would be ‘past cure status’ and the predicted class would contain values ‘yes or no’ meaning ‘prone to cure/ not prone to cure’ respectively.

Business Benefit: Given the health and body profile of a patient and recent treatments and drugs administered, the probability of a cure can be predicted and changes in treatment and drug recommendations can be suggested if required.

The Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Frequent Pattern Mining (Association) and How Does it Support Business Analysis? https://www.smarten.com/blog/what-is-frequent-pattern-mining-association-and-how-does-it-support-business-analysis/ Fri, 29 Jun 2018 13:53:31 +0000 https://www.smarten.com/blog/?p=5841 Continued]]>

In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’.

What is Frequent Pattern Mining?

Frequent Pattern Mining (AKA Association Rule Mining) is an analytical process that finds frequent patterns, associations, or causal structures from data sets found in various kinds of databases such as relational databases, transactional databases, and other data repositories. Given a set of transactions, this process aims to find the rules that enable us to predict the occurrence of a specific item based on the occurrence of other items in the transaction.

Let’s look at an example of Frequent Pattern Mining. First, we will want to understand the terminology used in this type of analysis. While there are numerous metrics and factors used in this technique, for this example, we will only consider two factors namely, Support and Confidence.

Support: The support of a rule x -> y (where x and y are each items/events etc.) is defined as the proportion of transactions in the data set which contain the item set x as well as y. So, Support (x -> y)= no. of transactions which contain the item set x & y / total no. of transactions.

Confidence: The confidence of a rule x -> y is defined as: Support (x -> y) / support (x). So, it is the ratio of the number of transactions that include all items in the consequent (y in this case), as well as the antecedent (x in this case) to the number of transactions that include all items in the antecedent (x in this case).

In the table below, Support (milk->bread) = 0.4 means milk and bread are purchased together occur in 40% of all transactions. Confidence (milk->bread) = 0.5 means that if there are 100 transactions containing milk then there will be 50 that will also contain bread.

Frequent Pattern Mining (Association)

How Does Frequent Pattern Mining Support Business Analysis?

This method of analysis can be useful in evaluating data for various business functions and industries.

  • Basket Data Analysis – To analyze the association of purchased items in a single basket or single purchase.
  • Cross Marketing and Selling – To work with other businesses that complement your own, not competitors. For example, vehicle dealerships and manufacturers have cross marketing campaigns with oil and gas companies for obvious reasons.
  • Catalog Design – The selection of items in a business’ catalog are often designed to complement each other, so that buying one item will lead to buying another, so these items are often complements or closely related.
  • Medical Treatments – Each patient is represented as a transaction containing the ordered set of diseases, and which diseases are likely to occur simultaneously/sequentially can be predicted.

To understand the value of this applied technique, let’s consider two business use cases.

Use Case – 1

Business Problem: A retail store manager wants to conduct Market Basket analysis to come up with a better strategy of product placement and product bundling.

Business Benefit: Based on the rules generated, the store manager can strategically place the products together or in sequence leading to growth in sales and, in turn, revenue of the store. Offers such as “Buy this and get this free” or “Buy this and get % off on this” can be designed based on the rules generated.

Use Case – 2

Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together. Each customer is represented as a transaction, containing the ordered set of products, and which products are likely to be purchased simultaneously/sequentially can then be predicted.

Business Benefit: Based on the rules generated, banking products can be cross-sold to each existing or prospective customer to drive sales and bank revenue. For example, if savings, personal loan and credit cards are frequently/sequentially bought, then a new saving account customer can be cross-sold with a personal loan and credit card.

Frequent Pattern Mining (AKA Association Rule Mining) is an analytical process that finds frequent patterns, associations, or causal structures from data sets found in various kinds of data repositories. This method of analysis can be useful in evaluating data for various business functions and industries and is useful in determining the frequent patterns in buying behavior for various products and services, and in analyzing the relationships among various data points to cross-sell and bundle products, and service offerings, and to understand target audiences.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is KNN Classification and How Can This Analysis Help an Enterprise? https://www.smarten.com/blog/what-is-knn-classification-and-how-can-this-analysis-help-an-enterprise/ Fri, 29 Jun 2018 13:19:02 +0000 https://www.smarten.com/blog/?p=5837 Continued]]>

In this article, we will discuss the KNN Classification method of analysis.

What is the KNN Classification Algorithm?

The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. It is useful for recognizing patterns and for estimating. Let’s say we want to determine the likelihood of loan default based on two predictors (age and loan type), with ‘default’ being the target.

We would first determine K = number of nearest neighbors (in terms of distance) to check for class assignment. Then calculate the distance between an instance and all the training instances. Then we would rank the instances by distance and find the nearest neighbors. In other words, we would find the shortest distance from the new instance. After that, we would gather the classes of the nearest neighbors to find the majority. This majority of class is a final predicted value of a class.

Let’s look at an example of KNN Classification, based on two attributes: Acid durability and strength. The goal is to classify a paper tissue into good/bad quality classes.

KNN Classification Example

As the majority class = Good for the three nearest neighbors (two out of three records have class = Good), predicted class of an instance = Good, i.e. quality of a paper tissue having acid durability =3 and strength =7 is good.

How Can KNN Classification Help an Enterprise?

KNN Classification analysis can be useful in evaluating many types of data.

  • Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan.
  • Weather Prediction – Based on temperature, humidity, pressure etc., an organization can predict if it will be rainy/sunny/cold.
  • Fraud Analysis – Based on various bills submitted for reimbursement by an employee for food, travel, and other expense a business can predict the likelihood of fraud.

Let’s look at two use cases to illustrate the benefit of KNN Classification.

Use Case – 1

Business Problem: A bank loans officer wants to predict if the loan applicant will be a bank defaulter or non-defaulter based on attributes such as loan amount, monthly installment, employment tenure, the number of times a payment has been delinquent, annual income, debt to income ratio etc. Here, the target variable would be ‘past default status’ and the predicted class would contain the values or ‘yes or no’ representing ‘likely to default/unlikely to default’ class respectively.

Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate for each applicant, based on the amount of risk.

Use Case – 2

Business Problem: A doctor wants to predict the likelihood of successful treatment for a new patient based on various attributes such as blood pressure, hemoglobin, blood sugar, the name of a drug given to the patient, the type of treatment given to the patient etc. Here, the target variable would be ‘past cure status’ and the predicted class would contain values ‘yes or no’ meaning ‘prone to cure/ not prone to cure’ respectively.

Business Benefit: Given the health and body profile of a patient and the recent treatments and drugs prescribed for the patient, the doctor can predict the probability and make recommendations on changes in treatment/drugs.

The KNN Classification algorithm is useful in determining probable outcome and results and in forecasting and predicting results, given the existence of multiple variables.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Multiple Linear Regression and How Can it be Helpful for Business Analysis? https://www.smarten.com/blog/what-is-multiple-linear-regression-and-how-can-it-be-helpful-for-business-analysis/ Fri, 29 Jun 2018 12:21:53 +0000 https://www.smarten.com/blog/?p=5833 Continued]]>

This article describes the analytical technique of multiple linear regression.

What is Multiple Linear Regression Analysis?

Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more variables (X, and Y). It is useful in identifying important factors (X,) that will impact a dependent variable (Y), and the nature of the relationship between each of the factors and the dependent variable.

Linear regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable.

To better understand multiple linear regression, let’s look at one such analysis of independent variables: Temperature and Humidity, and a target variable (yield).

Multiple Linear Regression

How Can Multiple Linear Regression Be Helpful for Business Analysis?

If we consider the use cases below, we can see the value of Multiple Linear Regression analysis.

Use Case – 1

Business Problem: An ecommerce company wants to measure the impact of product price, product promotions, and holiday seasonality on product sales.

Input Data: Predictor/independent variables include product price data, product promotions data such as discounts, flag representing presence/absence of seasonality. The dependent variable is product sales data.

Business Benefit: A product sales manager can discover which predictors included in the analysis will have significant impact on product sales. For the predictors with the most impact, the team can make important strategic decisions to meet product sales targets. For instance, if promotions and holiday seasons are significant factors, these factors should be given more focus when devising a marketing strategy.

Use Case – 2

Business Problem: An agriculture production firm wants to predict the impact of the amount of rainfall, humidity, and temperature on the yield of particular crop.

Input Data: Predictor/independent variables include the amount of rainfall during monsoon months, the humidity levels/measurements, and the temperature measurements. The dependent variable is crop production.

Business Benefit: An agriculture firm can understand the impact of each of these predictors on the target variable. For instance, if temperature and rainfall have a positive significant impact but humidity levels have a negative significant impact on crop yield, then crop production can be expected during high temperature and rainfall levels in conjunction with low humidity levels.

Multiple linear regression models are useful in helping an enterprise to consider the impact of multiple independent predictors and variables on a dependent variable, and can be beneficial for forecasting and predicting results.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is the Independent Samples T Test Method of Analysis and How Can it Benefit an Organization? https://www.smarten.com/blog/what-is-the-independent-samples-t-test-method-of-analysis-and-how-can-it-benefit-an-organization/ Fri, 29 Jun 2018 12:02:05 +0000 https://www.smarten.com/blog/?p=5828 Continued]]>

This article focuses on the Independent Samples T Test technique of Hypothesis testing.

What is the Independent Samples T Test Method of Hypothesis Testing?

The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples.

For example, one might use this method of analysis to determine whether the average value of a sedan type of car is significantly different from an SUV type of car. Here the hypothesis would be set as null hypothesis: SUV and Sedan car types have insignificant difference in terms of value, and the alternative hypothesis Value of SUV and Sedan: differ significantly.

Let’s look at a sample of the Independent t-test on two variables. One is a dimension containing two values and the other is a measure.

Independent Samples T Test Example

Independent Samples T Test Example

  • At 95% confidence level (5% chance of error): As p-value = 0.041 which is less than 0.05, there is a statistically significant difference between the means of two groups A and B. Mean of Group A is significantly higher than that of Group B.
  • At 98 % confidence level (2% chance of error): As p-value = 0.041 which is greater than 0.02, there is no statistically significant difference between the means of two groups A and B.

How Can the Independent Samples T Test Method Benefit an Organization?

The Independent Samples T Test Method of Hypothesis testing can be used to address various needs in various types of industries and organizations.

  • Medicine – Has the quality of life improved for patients who took drug A as opposed to patients who took drug B?
  • Sociology – Are men more satisfied with their jobs than women? Do they earn more?
  • Biology – Are foxes in one specific habitat larger than in another?
  • Economics – Is the economic growth of developing nations larger than the economic growth of the first world?
  • Marketing – Does customer segment A spend more on groceries than customer segment B?

To better understand the benefits of the Independent Sample T Test Analysis, let’s look at two use cases:

Use Case – 1

Business Problem: An HR Manager wants to find out whether male employees earn more than female employees. Here, the dependent variable would be ‘Total Annual Income’.

Business Benefit: Once the test is completed, a p-value is generated which indicates whether there is a statistical difference between the income of two groups. Based on this value, a manager can easily conclude whether the average income earned by female employees is statistically different from male employees and if the different is statistically significant they can further conclude which gender earns higher or lower salaries.

Use Case – 2

Business Problem: A Grocery store sales manager wants to know whether customer segment A spends more on groceries than customer segment B. Here, the dependent variable would be ‘Purchase Amount’.

Business Benefit: Once the test is completed, a p-value is generated which indicates whether there is a statistical difference between the purchase amounts of both segments. Based on this value, the grocery store manager can decide on its marketing strategies for better sales and increased revenue.

The independent sample t-test is a useful statistical method of hypothesis testing when an organization wants to determine whether there is a statistical difference between two categories or groups or items and, furthermore, if there is a statistical difference, whether that difference is significant.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What Are Simple Random Sampling and Stratified Random Sampling Analytical Techniques? https://www.smarten.com/blog/what-are-simple-random-sampling-and-stratified-random-sampling-analytical-techniques/ Fri, 29 Jun 2018 11:34:51 +0000 https://www.smarten.com/blog/?p=5825 Continued]]>

This article discusses the analytical technique known as Sampling and provides a brief explanation of two types of sampling analysis, and how each of these methods is applied.

What is Sampling Analysis?

Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling.

Let’s look at both techniques in a bit more detail.

Simple Random Sampling:

With this method of sampling, the selection is based on chance, and every item has an equal chance of selection. An example of simple random sampling would be a lottery system.

Example: if we want to come up with the average value of all cars in United States, it would be impractical to find every car and assign a value, and then develop an average. Instead, we might randomly select 200 cars, get a value for those cars and then find an average. The random selection of those 200 cars would be the ‘sample data of entire United States’ cars’ values (population data).

Pros and Cons of Simple Random Sampling:

Pros: Economical in nature, less time consuming.

Cons: Chance of bias, difficulty of getting a representative sample.

Stratified Random Sampling:

Here, the population data is divided into subgroups known as strata. The members in each of the subgroups have similar attributes and characteristics in terms of demographics, income, location etc. A random sample from each of these subgroups is taken in proportion to the subgroup size relative to the population size. These subsets of subgroups are then added to from a final stratified random sample. Improved statistical precision is achieved through this method due to the low variability within each subgroup, and the fact that a smaller sample size is required for this method as compared to simple random sampling. This method is used when the researcher wants to examine subgroups within a population.

Example: One might divide a sample of adults into subgroups by age groups, like 18-29, 30-39, 40-49, 50-59, and 60 and above. To stratify this sample, the researcher would then randomly select proportional amounts of people from each age group. This is an effective sampling technique for studying how a trend or issue might differ across subgroups. Some of the most common strata used in stratified random sampling include age, gender, religion, race, educational attainment, socioeconomic status, and nationality. With stratified sampling, the researcher is guaranteed that the subjects from each subgroup are included in the final sample, whereas simple random sampling does not ensure that subgroups are represented equally or proportionately within the sample.

Pros and Cons:

Pros: Economical in nature, Less time consuming, less chance of bias as compared to Simple random sampling, higher accuracy than simple random sampling.

Cons: Need to define the categorical variable by which sub groups should be created. For instance Age group, Gender, Occupation, Income, Education, Religion, Region etc.

Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population. The organization will choose either the Simple Random Sampling or the Stratified Random Sampling method, based on the type of data, the need for accuracy and representation of certain subsets and groups and other analytical requirements of the organization.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Spearman’s Rank Correlation and How is it Useful for Business Analysis? https://www.smarten.com/blog/what-is-spearmans-rank-correlation-and-how-is-it-useful-for-business-analysis/ Fri, 29 Jun 2018 10:45:59 +0000 https://www.smarten.com/blog/?p=5821 Continued]]>

This article describes the Spearman’s Rank Correlation and how it is used for enterprise analysis.

What is Spearman’s Rank Correlation?

Correlation is a statistical measure that indicates the extent to which two variables fluctuate together A positive correlation indicates the extent to which those variables increase or decrease in parallel. A negative correlation indicates the extent to which one variable increases as the other decreases. The Spearman’s Rank Correlation is a measure of correlation between two ranked (ordered) variables. This method measures the strength and direction of association between two sets of data when ranked by each of their quantities.

Let’s compute the Spearman’s Rank Correlation coefficient between two ranked variables X and Y that will illustrate a positive correlation:

Spearman's Rank Correlation Example

The closer the value is to ±1, the stronger the relationship between variables. The closer this value is to 0, the weaker the relationship/association is between both variables.

How is Spearman’s Rank Correlation Useful for Business Analysis?

Let’s look at two use cases to see the application and benefit of the Spearman’s Rank Correlation.

Use Case – 1

Business Problem: An educational organization wants to assess students’ rating, based on two different sources of observation.

Input Data: Students’ rating by department chairs and students’ rating by the faculty members.

Business Benefit: This analysis will help the organization to assess the consistency of the ratings provided by the two sources of student ranking and observation. If the ranking given by both observers is similar, the organization can put more faith in the ratings than if the observer ranking vary widely from one to the other. This will also reduce the chance of biased or unethical ranking systems.

Use Case – 2

Business Problem: A market research agency wants to cluster various survey responders into groups, based on the rank correlation output.

Input Data: Responses on brand loyalty containing values on a scale of 1 to 5, where 1 representing disloyal, 2 meaning somewhat disloyal and so on and respondent frequency of brand visits per month (here responders with visits above 10 per quarter can be ranked as “1” , between 8 to 10 as “2” and so on).

Business Benefit: If the values of brand visits and brand loyalty turn out to be positively correlated then the organization can cluster the ongoing frequently visiting customers into “brand loyal” segment and rarely visiting customers into “brand disloyal” segment. Upon identification of these disloyal customers, the organization can focus on converting ‘disloyal’ visitors to ‘loyal’ visitors.

The Spearman’s Rank Correlation is a measure of correlation between two ranked (ordered) variables. This method measures the strength and direction of association between two sets of data when ranked by each of their quantities and is useful in identifying relationships and the sensitivity of measured results to influencing factors.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Binary Logistic Regression Classification and How is it Used in Analysis? https://www.smarten.com/blog/what-is-binary-logistic-regression-classification-and-how-is-it-used-in-analysis/ Fri, 29 Jun 2018 10:13:06 +0000 https://www.smarten.com/blog/?p=5816 Continued]]>

In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business.

What is Binary Logistic Regression Classification?

Logistic regression measures the relationship between the categorical target variable and one or more independent variables. It is useful for situations in which the outcome for a target variable can have only two possible types (in other words, it is binary). Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict the target variable classes. This technique helps to identify important factors (Xi) impacting the target variable (Y) and also the nature of the relationship between each of these factors and the dependent variable.

Let’s look at an example of Binary Logistic Regression analysis, involving the potential for loan default, based on factors like age, marital status and income.

Binary Logistic Regression Example

Binary Logistic Regression Example

P value for marital status, income and existing loan is <0.05; so these variables are important factors for predicting the likely default/non default class. But the p value for Age is >0.05 which means that Age does not significantly impact the potential for loan default.

How is Binary Logistic Regression Classification Used in Analysis?

Let’s look at two use cases where Binary Logistic Regression Classification might be applied and how it would be useful to the organization.

Use Case – 1

Business Problem: A bank loans officer wants to predict if loan applicants will be a bank defaulter or non defaulter based on attributes such as loan amount, monthly installments, employment tenure, how many times has the applicant been delinquent, annual income, debt to income ratio etc. Here the target variable would be ‘past default status’ and predicted class would include values ‘yes or no’ representing ‘likely to default/unlikely to default’ class respectively.

Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default. Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate each applicant is eligible for based on the amount of risk involved.

Use Case – 2

Business Problem: A doctor wants to predict the likelihood of a successful treatment of a new patient condition based on various attributes of a patient such as blood pressure, hemoglobin level, blood sugar level, name of a drug given to patient, name of treatments given to the patient etc. Here the target variable would be ‘past cure status’ and the predicted class would contain values ‘yes or no’ meaning ‘prone to cure/not prone to cure’ respectively.

Business Benefit: Given the heath profile of a patient and the recent treatments and drugs taken by him/her, the doctor can predict the probability of a cure and identify necessary changes to treatment and drug recommendations.

Binary Logistic Regression is useful in the analysis of multiple factors influencing a negative/positive outcome, or any other classification where there are only two possible outcomes.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is the Paired Sample T Test and How is it Beneficial to Business Analysis? https://www.smarten.com/blog/what-is-the-paired-sample-t-test-and-how-is-it-beneficial-to-business-analysis/ Fri, 29 Jun 2018 09:28:11 +0000 https://www.smarten.com/blog/?p=5811 Continued]]>

This article discusses the Paired Sample T Test method of hypothesis testing and analysis.

What is the Paired Sample T Test?

The Paired Sample T Test is used to determine whether the mean of a dependent variable e.g., weight, anxiety level, salary, reaction time, etc., is the same in two related groups. For example, one might consider two groups of participants that are measured at two different “time points” or two groups that are subjected to two different “conditions”. Paired T Test is used to evaluate the before and after of a situation, treatment, condition, etc.

For example, a business might use this technique to understand whether there was a difference in manager salaries before and after undertaking a PhD program. In this case, the metrics would be, “salary”, and the two related groups would be the two different “time points”; that is, salaries “before” and “after” completion of the PhD program.

Here is another example. Let’s say that a medical organization wishes to measure the blood pressure of patient A, and then recommend a treatment plan like medication, diet or exercise to reduce his blood pressure, after which the organization would measure the blood pressure of patient A again. When this process is applied to patients B, C, and D, the results of the analysis for “Before” and “After” can be paired by patient to determine the effects and success rate of these treatments.

Paired Sample T testing can be applied only to two samples: one measure and one time dimension, or a sequence ID to decide the point where analysis will divide the measurement values into pre and post samples. The number of data points for Paired Sample T Tests should be at least 30.

Let’s conduct the Paired Sample T-Test on two variables. One is a time dimension containing months and the other is a measure.

Paired Sample T-Test Example

Let’s say that the measurement values before April belong to the ‘before’ or ‘pre’ sample and from April belong to the ‘After’ or ‘post’ sampling.

Paired Sample T-Test Example Output

  • At 95% confidence level (5% chance of error): As p-value = 0.041 which is less than 0.05, there is a statistically significant difference between means of pre and post sample values. Therefore, the treatment was effective.
  • At 98 % confidence level (2% chance of error): As p-value = 0.041 which is greater than 0.02, there is no statistically significant difference between the means of pre and post samples. Therefore, the treatment was not effective.

How is the Paired Sample T Test Beneficial to Business Analysis?

This type of analysis can be useful in numerous situations.

  • Medicine – Has the particular medicine or treatment been effective?
  • Marketing – Have sales increased following a particular campaign?
  • Manufacturing – Has the cycle time or defect instance been reduced following a particular process change.
  • Logistics – Has the transit time reduced from supplier to customer following a route change.

Let’s look at two use cases to better understand the benefit of this technique in business analysis.

Use Case – 1

Business Problem: A manufacturing unit manager want to know if there is a statistically significant difference in cycle time pre and post a particular process change. Here the dependent variable would be ‘cycle time values’.

Business Benefit: Once the test is completed, p-value is generated which indicates whether there is statistical difference between cycle time of both time points. Based on this value, a manager can easily conclude whether a particular process change has had a significant impact on cycle time.

Use Case – 2

Business Problem: A grocery store sales manager wants to know whether daily sales have increased after an advertising campaign. Here the dependent variable would be ‘Daily sales’.

Business Benefit: Once the test is completed, p-value is generated which indicates whether there is a statistical difference between the average daily sales- pre and post an advertising campaign. Based on this value, grocery store manager can get to know if the campaign has been effective.

The Paired Sample T Test used to determine whether the mean of a dependent variable and is particularly useful in measuring results before and after a particular event, action, process change, etc. Paired Sample T testing can be applied only to two samples: one measure and one time dimension.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Simple Linear Regression and How Can an Enterprise Use this Technique to Analyze Data? https://www.smarten.com/blog/what-is-simple-linear-regression-and-how-can-an-enterprise-use-this-technique-to-analyze-data/ Fri, 29 Jun 2018 08:08:50 +0000 https://www.smarten.com/blog/?p=5807 Continued]]>

This article describes the Simple Linear Regression method of analysis.

What is Simple Linear Regression?

Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). This method helps a business to identify the relationship between X and Y and the nature and direction of that relationship.

Here is a sample of simple linear regression analysis, considering the effects of temperature on crop yield:

Simple Linear Regression Example

Simple linear regression is limited to predicting numeric output i.e., dependent variable has to be numeric in nature. This method of analysis can handle only two variables, namely one predictor and one dependent variable.

How Can the Enterprise Use Simple Linear Regression to Analyze Data?

Let’s look at two use cases that illustrate the value of Simple Linear Regression.

Use Case – 1

Business Problem: An eCommerce company wants to measure the impact of product price on product sales.

Input Data: The predictor/independent variable is product price data for last year. The dependent variable is product sales data for last year.

Business Benefit: The product sales manager can identify the amount and direction of product price impact on product sales. The organization can make confident decisions on product pricing and sales targets for a particular product.

Use Case – 2

Business Problem: An agriculture production firm wants to predict the impact of rainfall amounts on the yield of particular crop.

Input Data: The Predictor/independent variable is the amount of rainfall during the monsoon months of the last year. The dependent variable is crop production data during the monsoon months of the last year.

Business Benefit: An agriculture firm can predict the yield of a particular crop, based on the amount of rainfall this year and can plan for alternative crop contingencies if the amount of rainfall is not adequate enough to achieve the targeted crop production.

Simple Linear Regression is a useful analytical technique when an organization wants to explore and analyze existence and nature of a relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is ARIMAX Forecasting and How is it Used for Enterprise Analysis? https://www.smarten.com/blog/what-is-arimax-forecasting-and-how-is-it-used-for-enterprise-analysis/ Fri, 29 Jun 2018 07:46:17 +0000 https://www.smarten.com/blog/?p=5803 Continued]]>

This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis.

What is ARIMAX Forecasting?

An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. This method is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.

For more information about data trend and pattern analysis techniques, read our article entitled, ‘ What Are Data Trends and Patterns, and How Do They Impact Business Decisions?’

ARIMAX is related to the ARIMA technique but, while ARIMA is suitable for datasets that are univariate (see the article, entitled’ What is ARIMA Forecasting and How Can it Be Used for Enterprise Analysis?’). ARIMAX is suitable for analysis where there are additional explanatory variables (multivariate) in categorical and/or numeric format.

To understand ARIMAX Forecasting, let’s look at an example of annual GDP values in India. As shown in the figure below, the plot of these data points suggests that this is non stationary data with an upward trend. This dataset is suitable for the ARIMAX algorithm because there is more than one variable affecting the GDP – in other words, the dataset is multivariate.

ARIMAX Forecasting Example

How Can ARIMAX Forecasting Be Used for Enterprise Analysis?

Let’s look at a business use case to illustrate the benefit of the ARIMAX Forecasting method.

Business Problem: A company wants to forecast its product line growth for the new couple of years, based on data from the past thirty (30) years. The predictor variables for this use case would be yearly consumer inflation rate, yearly GDP data and yearly population growth rate.

Business Benefit: By analyzing the various combinations of predictor variables, the business can forecast product growth, trends, patterns and seasonality, if any. The enterprise can also identify any gap between the targeted and estimated growth and develop an appropriate strategy to reduce this gap in order to achieve targets and results.

The ARIMAX forecasting method is suitable for forecasting when the enterprise wishes to forecast data that is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity.

ARIMAX provides forecasted values of the target variables for user-specified time periods to clearly illustrate results for planning, production, sales and other factors.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Karl Pearson Correlation Analysis and How Can it be Used for Enterprise Analysis Needs? https://www.smarten.com/blog/what-is-karl-pearson-correlation-analysis-and-how-can-it-be-used-for-enterprise-analysis-needs/ Fri, 29 Jun 2018 07:26:01 +0000 https://www.smarten.com/blog/?p=5797 Continued]]>

This article explains the Karl Pearson Correlation method of analysis, and how it can be applied in business.

What is the Karl Pearson Correlation Analytical Technique?

Correlation is a statistical measure that indicates the extent to which two variables fluctuate together. A positive correlation indicates the extent to which those variables increase or decrease in parallel. A negative correlation indicates the extent to which one variable increases as the other decreases. The Karl Pearson’s correlation measures the degree of linear relationship between two variables.

What is the Karl Pearson Correlation Analytical Technique?

In order to better understand the application of the Karl Pearson Correlation technique, let’s look at a sample analysis showing a positive correlation among data points.

Karl Pearson Correlation

Karl Pearson Correlation

Like other forms of correlation analysis, the Karl Pearson method measure the strength of relationships between only two variables, without taking into consideration the fact that both these variables may be influenced by a third variable. For example, sale of ice cream and the sale of cold drinks are related to weather conditions. They may show a positive correlation but they are not related to each other, but rather to the weather. Correlation analysis is applied only to numeric values, so if the data is not in numeric form, it must be converted. For example, survey responses like “Very dissatisfied”, “dissatisfied”, “neutral“, “satisfied”, “very satisfied” etc., must be converted to numeric ranking, i.e., 1,2,3,4,5.

How Can the Karl Pearson Correlation Method Be Used to Target Enterprise Analytical Needs?

Let’s take a moment to look at a use case so that we might better understand the application of the Karl Pearson Correlation method of analysis.

Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders.

Input Data: The delinquency rate of each credit card customer and the monthly income of each credit card customer.

Business Benefit: The credit card manager can decide on individual credit limit eligibility based on the correlation coefficient value between Income and delinquency rates.

Correlation analysis, and the Karl Pearson Correlation method, can be used to identify negative, positive and neutral correlations between two data points, e.g., the relationship between the age of a consumer and the color of shirt they might purchase or the level of education of a consumer and the delivery mechanism they choose for news and information.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Hierarchical Clustering and How Can an Organization Use it to Analyze Data? https://www.smarten.com/blog/what-is-hierarchical-clustering-and-how-can-an-organization-use-it-to-analyze-data/ Fri, 29 Jun 2018 06:48:08 +0000 https://www.smarten.com/blog/?p=5792 Continued]]>

This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes.

What is Hierarchical Clustering?

Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as much similar as possible within each group.

What is Hierarchical Clustering and How Can an Organization Use it to Analyze Data?

For example, items shown in the image above should be as similar as possible in terms of attributes of the items in each group, and objects in group 1 and group 2 should be as dissimilar as possible. All observations start in one cluster, and are then divided into two clusters. The data points within one cluster are as similar as possible, and the data points in other clusters are dissimilar from the other clusters being analyzed. For each cluster, we repeat the process until the specified number of clusters is reached.

Hierarchical Clustering Example

This type of analysis can be applied to segment customers by purchase history, segment users by the types of activities they perform on websites or applications, to develop consumer profiles based on activities or interests, and to recognize market segments, etc.

How Does and Organization Use Hierarchical Clustering to Analyze Data?

In order to understand the application of Hierarchical Clustering for organizational analysis, let us consider two use cases.

Use Case – 1

Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc.

Business Benefit: Once the segments are identified, the bank will have a loan applicants’ dataset with each applicant labeled as high/medium/low risk. Based on these labels, the bank can easily make a decision on whether to give loan to an applicant and how much credit to extend, as well as the interest rate the applicant will be given, based on the amount of risk involved.

Use Case – 2

Business Problem: The enterprise wishes to organize customers into groups/segments based on similar traits, product preferences and expectations. Segments are constructed based on customer demographic characteristics, psychographics, past behavior and product use behavior.

Business Benefit: Once the segments are identified, marketing messages and products can be customized for each segment. The better the segment(s) chosen for targeting by a particular organization, the more successful the business will be in the market.

Hierarchical Clustering can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so that the business can target pricing, products, services, marketing messages and more.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is SVM Classification Analysis and How Can It Benefit Business Analytics? https://www.smarten.com/blog/what-is-svm-classification-analysis-and-how-can-it-benefit-business-analytics/ Wed, 27 Jun 2018 13:39:27 +0000 https://www.smarten.com/blog/?p=5785 Continued]]>

This article provides a brief explanation of the SVM Classification method of analytics.

What is SVM Classification Analysis?

SVM Classifications are based on the idea of finding a hyper plane that best divides a dataset into predefined classes, as shown in the image below. The goal is to choose a hyperplane with the greatest possible margin between the hyper-plane and any point within the training set, giving a greater chance of new data being classified correctly.

What is SVM Classification Analysis?

To explore this technique further, let’s conduct the SVM classification using the following variables:

Conduct the SVM classification

Sample output of SVM classification

Here we see a sample output for the actual versus predicted outcome.

Here we see a sample output for the actual versus predicted outcome

Classification Accuracy (35+ 70) / (35+70+4+4) = 92%. The prediction accuracy is useful criterion for assessing the model performance. Model with prediction accuracy >= 70% is useful

Classification Error = 100- Accuracy = 8%. There is 8% chance of error in classification

SVM Classification Analysis can be used for many analytical tasks:

  • Credit/Loan Approval Analysis – Given a list of client transactional attributes, a business can predict whether a client will default on a loan.
  • Medical Diagnosis – Given a list of symptoms, a doctor can predict if a patient has a particular disease.
  • Weather Forecasting – Based on temperature, humidity, pressure etc. the organization can predict precipitation.
  • Treatment Effectiveness Analysis – Based on the body attributes of a patient, e.g., blood pressure, blood sugar, hemoglobin, prescription medication, and previous treatment methods, a doctor can determine the likelihood of treatment success.
  • Fraud Analysis – Based on various bills submitted for employee reimbursement for food, travel, medical expenses etc., the organization can predict the likelihood of an employee submitting fraudulent expenses.

How Can SVM Classification Analysis Benefit Business Analytics?

Let’s examine two business use cases where SVM Classification can benefit the organization.

Use Case – 1

Business Problem: A bank loan officer wants to predict if the loan applicant will default on a loan, based attributes such as Loan amount, monthly payment installments, employment tenure, number of times delinquent, annual income, debt to income ratio etc. The target variable would be ‘past default status’ and the predicted class would contain values ‘yes or no’ representing ‘whether the applicant is likely to default/unlikely to default’.

Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. Based on these labels, the bank can easily make a decision on whether to give loan to an applicant and how much credit to extend, as well as the interest rate each applicant is eligible for based on the amount of risk involved.

Use Case – 2

Business Problem: A doctor wants to predict the likelihood of successful treatment of a patient illness based on various attributes such as blood pressure, hemoglobin level, blood sugar, prescription medications, and current and previous treatments. The target variable would be ‘past cure status’ and predicted class would contain values ‘yes or no’ meaning ‘prone to cure/not prone to cure’ respectively.

Business Benefit: Given the patient profile, and current and previous treatments and medications, the doctor can establish a probability of success and make changes in treatments/medications.

SVM Classification analysis can help organizations to predict outcomes, based on attributes and variables in the profile of a customer, a patient, a product or other subjects or targets that are crucial to enterprise success.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Outlier Analysis and How Can It Improve Analysis? https://www.smarten.com/blog/what-is-outlier-analysis-and-how-can-it-improve-analysis/ Wed, 27 Jun 2018 12:51:53 +0000 https://www.smarten.com/blog/?p=5779 Continued]]>

This article presents a brief explanation of Outliers, and how this type of analysis is used.

What is Outlier Analysis?

An outlier is an element of a data set that distinctly stands out from the rest of the data. In other words, outliers are those data points that lie outside the overall pattern of distribution as shown in figure below.

Outlier Analysis

The easiest way to detect outliers is to create a graph. Plots such as Box plots, Scatterplots and Histograms can help to detect outliers. Alternatively, we can use mean and standard deviation to list out the outliers. Interquartile Range and Quartiles can also be used to detect outliers.

Here is another illustration of an outlier. If you look at the Histogram below, you will see that one value lies far to the left of all other data. This data point is an outlier.

Another illustration of an outlier

How Can Outlier Detection Improve Business Analysis?

Outlier data points can represent either a) items that are so far outside the norm that they need not be considered or b) the illustration of a very unique and singular category or variable that is worth exploring either to capitalize on a niche or find an area where an organization can offer a unique focus.

When considering the use of Outlier analysis, a business should first think about why they want to find the outliers and what they will do with that data. That focus will help the business to select the right method of analysis, graphing or plotting to reveal the results they need to see and understand.

When considering the use of Outlier analysis, it is important to recognize that, when the Outlier analysis is applied to certain datasets, the results will indicate that outliers should be discounted, while in other cases, the outlier results will indicate that the organization should focus solely on those outliers. For example, if an outlier indicates a risk or a mistake, that outlier should be identified and the risk or mistake should be addressed. If an outlier indicates an exceptional result, such as a person that recovered from a particular disease in spite of the fact that most other patients did not survive, the organization will want to perform further analysis on the outlier result to identify the unique aspects that may be responsible for the patient’s recovery.

When a business uses Outlier analysis, it is important to test the results and analyze the overall dataset and environment to be sure that the presence of outliers does not indicate that the dataset may be more complex than anticipated and may require a different form of analysis.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is the Decision Tree Analysis and How Does it Help a Business to Analyze Data? https://www.smarten.com/blog/what-is-the-decision-tree-analysis-and-how-does-it-help-a-business-to-analyze-data/ Wed, 27 Jun 2018 12:14:46 +0000 https://www.smarten.com/blog/?p=5774 Continued]]>

In this article, we will discuss the Decision Tree analysis method.

What is Decision Tree Analysis?

There are two basic types of decision tree analysis: Classification and Regression.

1) Classification Trees are used when the target variable is categorical and, as the name implies, are used to classify/divide the data into these predefined categories of a target variable.

Let’s look at two examples:

  • Based on the historical data related to credit card payments, loan payments, delinquency rate, outstanding balance we want to classify/divide the customers into those who default and those who do not default.
  • To access the characteristics of a customer such as his or her purchase frequency, income, age, type of bank account, occupation etc. that leads to purchase of a particular banking product such as installment loan, personal loan, checking account etc.

Let’s take a closer look at an example of classification tree analysis. Let’s say we have only two predictors, namely the level of Alcohol and free sulfur dioxide in a wine and we want to predict if wine quality (target variable) will be high or low.

Example of classification tree analysis

Since the target variable wine quality contains categorical values (high and low), the classification method will be applicable, as the predictors will be classifying the data into high and low.

2) Regression Trees are used when the target variable is numeric.

Let’s look at an example:

A business will analyze the past behavior of customers on a retail website, and look at variables like the number of days from the last purchase, the brand preference, income, age, gender, website visits, location, and the total amount of purchases. If we want to predict the purchase amount by each customer, regression trees are useful. Here the target variable would be the purchase amount. Similarly, regression trees can also be used to identify the market segment, identifying who is more likely to respond to a future mailing.

In this example, the segments that have a response rate higher than the overall response rate can be targeted first since they will require less effort to convert to a purchase, whereas a different marketing strategy must be devised for the lower segments (segments that have a response rate less than the overall rate).

Regression Trees Example

How Does Decision Tree Analysis Help an Organization to Analyze Data?

Let’s look at a few use cases that illustrate the benefits of the Decision Tree Classification Method.

Use Case – 1

Business Problem: Based on the historical customer attributes such as his/her credit card payments, loan payments, outstanding balance etc., a bank needs to classify customers into those that will default and those that will not default. In this case, the classification tree can be used to access the characteristics of customers that are likely to default.

Business Benefit: The bank can decide which customer segments are eligible for any type of loan and the customer segments that should be denied any loan, as they are likely to default. In this way, the riskier customers are identified easily and bank can avert the risk of delinquencies.

Use Case – 2

Business Problem: Based on customer attributes and past online shopping behavioral data, an online retail giant wants to predict the future purchases of customers. Here predictors can be ‘days from last purchase’, ‘brand preference’, ‘income’, ‘age’, ‘gender’, ‘website visits’, ‘location’, ‘total amount of purchase so far’ etc. As the target variable is numeric, namely the purchase amount, the regression tree can be used to predict the purchase amount by different types of customer segments.

Business Benefit: Online retailers can identify the customer segments with a higher capacity to purchase, and can design special marketing strategies for these types of segments, which are their main revenue drivers. This way, premium customers can be given special attention to retain their loyalty and in turn, revenue can be increased.

The Decision Tree analysis technique is useful in classifying and segmenting markets, types of customers and other categories in order to make decisions on where to focus enterprise resources.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is the Chi Square Test of Association and How Can it be Used for Analysis? https://www.smarten.com/blog/what-is-the-chi-square-test-of-association-and-how-can-it-be-used-for-analysis/ Tue, 26 Jun 2018 14:16:40 +0000 https://www.smarten.com/blog/?p=5760 Continued]]>

This article describes chi square test of association and hypothesis testing.

What is the Chi Square Test of Association Method of Hypothesis Testing?

It is used to determine whether there is a statistically significant association between the two categorical variables. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type. One might use this technique to determine whether gender is related to a voting preference or whether the two data points are independent and unrelated. An enterprise might also use Chi Square to determine if there is a relationship between the region in which a product is purchased, and the product or category of product that is purchased.

Let’s conduct the Chi square test of independence using two variables: Gender and Product category.

What is the Chi Square Test of Association Method of Hypothesis Testing?

At 95% confidence level (5% chance of error) – As p-value = 0.041 which is less than 0.05, there is a statistically significant association between gender and product category purchased. So, Gender has an influence on the type of product being purchased.

At 98 % confidence level (2% chance of error) – As p-value = 0.041 which is greater than 0.02, there is no statistically significant association between gender and product category purchased, so Gender has no influence on the type of product being purchased.

Chi square statistics

This analytical technique can be used for numerous purposes.

Marketing / Market Research – To determine if certain types of products sell better in certain geographic locations than others, and to verify if gender has an influence on purchasing decisions. It might be used to identify if there is an association between income level of consumers and their choice of brand. To determine If customer age has an influence on product/service subscription (assuming age is converted into age buckets such as 18 to 25, 26 – 35 etc). This technique is helpful in finding a relationship between any demographic characteristic of consumers or research respondents for education, location, gender etc. and various preferences/perspective/behavioral attributes such as entrepreneurial characteristics, product/service preferences, career preference, income level, political party preference etc.

Finance – An organization might use this technique to Identify if demographic factors influence banking channel/product/service preference or selection of a type of term plan of an insurance etc.

How Can the Chi Square Test of Association Be Used for Business Analysis?

Let’s look at a few use cases that depict the value of the Chi Square Test of Association.

Use Case – 1

Business Problem: A retail store marketing manager wants to know if there is a significant association between the geography of a customer and his/her brand preferences.

Business Benefit: Once the test is completed, p-value is generated which indicates whether there is significant association between geography and brand preference. Based on this value, a retail store marketing manager can design the ongoing marketing campaigns for different brands to address different geographical customers/prospect preferences.

Use Case – 2

Business Problem: A marketing researcher wants to know if gender has an influence on political party preference.

Business Benefit: Once the test is completed, p-value is generated which indicates whether there is significant association between gender and political party preference. If significant association is found, then both males and females can be targeted with different political campaigns to turn their votes in preference of a political client.

The Chi Square Test of Association Method of Hypothesis Testing allows businesses to test theories regarding the relationship of one or more data points to another data point to determine possible influencing factors for product purchases, or other outcomes.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is FP Growth Analysis and How Can a Business Use Frequent Pattern Mining to Analyze Data? https://www.smarten.com/blog/what-is-fp-growth-analysis-and-how-can-a-business-use-frequent-pattern-mining-to-analyze-data/ Tue, 26 Jun 2018 13:42:34 +0000 https://www.smarten.com/blog/?p=5756 Continued]]>

This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining.

What is the FP Growth Algorithm?

Frequent pattern mining (previously known as Association) is an analytical algorithm that is used by businesses and, is accessible in some self-serve business intelligence solutions. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.

When considering a particular set of transactions, association rule mining aims to find the rules that will enable businesses to predict the occurrence of a specific item based on the occurrences of the other items in the transaction.

This technique can used to analyze numerous types of datasets.

  • Basket data analysis – To analyze the association of purchased items in a single basket or single purchase.
  • Cross marketing/Selling – To work with other businesses that complement your business, but not your competitors. For example, vehicle dealerships and manufacturers have cross marketing campaigns with oil and gas companies for obvious reasons.
  • Catalog Design – The selection of items in a business catalog are often designed to complement each other so that buying one item will lead to buying of another. So these items are often complements or related.
  • Medical Treatments – Each patient is represented as a transaction containing the ordered set of diseases, and which diseases are likely to occur simultaneously or sequentially and can therefore be predicted.

How Does a Business Use the FP Growth method of Frequent Pattern Mining to Analyze Data?

Let’s look at a few use cases, where the FP Growth method of Frequent Pattern Mining can be used to benefit the organization.

Use Case – 1

Business Problem: A retail store manager wants to conduct Market Basket analysis to come up with better strategy of products placement and product bundling.

How Does a Business Use the FP Growth method of Frequent Pattern Mining to Analyze Data?

Business Benefit: The darker segments reveal the ideal methods of product bundling and placement to increase cross-sales. Based on the association rules generated, the store manager can strategically place the products together or in sequence leading to growth in sales and in turn revenue of the store. The business can develop promotions and offers, e.g., “Buy this and get this free” or “Buy this and get % off on another product”.

Use Case – 2

Business Problem: A bank marketing manager wishes to analyze which products are frequently and sequentially bought together. Each customer is represented as a transaction containing the ordered set of products, and which products customers are likely to purchase simultaneously, and sequentially.

Business Benefit: Based on the rules generated, the organization can determine which banking products can be cross sold to each existing or prospective customer to drive sales and bank revenue. For instance, if saving, personal loan and credit card are frequently sequentially bought, then a new saving account customer can be cross sold with personal loan and credit card services and products.

Frequent Pattern Mining and the FP Growth analytical technique are useful to identify patterns of purchases, behaviors and frequent and sequential occurrences based on historical data and demographics.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is ARIMA Forecasting and How Can it Be Used for Enterprise Analysis? https://www.smarten.com/blog/what-is-arima-forecasting-and-how-can-it-be-used-for-enterprise-analysis/ Tue, 26 Jun 2018 12:48:27 +0000 https://www.smarten.com/blog/?p=5752 Continued]]>

This article provides a brief explanation of the ARIMA method of analytical forecasting.

What is ARIMA Forecasting?

Autoregressive Integrated Moving Average (ARIMA) predicts future values of a time series using a linear combination of its past values and a series of errors. This analytical forecasting method is suitable for instances when data is stationary/non stationary and is univariate, with any type of data pattern, i.e., level/trend/seasonality/cyclicity.

For more information about data trend and pattern analysis techniques, read our article entitled, ‘ What Are Data Trends and Patterns, and How Do They Impact Business Decisions?’

The ARIMA model is suggested for short term forecasting. ARIMA is only for univariate data forecasts, but there might be other variables affecting the output/dependent variable. ARIMA doesn’t take into account the influence of other predictors while forecasting, hence forecasts made might not be accurate.

Let’s look at an example of a monthly analysis of monthly index values. The plot of this data suggests that this is non-stationary data and that it shows a gradual upward trend (see the figure below). The ARIMA algorithm would be a suitable method for forecasting analysis because the data exhibits non-stationarity, and trend.

What is ARIMA Forecasting?

The ARIMA forecasting technique uses three primary parameters for analysis within the model.

p: to apply autoregressive model on series
d: to apply differencing on series. It converts non-stationary data to stationary to allow for a fairly constant level over time
q: to apply moving average model on series

How Can the ARIMA Forecasting Method Be Used for Enterprise Analysis?

In order to further examine the ARIMA forecasting method, and its application within an organization, let’s look at a sample use case.

Business Problem: A pharmaceutical company wants to predict the sales of a drug for the next two months, based on drug sales data from the past 12 months.

Data Pattern: Input data exhibits non-stationarity and cyclical pattern.

Business Benefit: The business can make use of these forecasts for better planning of drug production and accuracy of sales targets. This analysis also helps to balance supply and demand for the drug.

The ARIMA forecasting method is suitable for forecasting when data is stationary or non-stationary and is univariate with any type of data pattern. It will produce accurate, dependable forecasts, when planning for short-term business results. ARIMA provides forecasted values of the target variables for user-specified periods to clearly illustrate results for planning, production, sales and other factors.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is the Multinomial-Logistic Regression Classification Algorithm and How Does One Use it for Analysis? https://www.smarten.com/blog/what-is-the-multinomial-logistic-regression-classification-algorithm-and-how-does-one-use-it-for-analysis/ Tue, 26 Jun 2018 12:25:45 +0000 https://www.smarten.com/blog/?p=5748 Continued]]>

This article provides a brief definition of the multinomial-logistic regression classification algorithm and its uses and benefits.

What is the Multinomial-Logistic Regression Classification Algorithm?

Logistic regression measures the relationship between the categorical target variable and one or more independent variables It deals with situations in which the outcome for a target variable can have two or more possible types. Logistic regression makes use of one or more predictor variables that can be either continuous or categorical and predicts the target variable classes. Logistic regression model output is helpful in identifying important factors that will impact the target variable and the nature of relationships between each of these factors and dependent variables.

What is the Multinomial-Logistic Regression Classification Algorithm?

This analysis reveals the following:

  • Age – Multinomial logit (Natural log of the proportion of High Satisfaction to that of Medium satisfaction) estimate for 1 year increase in age for high job satisfaction relative to medium job satisfaction when other independent variables are held constant = 1.54
  • Male vs. Female – Multinomial logit estimate for comparing male to females for high job satisfaction relative to medium job satisfaction when other variables are held constant = 0.67

How Does One Use the Multinomial-Logistic Regression Classification Algorithm?

Multinomial-Logistic Regression Classification can be applied to analyze numerous factors.

  • Medical Diagnosis – Given a list of symptoms, one can predict if a patient is likely to be diagnosed with initial/intermediate/serious stages of a particular disease.
  • Weather Prediction – Based on temperature, humidity, pressure etc. this analysis can predict rainy/sunny/cold weather.
  • Satisfaction Analysis – Based on the attributes of a respondent e.g., demographics, marital status, gender, income, age, qualification etc., analysis can check the level of likely satisfaction with life/job/product/services.

Let’s look at two use cases:

Use Case – 1

Business Problem: A research agency wants to predict the likelihood of each election candidate being voted on by each voter and in turn devise a strategy to take proactive steps. The analysis can include specific data points such as ‘preferred party name’ and predictors might include customer demographics such as age, income, qualification, occupation, gender, religion and past voting status etc.

Business Benefit: By having knowledge of the probable election outcome, the organization can develop a proper strategy to address the discrepancies between expectations and predictions and identify the segments with a high likelihood of voting oppositions to effectively target voters and achieve more votes in favor of a particular candidate.

Use Case – 2

Business Problem: A doctor wants to predict the likelihood of a new patient having a disease that is in the initial/moderate/severe stage based on various health and body attributes of a patient such as blood pressure, hemoglobin, blood sugar, red blood count, etc.

Business Benefit: Given the profile of a patient and predicted level of disease, the doctor can determine the right treatment and/or medication.

The Multinomial-Logistic Regression Classification Algorithm is useful in identifying the relationships of various attributes, characteristics and other variables to a particular outcome.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is the KMeans Clustering Algorithm and How Does an Enterprise Use it to Analyze Data? https://www.smarten.com/blog/what-is-the-kmeans-clustering-algorithm-and-how-does-an-enterprise-use-it-to-analyze-data/ Mon, 25 Jun 2018 11:32:18 +0000 https://www.smarten.com/blog/?p=5734 Continued]]>

This article provides a brief explanation of the KMeans Clustering algorithm.

What is the KMeans Clustering algorithm?

The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. KMeans Clustering is a grouping of similar things or data. For example, objects within group 1 (cluster 1) shown in image below should be as similar as possible.

What is the KMeans Clustering algorithm?

But there should be much difference between an object in group 1 and group 2.

The attributes of objects decide which objects should be grouped together. This method is used to find groups that have not been explicitly labeled in the data, and it can be used to confirm business assumptions about what types of groups exist, or to identify unknown groups in complex data sets. Once the algorithm has been run and the groups are defined, any new data can be easily assigned to the correct group.

How Does an Enterprise Use the KMeans Clustering Algorithm to Analyze Data?

In order to understand how best to make use of this algorithm; let’s look at some general examples, followed by some business use cases.

  • Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc.
  • A movie ticket booking website can group users into frequent ticket buyers, moderate ticket buyers and occasional ticket buyers, based on past movie ticket purchases.

KMeans Clustering can be applied to segment customers by purchasing history, segment users by the activities they perform on a website, define demographic profiles based on interests, and recognize market patterns.

Use Case – 1

Business Problem: Organizing customers into groups/segments based on similar traits, product preferences and expectations. Segments are constructed on basis of the customers’ demographic characteristics, psychographics, past behavior and product use behaviors.

Business Benefit: Once the segments are identified, marketing messages and even products can be customized for each segment. The better the segment(s) chosen for targeting by a particular organization, the more successful it is assumed to be in the market place.

Use Case – 2

Business Problem: Discount Analysis and Customer Retention will help the organization to target discounts to specific customers and the business will need to visualize ‘segments of sales group based on discount behavior’ and ‘customer churn to identify segments of customers on the verge of leaving’.

Business Benefit: The business marketing team can focus on risky customer segments in an efficient way in order to avoid losing those customers. Sales team segments that are facing challenges based on any current discounting strategy can be identified and a deal negotiation strategy can be improved and optimized.

The KMeans Clustering algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is Descriptive Statistics and How Do You Choose the Right One for Enterprise Analysis? https://www.smarten.com/blog/what-is-descriptive-statistics-and-how-do-you-choose-the-right-one-for-enterprise-analysis/ Mon, 25 Jun 2018 10:55:37 +0000 https://www.smarten.com/blog/?p=5725 Continued]]>

This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms.

What is a Descriptive Statistics?

Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. There are numerous methods of descriptive statistics, including Mean, Median, and Mode methods of averaging data and percentile, quartile, skewness and standard deviation/variance measurements as well as plotting methods like box plots and histograms.

To better understand these methods, let’s take a moment to review each.

Mean is the average of all the data values. This measurement can be biased in a case where there are a significant number of outliers present in the data.

Median is the value in the middle when the data items are arranged in ascending order. This measure is relatively robust where there are a significant number of outliers present in data.

Mode is the most frequently occurring value in a series of data. If there is no repeating value in the data, there would be no mode.

Percentile represents a percentage position in a list of data. For example, the 20th percentile is the value below which 20% of the observations may be found.

Percentile

Quartile represents the specific percentiles which divide the dataset into four equal parts. For example, Q1 = 25th Percentile, Q2 = 50th Percentile = Median, Q3 = 75th Percentile.

Quartile

Standard Deviation/Variance are both popular measures of the spread of data points measured from a center value mean.

Standard Deviation/Variance

Skewness is a measure of symmetry. A dataset is symmetric if it looks the same to the left and right of the center point.

Skewness

Histogram is a graphical display where the data is grouped into buckets and then plotted as bars.

Histogram

Box Plot is a standardized way of displaying data distribution based on the five-number summary: minimum, first quartile, median, third quartile, and maximum.

Box Plot

How Does One Choose the Right Descriptive Statistics Algorithm for Enterprise Analysis?

Let’s look at a few use cases for the various types of descriptive statistics.

1) Mean/Median

Business Problem: Find out the average age and income for a particular type of product category purchased.

Business Benefit: By identifying mean/median income of this segment, one can target marketing to this segment in order to improve ROI and sales revenue. Be sure to choose the right method for the type of data. For example, median is a better measure than mean if the business wishes to get an accurate picture. The median method would more accurately address the outliers in terms of extreme income values, whereas the mean method would skew the overall average when extreme income values are taken into the dataset.

2) Mode

Business Problem: Identify the most popular dish served in the restaurant or find out the most frequent rating given by customers for a given movie/ restaurant or most frequent size or category of a sold product etc.

Business Benefit: By identifying the mode of a name of Dish purchased, a restaurant owner can determine the most popular dish and decide on pricing and anticipate the need to order ingredients.

3) Percentile

Business Problem: A bank’s loan manager needs to find out the percentile distribution of the credit score of the loan applicants.

Business Benefit: By checking the credit score distribution, the loan manager will know the percentage of applicants that fall in the top 10 percentile and can estimate the total number of eligible loan applicants based on the bank’s set criteria for loan eligibility in terms of credit score.

4) Quartile

Business Problem: A business owner wants to reduce the business process cycle time.

Business Benefit: By checking Q1, Q3 and Inter quartile range (Q3-Q1) values of each step of the process, the owner can determine which particular step has a scope of time reduction.

5) Standard Deviation/Variance

Business Problem: A stock broker wants to analyze the price volatility of a stock as a measure of risk.

Business Benefit: By analyzing the standard deviation or variance, one can measure the risk associated with a particular stock in terms of price fluctuations.

6) Skewness/Histogram

Business Problem: A quality control manager of a company producing elevator rails needs to know which machine is ideal to produce rails.

Business Benefit: If the required diameter for an elevator rail is 3 inches, the quality control manager can determine which machines would produce rails that are too narrow and which would be too wide and choose the right machine without producing samples that would result in lost time and waste materials.

Flexible self-serve business intelligence and analytical tools will ensure data democratization among business users, and provide a comprehensive toolset to be used for planning methods and to test theories and clearly illustrate results by type, time period or other variables. Descriptive Statistical algorithms are sophisticated techniques that, within the confines of a self-serve analytical tool, can be simplified in a uniform, interactive environment to produce results that clearly illustrate answers and optimize decisions.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What is the Holt-Winters Forecasting Algorithm and How Can it be Used for Enterprise Analysis? https://www.smarten.com/blog/what-is-the-holt-winters-forecasting-algorithm-and-how-can-it-be-used-for-enterprise-analysis/ Thu, 21 Jun 2018 14:31:30 +0000 https://www.smarten.com/blog/?p=5718 Continued]]>

This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment.

What is the Holt-Winters Forecasting Algorithm?

The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method. Time series forecasting methods are used to extract and analyze data and statistics and characterize results to more accurately predict the future based on historical data.

For more information about data trend and pattern analysis techniques, read our article entitled, ‘ What Are Data Trends and Patterns, and How Do They Impact Business Decisions?’

The Holt-Winters forecasting algorithm allows users to smooth a time series and use that data to forecast areas of interest. Exponential smoothing assigns exponentially decreasing weights and values against historical data to decrease the value of the weight for the older data. In other words, more recent historical data is assigned more weight in forecasting than the older results.

There are three types of exponential smoothing methods used in Holt-Winters:

  • Single Exponential Smoothing – suitable for forecasting data with no trend or seasonal pattern, where the level of the data may change over time.
  • Double Exponential Smoothing – for forecasting data where trends exist.
  • Triple Exponential Smoothing – used for forecasting data with trend and/or seasonality.

When to Use Holt-Winters Single, Double and Triple Smoothing Models

How Can Holt-Winters Forecasting Be Used for Enterprise Analysis?

Time-Series forecasting methods and, in particular, the Holt-Winters forecasting algorithm can be helpful in providing forecasts for planning purposes by using historical data in a meaningful way. Because the results are smoothed, and the user can select the best option for the TYPE of data to be analyzed, the enterprise can avoid assigning too much weight or importance to older data that may no longer be as valid because of changing buying behaviors, market competition or other factors.

Let’s look at a few use cases that represent ideal examples of the various Holt-Winters exponential smoothing methods:

1) Single Exponential Smoothing Use Case

  • Business Problem: Forecasting number of viewers by day for a particular game show for next two months.
  • Input Data: Last six months daily viewer count data.
  • Data Pattern: Data taken as an input exhibit no trend /seasonality.
  • Business Benefit: Helps in planning for repeat telecast and for more advertisement (fund raising) if the projected count of viewers is high. Improvement planning can be done for the game show to increase/maintain the level of popularity.

2) Double Exponential Smoothing Use Case

  • Business Problem: Insurance claim manager wants to forecast policy sales for next month based on past 12 months data.
  • Data Pattern: Input data exhibits level and strong upward trend but no seasonality.
  • Business Benefit: If projected claims are lower than expected then proper marketing strategy can be devised to improve sales. Competition policy can be analyzed in terms of what all perks and benefits they provide to customers and existing policy can be modified to increase the market share.

3) Triple Exponential Smoothing Use Case

  • Business Problem: A power generator company wants to predict the electricity demand for next two months based on past 2 years’ daily power consumption data.
  • Data Pattern: Input data exhibits trend and seasonality.
  • Business Benefit: A power generator company can make use of these forecasts for the control and scheduling of power systems or power purchase agreements. It helps in balancing supply and demand.

When users select the appropriate forecasting algorithm for the data they wish to analyze, they can produce and share reports and data that will provide clear direction and decision support. In order to achieve the right results, it is imperative that a user select the right forecasting algorithm, based on the pattern and underlying data. Tools such as Smarten Plug n’ Play predictive analysis provide assisted predictive modeling capabilities. These augmented analytics tools use machine learning to auto-detect and recommend the best algorithm so users do not have to guess at the right selection. Smart Visualization ensures that data and its interpretation are clearly depicted in simple, natural language.

To provide flexible business intelligence and forecasting tools and ensure data democratization among business users, as well as accurate planning methods, an enterprise must select tools that are easy-to-use and easy to implement. The solution must include a full suite of advanced analytics tools to empower business users and create Citizen Data Scientists whose contribution to the organization will be a real asset and a true contributor to business results.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analyticsassisted predictive modelingsmart data visualizationself-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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What Are Data Trends and Patterns, and How Do They Impact Business Decisions? https://www.smarten.com/blog/what-are-data-trends-and-patterns-and-how-do-they-impact-business-decisions/ Wed, 20 Jun 2018 13:04:13 +0000 https://www.smarten.com/blog/?p=5708 Continued]]>

In this article, we will focus on the identification and exploration of data patterns and the trends that data reveals. The business can use this information for forecasting and planning, and to test theories and strategies. Let’s look at the various methods of trend and pattern analysis in more detail so we can better understand the various techniques.

Linear Trend

A linear pattern is a continuous decrease or increase in numbers over time. On a graph, this data appears as a straight line angled diagonally up or down (the angle may be steep or shallow). So the trend either can be upward or downward.

Linear Trend

Exponential Trend

This technique produces non linear curved lines where the data rises or falls, not at a steady rate, but at a higher rate. Instead of a straight line pointing diagonally up, the graph will show a curved line where the last point in later years is higher than the first year, if the trend is upward.

Exponential Trend

Damped Trend

In this analysis, the line is curved line to show data values rising or falling initially, and then showing a point where the trend (increase or decrease) stops rising or falling.

Damped Trend

Seasonality

One can identify a seasonality pattern when fluctuations repeat over fixed periods of time and are therefore predictable and where those patterns do not extend beyond a one year period. Seasonality may be caused by factors like weather, vacation, and holidays. It usually consists of periodic, repetitive, and generally regular and predictable patterns. Seasonality can repeat on a weekly, monthly or quarterly basis.

Seasonality

Irregular/Random Patterns

This type of analysis reveals fluctuations in a time series. These fluctuations are short in duration, erratic in nature and follow no regularity in the occurrence pattern. In prediction, the objective is to “model” all the components to some trend patterns to the point that the only component that remains unexplained is the random component.

Irregular/Random Patterns

Stationary/Stationarity

A stationary time series is one with statistical properties such as mean, where variances are all constant over time. A stationary series varies around a constant mean level, neither decreasing nor increasing systematically over time, with constant variance.

Stationary/Stationarity

Cyclical Patterns

Cyclical patterns occur when fluctuations do not repeat over fixed periods of time and are therefore unpredictable and extend beyond a year.

Cyclical Patterns

In this article, we have reviewed and explained the types of trend and pattern analysis. Every dataset is unique, and the identification of trends and patterns in the underlying the data is important. If a business wishes to produce clear, accurate results, it must choose the algorithm and technique that is the most appropriate for a particular type of data and analysis. For example, the decision to the ARIMA or Holt-Winter time series forecasting method for a particular dataset will depend on the trends and patterns within that dataset.

A basic understanding of the types and uses of trend and pattern analysis is crucial, if an enterprise wishes to take full advantage of these analytical techniques and produce reports and findings that will help the business to achieve its goals and to compete in its market of choice.

About Smarten

The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Smarten Augmented Analytics tools include plug n’ play predictive analytics, assisted predictive modeling, smart data visualization, self-serve data preparation and clickless analytics for search analytics with natural language processing (NLP). All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

The Smarten approach to data discovery is powered by ElegantJ BI Business Intelligence Solutions, a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

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Self-Serve Data Preparation for YOUR Users https://www.smarten.com/blog/2018/06/19/june-19-2018/augmented-data-preparation/ Tue, 19 Jun 2018 12:55:08 +0000 https://www.smarten.com/blog/?p=5704 Continued]]> Augmented Data Prep is Suitable for Every Business User

Self-Serve Data Prep Should be Just That – Self-Serve!

Self-serve has many meanings. You can pump your own gas, you can serve yourself at a buffet, and sometimes you can even do your own data preparation. You will notice that I said ‘sometimes’. That is because you have to choose the right tool if you want to really participate in self-serve data preparation.

With the right augmented analytics solution, advanced data discovery is accessible to team members and business users no matter their skills or technical knowledge with guides and recommendations to allow for easy integration and preparation of data.

Augmented Data Preparation allows business users to access, extract and prepare date on their own with clear insight into the sources and methods so that the outcome meets requirements. In the past, data preparation was a complex process of Data Extraction, Transformation and Loading (ETL), with restricted access to data warehouses and data marts, so if a business user needed data, they had to put in a request to the IT staff and would nearly always experience a delay in data delivery.

Today’s businesses don’t have the time or budget to provide unlimited IT resources and the fast pace of business and market changes has made it difficult to satisfy the day-to-day data requirements of business users.

ETL for business users and Self-Serve Augmented Data Preparation provides a set of sophisticated tools that is specifically designed for business users with ease-of-use and intuitive tools that allow access by business users to explore, manipulate and merge data sources, without advanced skills or training.

THAT is what I mean by Self-Serve Data Preparation. So, step right up to the buffet of data in your organization and get what you need, when you need it, with easy to use, sophisticated tools that will walk you through the entire experience. You’ll love it!

Here is how to get started: Self-Serve Data Prep

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Advanced Analytics Tools for Business Users! https://www.smarten.com/blog/is-an-augmented-analytics-tool-right-for-my-business-users/ Fri, 08 Jun 2018 12:09:08 +0000 https://www.smarten.com/blog/?p=5699 Continued]]> Is an Augmented Analytics Tool Right for my Business Users?

Are Smart Data Discovery Tools Smart Enough to Help Business Users?

Augmented analytics, augmented data discovery, advanced data discovery…all of these terms can seem intimidating but they don’t have to be. In fact, it might be less intimidating to think of advanced analytics software as Smart Data Discovery. When you use that term, you acknowledge the most important factor in this process, namely that the software itself is ‘smart’ and that using advanced analytics tools is not a process that is limited to data analysts or data scientists.

That is an important distinction! Because, the right Data Discovery Tool provides the most sophisticated, cutting edge features within a simple, easy-to-use interface that leads the business user through the process of data discovery. These advanced data discovery tools can significantly improve user adoption and make it easier for business users to share data and make clear decisions by understanding patterns, trends and the root cause of issues.

The tools can help users to identify opportunities and see interdependencies. In short, the right Data Discovery Software can lower the total cost of ownership (TCO) for an analytical tool and improve the return on investment (ROI). It can help the organization to sustain a competitive advantage and democratize analytics.

Contact Us to find out how these easy-to-use Smart Data Discovery Tools can help business users to be more effective.

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Silver Sponsor ElegantJ BI Demonstrates Smarten Analytic at Gartner Data & Analytics Summit, June 5-6, Mumbai, India https://www.smarten.com/blog/silver-sponsor-elegantjbi-demonstrates-smarten-analytic-at-gartner-data-analytics-summit-june-5-6-mumbai-india/ Fri, 25 May 2018 05:15:40 +0000 https://www.smarten.com/blog/?p=5692 Continued]]> Silver Sponsor ElegantJ BI Demonstrates Smarten Analytic at Gartner Data & Analytics Summit, June 5-6, Mumbai, India

ElegantJ BI, an innovative vendor in Business Intelligence, Augmented Analytics and Augmented Data Preparation, is pleased to announce its participation in the Gartner 2018 INDIA Data & Analytics Summit from 5 – 6th June 2018 in Mumbai, India. ElegantJ BI is proud to be a Silver Sponsor at this important event.

ElegantJ BI CEO, Kartik Patel says, “We look forward to demonstrating our Smarten analytics software solution, and to seeing new and familiar faces, as we welcome analytics experts and customers to this exciting event.”

Smarten Augmented Analytics represents the evolution of the ElegantJ BI approach to business intelligence, and the significance of self-serve data preparation, smart visualization, and assisted predictive modeling. The Smarten product roadmap lays the groundwork for Clickless Analytics powered by Natural Language Processing, and the ElegantJ BI team looks forward to introducing these and other features in the near future.

“As self-serve business analytics drives the industry, so too does the Smarten product embrace the concept of data democratization,” says Patel. “The Smarten roadmap is designed to address the need for more sophisticated analytics, in an easy-to-use environment, with tools that allow business users to perform complex analysis and achieve concise, personalized results quickly, with improved insight and clarity.”

The Smarten team will be on hand at the Gartner Data & Analytics Summit on June 5 and June 6 to demonstrate current product functionality including Smart Visualization, Plug n’ Play Predictive Analytics and Self-Serve Data Preparation. These Advanced Data Discovery tools enable business users to quickly and easily prepare and analyze data, to visualize and explore data, notate, highlight and share data with others. These tools allow for access to crucial data and enable users to mash up and integrate data, clarify analysis and use sophisticated algorithms in an intuitive environment to balance agility with data governance.

CEO, Kartik Patel says, “The Gartner Data & Analytics Summit also provides the opportunity for us to introduce customers and industry professionals to the future of Clickless Analytics. We are excited to discuss the future of Natural Language Processing within the Smarten Advanced Data Discovery solution and the resulting leverage of computational linguistics, data mining, and analytical algorithms which will provide a self-serve, natural language approach to data analysis.”

More information on the Gartner Data & Analytics Summit is available here: Gartner 2018 INDIA Data & Analytics Summit from 5 – 6 June 2018 in Mumbai, India

Read More: Silver Sponsor ElegantJ BI Demonstrates Smarten Analytic at Gartner Data & Analytics Summit, June 5-6, Mumbai, India

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Smart Visualization Tools Make You Smarter https://www.smarten.com/blog/2018/05/11/may-11-2018/smart-visualization/ Fri, 11 May 2018 13:16:37 +0000 https://www.smarten.com/blog/?p=5681 Continued]]> What is Smart Visualization and How Can It Help Me?

Why Should I Care About Smart Visualization and Advanced Data Discovery?

Are you up on the latest analytics lingo or do you still think smart visualization is some kind of artificial eyeball? Well, before you embarrass yourself at the next business conference, let’s get the facts!

Data Visualization Tools are part of an Advanced Data Discovery approach that allow users to gather various data components and tell a story that will clarify a problem, identify an opportunity or help to make a decision. In order to build and tell that story, the business user must be able to interact with their analytics software and build the story through guided visualization and recommended data presentation to best illustrate the underlying data and issues. Business users can quickly, and efficiently produce best possible visualization of underlying data based on data type, volume, dimensions, patterns and nature of data.

Smart Data Visualization tools allow users with average skills to cut through a mountain of data and find the nuggets of information that will make a real difference in business results using cutting-edge technology on the backend, and an understanding of the user experience on the front end. With this kind of support and foundation, your users can get a helping hand with suggestions and recommendations on how to view certain types of data and can personalize data displays to create meaningful views and collaboration. Smart Visualization takes the handcuffs off users and eliminates the need for the enterprise to anticipate exactly what each user needs.

Auto-recommendations, auto-suggestions and other tools guide users by recommending displays, views, and plotting to explore best possible value from underlying data. Here’s the thing. Smart Data Visualisation goes beyond data display and data monitoring to suggest options for visualization and plotting for certain types of data, based on the nature, dimensions and trends inherent in the data, so users can leverage sophisticated tools in an easy-to-use, drag and drop interface, with no advanced skill requirement or technical knowledge.

You don’t need an artificial eyeball, but you DO need Advanced Data Discovery with Smart Visualization Tools, and so do your users! : The Smarten approach to data analytics will get you there.

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Is Plug & Play Predictive Analytics Good for Business Users? https://www.smarten.com/blog/plug-n-play-predictive-analytics-helps-business-users/ Wed, 02 May 2018 11:37:33 +0000 https://www.smarten.com/blog/?p=5676 Continued]]> Plug n' Play Predictive Analytics Helps Business Users

Can Plug & Play Predictive Analytics Help Business Users Function Effectively?

Plug & Play Predictive Analytics is not an exotic process that is limited to data scientists or IT staff. Plug & play predictive analysis is so named because it really is a plug and play process. This type of predictive analytics tool is designed to be accessible and usable by business users.

In today’s fast-paced, competitive business landscape, no enterprise can afford to wait for clear, concise information. No team member can be expected to achieve peak performance if they have to wait for business intelligence and clear analysis and information. Often, the process of submitting predictive analytics requests can be cumbersome and much is lost in the translation. Users wait for data, only to find that the data is incomplete or out-of-date or that they forgot certain parameters or critical factors and that they must start again.

A good Predictive Analytics Tool offers Assisted Predictive Modeling with guidelines and auto-recommendations to help the user develop an approach to forecasting and planning and obtain clear data that will answer questions and clarify direction. This type of predictive analytics for business users will ensure that the process is simple and clear so users can do a better job of predicting outcomes, revenue targets, planning for new locations, new products and other critical business initiatives.

Predictive Analysis Software with sophisticated, easy-to-use tools can significantly improve time-to-market, and ensure that forecasts and predictions are more accurate and that each business user is working with and sharing clear, actionable data.

Contact Us to find out how these Smarten tools can help your business.

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Data Visualization Software for Business Users https://www.smarten.com/blog/self-serve-data-visualization-guides-business-users-to-the-best-view/ Fri, 27 Apr 2018 12:34:53 +0000 https://www.smarten.com/blog/?p=5671 Continued]]> Self-Serve Data Visualization Guides Business Users to the Best View

Smart Data Visualization Takes the Guesswork Out of Analytics!

Smart data visualization tools should include augmented data discovery with features that guide and show business users how to get the most out of data and how to display that data in a way that will make decision-making and analysis as easy as drag and drop. Data visualization does not have to be complicated, nor does it require software or features that take months to learn and master.

Smart Data Visualization suggests the best options for visualizing and plotting for a particular set or type of data, based on the nature, dimensions and trend of data.

Smart Data Visualization Tools allow users to gather various data components and tell a story that will clarify a problem, identify an opportunity or help to make a decision. In order to build and tell that story, the business user must be able to interact with their analytics software and build the story through guided visualization and recommended data presentation to best illustrate the underlying data and issues. Business users can quickly and efficiently produce the best possible visualization of underlying data, based on data type, volume, dimensions, patterns and nature of data.

In the past, reporting and data visualization was a static process with graphs and charts and little if any options. These methods and tools certainly did not offer suggestions or recommendations on how best to view and analyze the data based on data types or other parameters.

If you want your business users to participate in data analysis and make the right decisions about their team, their division, their role and their responsibilities, you have to give them better tools to analyze and understand data and choose the right method to display and report data. Self-Serve Data Visualization Software incorporates cutting-edge technology on the backend, and an understanding of the user experience on the front end, so users get a helping hand with suggestions and recommendations on how to view certain types of data and can personalize data displays to create meaningful views and collaboration.

Smart Data Visualization can give your organization an advantage by identifying critical data points, patterns and trends that will allow you to strategize and operate in an optimal environment.

Contact Us to find out how these Smarten tools can help your business.

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Advanced Analytics that is Easy Enough for All Business Users https://www.smarten.com/blog/data-discovery-software-that-will-guide-users-at-every-step/ Wed, 25 Apr 2018 12:07:43 +0000 https://www.smarten.com/blog/?p=5667 Continued]]> Data Discovery Software that Will Guide Users at Every Step!

Augmented Analytics and Advanced Data Discovery for Business Users!

Augmented analytics can help your business users to find and understand critical data and to do their jobs more effectively. Without the knowledge of a data scientist or an IT professional, business users can perform advanced analytics, using features that guide and make recommendations at every step so that data is prepared, viewed and analyzed in a way that ensures clarity and a comprehensive understanding of trends, patterns, and data sensitivity and success thresholds.

Advanced Data Discovery software must provide appropriate access and security, mobile scalability and performance and guides and auto-suggestions, that will allow Smart Data Discovery and allow users to find the right data and to leverage that data to anticipate problems and capitalize on opportunities without a misstep or delay.

No organization can afford to hire a large team of data professionals and business users should not be expected to employ complex tools that require that kind of sophisticated knowledge. That doesn’t mean that your business users do not deserve and need the same sophisticated analysis. They need to have and share data that will illustrate clear results in a format that leaves no doubt about the right decision.

Advanced Data Discovery can and should be accessible to everyone in your organization. That access will make your users and your business more successful and will eliminate frustration and needless delays. It will also ensure user adoption of Self Service Business Intelligence tools and swift implementation and training of these crucial tools to achieve low total cost of ownership (TCO) and rapid return on investment (ROI).

Discover the benefits of Augmented Data Discovery. Contact Us now to find out about Smarten BI and how these tools can help your organization and users achieve goals and function more effectively.

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Advanced Data Discovery for Every Skill and User https://www.smarten.com/blog/augmented-data-discovery-helps-business-users-achieve-results/ Fri, 20 Apr 2018 11:50:13 +0000 https://www.smarten.com/blog/?p=5663 Continued]]> Augmented Data Discovery Helps Business Users Achieve Results

Augmented Analytics is the Key to User-Friendly Business Intelligence!

Augmented Analytics and augmented data discovery is a form of advanced data discovery that automates data insight using machine learning and natural language generation. It automates data preparation and makes it easy for business users to enjoy data sharing.

Advanced Analytics uses sophisticated techniques and algorithms in an automated environment to simplify the analytical process for the average business user, so users are presented with clear results to use in making decisions and analyzing problems. The augmented analytics approach provides better clarity and insight than the more traditional forms of analysis.

The world of data analytics is no longer restricted to IT, data scientists and analysts. If your business is going to be productive and successful it must allow business users to access intuitive tools with sophisticated features, so that the entire team can work from the same data and share that data in reports and graphics that will help he organization achieve its goals.

Advanced Analytics Tools allow the average business user to analyze and display results in a clear manner to make informed, unbiased decisions. Users can compare results against plans and forecasts, and explore and present data using data science modeling, algorithms and auto-suggested data displays, so the organization can build and sustain a competitive advantage. Augmented Analytics provides clear results in context so users can drill down to find the root cause of a problem and discover subtle patterns and trends that will help the business achieve its goals.

There are many benefits to the Smarten and Augmented Analytics approach to business intelligence. If you want to take advantage of these benefits and add value to your organization, Contact Us.

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Plug n’ Play Predictive Analytics for Your Business https://www.smarten.com/blog/assistive-predictive-modeling-for-every-business-user/ Tue, 17 Apr 2018 13:14:10 +0000 https://www.smarten.com/blog/?p=5658 Continued]]> Assistive Predictive Modeling for Every Business User

How Can Assistive Predictive Modeling Help My Business Users?

Assistive Predictive Modeling allows business users to leverage a self-serve advanced analytical tool and to enjoy complex, sophisticated forecasting and business predictions in a simple, user-friendly dashboard environment – all without the skills of an analyst, data scientist or IT professional.

Predictive Analytics for business users incorporates analytical and forecasting techniques with easy-to-use tools to guide them through recommended techniques and report formats and ensure that the methods and reports they choose are appropriate to the type of data and information they need.

If you are wondering how and why predictive analytics software has expanded into the self-serve business user market, the reason is simple. Every business is operating in a rapidly changing competitive environment and market. In order to achieve goals and objectives and perform well in their respective roles, business users need to analyze data, draw conclusions, predict results and help the organization achieve its goals.

Businesses cannot afford to wait for IT, or professional analysts or data scientists to produce reports. Every enterprise needs a clear guide to results – one that every business user can leverage without expensive, time-consuming training or special skills.

Plug n’ Play Predictive Analysis offers easy-to-use navigation, drag and drop flexibility and personalized dashboard capabilities, so every user in the organization can plan and make decisions, share data, analyze and make decisions with confidence.

Predictive Analytics Software should be easy to implement, easy to personalize and easy to use. It should provide for rapid ROI and low TCO and allow for easy integration of data from numerous data sources and mobile accessibility that is suitable for every type of device.

Assisted Predictive Modeling allows for planning and forecasting at any point in time, and enables business users and enterprises to test assumptions and theories in a risk-free environment so the business can make changes, add resources, and get to the heart of issues without depending on educated guesses or opinion.

Assisted Predictive Modeling can help your business achieve its goals. If you want to find out more, Contact Us.

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What is So Important About Augmented Data Discovery? https://www.smarten.com/blog/2018/04/12/april-12-2018/augmented-data-discovery/ Thu, 12 Apr 2018 13:22:11 +0000 https://www.smarten.com/blog/?p=5654 Continued]]> Are Augmented Data Discovery Tools Important for My Business?

Can Augmented Data Discovery Make a Difference in My Organization?

There are so many new terms in the business intelligence and advanced analytics domain. So, what is augmented data discovery, and why is it important for your enterprise? Augmented Data Discovery (aka Smart Data Discovery), takes the enterprise beyond data monitoring and helps users discover the more subtle yet crucial factors that affect business success. It identifies hidden issues and patterns within the data so the organization can address challenges, capitalize on competitive and market advantages and plan for the future with more confidence. There are a couple of components to the augmented data discovery continuum.

First, there is Augmented Data Preparation This process helps users access data integrated from varied data repositories and sources in a single interface, so they can test theories and hypotheses. These tools give users access to crucial data and Information and connect to data sources, including personal, external, Cloud, and IT provisioned data, to mash-up and integrate data. The right augmented data discovery solution provides tools like auto-suggested relationships, JOINs, type casts, hierarchies and clean, reduce and clarify data with integrated statistical algorithms like binning, clustering and regression for noise reduction and identification of trends and patterns.

The second component of Augmented Data Discovery is Augmented Analytics. This process automates data insight by utilizing Machine Learning and Natural Language Processing and automates data preparation so users can share crucial data and make decisions. With an advanced augmented data discovery solution, the organization can share, manipulate and present data with clear results and access to sophisticated tools. These tools ensure that the organization makes decisions based on fact rather than opinion.

If you want to know what makes augmented data discovery so important, it is quite simple, actually. With Augmented Data Discovery, the organization can notate and highlight data, share data with other users and most importantly, the enterprise can identify critical ‘ah hah’ data points and information hidden within the data to accurately plan, forecast and solve problems. These tools provide support, suggesting relationships, identifying patterns, suggesting visualization techniques and formats, highlighting trends and patterns and helping users and organizations to forecast and predict results. This enables the enterprise to manage data overload and overwhelming workload, and to provide important information and answers to every team member with sophisticated tools in an easy-to-use, drag and drop interface, which requires no advanced skill or knowledge of statistical analysis, algorithms or techniques.

If this sounds like something you want to explore, you can get started here: Smarten Approach

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Analytics Translator? Citizen Data Scientist? What is the Difference? https://www.smarten.com/blog/analytics-translator-citizen-data-scientist-what-is-the-difference/ Wed, 11 Apr 2018 10:13:57 +0000 https://www.smarten.com/blog/?p=5648 Continued]]> Analytics Translator? Citizen Data Scientist? What is the Difference?

There is a new business role on the horizon and, at first glance, it may seem very much like a role that was introduced a few short years ago. This new enterprise role is known as an ‘Analytics Translator’ and, while there is some confusion regarding the distinction between this role and the newly minted Citizen Data Scientist or Citizen Analyst, there are some subtle but important differences. In a previous article (What is an Analytics Translator and Why is the Role Important to Your Organization?), we discussed the definition of an Analytics Translator. Here, we will discuss the role of Citizen Data Scientist and Analytics Translator and how they differ. To understand these roles, let’s look first at the somewhat more familiar role of Citizen Data Scientist (AKA Citizen Analyst).

What is a Citizen Data Scientist (Citizen Analyst)?

Gartner defines a Citizen Data Scientist as ‘a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.’ A Citizen Data Scientist is different from a true Data Scientist in one crucial way; namely, they do not have the skills or training to be an analyst or a programmer but, with the right tools, they are capable of generating reports, analyzing data and sharing data to make decisions.

Citizen Analysts represent a new breed of business user. These business users have adopted business intelligence and advanced analytical tools to gather and analyze data from varied data sources and use that analysis to identify the root cause of problems, identify opportunities, solve problems and share crucial data to support business decisions. Citizen Analysts create and generate data models and use sophisticated analytics that are supported by easy-to-use interactive BI dashboards. By definition, Citizen Analysts are not data scientists, or professional analysts or IT staff. Rather, they hold varied positions within the business organization and use data analysis to support decisions made within their business role, team or responsibility.

How Does the Analytics Translator Role Differ?

The Analytics Translator is an important member of the new analytical team. As organizations encourage data democratization and implement self-serve business intelligence and advanced analytics, business users can leverage machine learning, self-serve data preparation, and predictive analytics for business users to gather, prepare an analyze data. The emerging role of Analytics Translator adds resources to a team that includes IT, data scientists, data architects and others.

Analytics Translators do not have to be analytical specialists or trained professionals.

With the right tools, they can easily translate data and analysis without the skills of a highly trained data pro. Using their knowledge of the business and their area of expertise, translators can help the management team focus on targeted areas like production, distribution, pricing and even cross-functional initiatives. With self-serve, advanced analytics tools, translators can then identify patterns, trends and opportunities, and problems. This information is then handed off to data scientists and professionals to further clarify and produce crucial reports and data with which management teams can make strategic and operational decisions.

When identifying possible candidates to perform the Analytics Translator role, the organization should look for skills that can be nurtured and optimized as an asset.

  • A power user of self-serve BI tools
  • Recognized as an expert in a functional, industry or organizational role
  • Comfortable with building and presenting reports and use cases
  • Works well with technical and management teams
  • Manages projects, milestones and dependencies with ease
  • Able to translate analysis and conclusions into actionable recommendations
  • Comfortable with metrics, measurements and prioritization
  • Acts as a role model for user and team member adoption of new processes and data-driven decisions

If this role is recognized as important to the organization, most enterprises will structure a logical program to identify and train candidates to ensure uniform skills and performance.

By combining domain, organizational and industry skills with self-serve analytical tools, the Analytics Translator can help the enterprise to achieve low total cost of ownership (TCO) and rapid return on investment (ROI) for its Business Intelligence and Advanced Analytics initiatives and can encourage and nurture data democratization and optimal analytical business results within the organization.

Citizen Data Scientists/Citizen Analysts play a crucial role in day-to-day analysis and decision-making, using Self-Serve Business Intelligence Tools. Analytics Translators bridge the gap between IT, data scientists and business users, and move initiatives forward by acting as a liaison and topic expert to help the organization focus on the right things to achieve its goals.

Does your organization need Citizen Data Scientists AND Analytics Translators? The answer is, YES!

Original Post: Analytics Translator? Citizen Data Scientist? What is the Difference?

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Self-Serve Data Preparation for Business Users https://www.smarten.com/blog/augmented-data-preparation-saves-time-and-produces-results/ Thu, 05 Apr 2018 11:53:54 +0000 https://www.smarten.com/blog/?p=5643 Continued]]> Augmented Data Preparation Saves Time and Produces Results

Is Self-Serve Data Preparation Really Possible?

Your business users are ready to do the job! They have a lot of data spread across the enterprise in various data repositories and forms and they want to pull it all together and analyze the data to get the answers to the questions you ask them every day. But, preparing that data is not easy.

In the old days, team members would reach into a file cabinet and pull out folders with the right information. They would pour over the numbers and the data and draw conclusions and then make a plan or provide a report. Sometimes the data was old and sometimes the conclusions they drew were incorrect or based on biased opinion.

Today, Self-Serve Data Preparation can provide business users with the tools they need to find and prepare the data they will need to answer those crucial questions and to do all of that without the assistance of an already overburdened IT staff. Data preparation does not have to be difficult.

Now your users can get what they want, when they want it with complete control over data elements, as well as the volume and the timing. Self Service Business Intelligence provide easy to use tools so that business users can prepare their data on their own without the assistance of IT staff. They can use simple extraction, transformation and loading features – ETL for business users – to extract the data they want and perform analysis and reporting quickly and easily.

Self-Serve Data Preparation allows business users to perform data preparation and Augmented Data Preparation features allow business users to test theories and hypotheses by prototyping on their own. Users have access to simple, easy-to-use interfaces, and drag and drop functionality, without the need for complex tools.

When data preparation for analytics is married with self-serve data prep, users can bypass the process of preparing data at the central meta data layer and access, and prepare data to create and share reports and create custom alerts. Using Smarten suggestions and auto-suggested relationship, they can discover answers and leverage JOINS, hierarchies and type casts without the skill and knowledge of a data scientist.

Today, Self Service Data Preparation is a reality. If you want to find out more, Contact Us.

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Predictive Analytics for Business Users! https://www.smarten.com/blog/plug-n-play-predictive-analysis-is-good-for-all/ Mon, 02 Apr 2018 11:48:24 +0000 https://www.smarten.com/blog/?p=5639 Continued]]> Plug n' Play Predictive Analysis is Good for All!

Can Business Users Adopt Assisted Predictive Modeling?

Assisted predictive modeling is no longer the sole domain of data scientists and IT staff. With the right predictive analytics tools, your business users can accurately plan and forecast results and share data to build a dependable picture of the future for your organization, and for each team, division and individual.

Plug n’ Play Predictive Analysis can truly provide predictive analytics for business users and predictive analytics can benefit organizations in many ways. Whether you want to find out how best to acquire and retain customers, what new products and services your customers want, how to price a product, where to open a new location, or how your customer purchasing behavior is changing, predictive analysis can help you understand historical patterns and use your data and results to more accurately predict the future.

In the past, business users struggled to understand the relationship between their roles and responsibilities and the results the organization achieved. If you empower your business users with easy-to-use, sophisticated predictive analytics, you can cascade and share strategic, operational and tactical goals and enable users to share data and use that data to make better decisions.

Your business users are not data scientists, nor should they have to acquire and use sophisticated skills to get the answers they need. With real self-serve, Predictive Analysis Tools, business user can apply complex predictive algorithms without the expertise and skill of a trained data scientist or the assistance of IT staff. With these tools, business users will become more of an asset to the organization and you can transform every team member into a Citizen Data Scientist and employ their unique professional skills and knowledge to gather and analyze data in a way that is meaningful to their role and their assigned tasks and activities.

No matter how small or large your business, and no matter your industry, your organization cannot afford a market or competitive misstep. There is no time to guess at the future. But, with the right tools to build the right plan and anticipate product, service, pricing, distribution, location and customer changes and evolution, your business can accurately plan for success.

Take the Smarten approach to assisted predictive modeling and Plug n’ Play Predictive Analysis.

Contact Us today to find out how to get started.

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Augmented Data Discovery for Business Users https://www.smarten.com/blog/augmented-analytics-is-easy-enough-for-business-users/ Tue, 20 Mar 2018 10:41:25 +0000 https://www.smarten.com/blog/?p=5634 Continued]]> Augmented Analytics is Easy Enough for Business Users

Is Augmented Analytics Too Complex for Business Users?

The concept of Augmented Analytics may sound complicated (and it may, in fact, be a challenge for a software vendor to accomplish), but the new augmented data discovery tools are quite easy to use. Unlike the data discovery tools of old, business users do not need to look to the IT staff or data scientists to perform advanced data discovery.

The rapidly expanding self-serve business intelligence and analytics market has provided Advanced Analytics Tools that are suitable for every business user. True data democratization and self-serve tools and features allow business users to leverage advanced analytics, and sophisticated algorithms and features in an easy-to-use environment to easily prepare and analyze data and to visualize and explore data, notate and highlight data and share data with others.

If you think your users can’t take advantage of these tools, imagine an environment with a drag and drop interface, where no advanced skills are required. And users can gather, prepare, integrate and analyze data, find patterns and trends, share findings and apply those results to strategic, operational and tactical activities.

To truly gain a competitive advantage and achieve objectives, your users must be able to take the guesswork out of decision-making and planning and discover those subtle and important factors that highlight issues and patterns, and help the organization capitalize on opportunities.

Advanced Analytics Software features suggests relationships, identify patterns, suggest visualization techniques and formats, highlight crucial trends and patterns, and present accurate predictions and forecasts, all in an intuitive interface that will ensure ease-of-use and business user adoption. If you want to explore the benefits of Augmented Analytics, Contact Us now.

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Assisted Predictive Modeling and Analytics for Everyone https://www.smarten.com/blog/2018/03/13/march-13-2018/assisted-predictive-modeling-plug-and-play/ Tue, 13 Mar 2018 13:08:51 +0000 https://www.smarten.com/blog/?p=5627 Continued]]> Predictive Analytics for All

Need Analytics for Business Users AND Data Scientists? No Problem!

Does your business intelligence solution provide true advanced analytics capabilities? Can your BI tool satisfy the needs of business users, data scientists and IT staff? That may seem like a tall order but with the right business intelligence software, you can provide predictive analytics for business users, including assisted predictive modeling that walks users through the analytical process and allows them to achieve the best results without a sophisticated knowledge of data analytical techniques.

You can transform those business users into Citizen Data Scientists! Plug n’ play predictive and forecasting tools help businesses to create Citizen Data Scientists by enabling the average business user to leverage sophisticated predictive algorithms without the expertise and skill of a trained data scientist, so users who are not statisticians or predictive algorithm experts, can leverage Self-Service Plug n’ Play Predictive Analytics Tools to confidently make business decisions.

At the same time you can give your data professionals tools like R integration to satisfy the needs of skilled data scientists and business analysts who use the ‘R’ platform for statistical and predictive algorithms. You can give them a solution that integrates seamlessly with R Script so that they can leverage Plug n’ Play Predictive Analysis in combination with R scripting integration, to perform more sophisticated and complex analysis, achieve clarity and provide detailed, meaningful advanced analytics and reporting for the organization.

Plug and Play Predictive Analysis enables analysis of customer churn and acquisition, cross-sales and other opportunities, price points for new products, distribution channels, customer loyalty programs, and more. Whether serving business users, IT staff or data scientists, the right advanced analytics solution will provide features for time series forecasting, causation and prediction algorithms, classification and prediction and other features that allow users to work at their own skill level and produce clear reporting and analysis.

There is no point in frustrating or limiting the user when one tool can satisfy the requirements of every user and skill level in the organization. If you want to explore the possibilities, start here:

If this sounds like something you want to try, you can start here: Smarten Approach

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Why Do You Need Self-Serve Data Preparation? https://www.smarten.com/blog/is-self-serve-data-prep-really-self-serve/ Wed, 07 Mar 2018 10:24:07 +0000 https://www.smarten.com/blog/?p=5623 Continued]]> Is Self-Serve Data Prep, REALLY Self-Serve?

Self-Serve Data Preparation Takes the Headache Out of Data Analytics!

Self-Serve Data Preparation (aka augmented data preparation) is all about efficiency and the presentation of sophisticated data preparation tools in an easy-to-use environment. The idea behind self-service data preparation is to give the average business user the ability to prepare, use, report on and share data without the assistance of IT staff or analysts, thereby making their jobs easier and making every team member more of an asset to the organization.

Business users love Self-Serve Data Preparation because they can control data elements, and the volume and timing, perform data preparation and test theories and hypotheses by prototyping on their own. No one likes to be restricted to complex tools or forced to wait for programmers or data scientists.

Give your business users access to crucial data and connect them to data sources so they can mash up and integrate data in a single, one-stop, interactive view. Great Data Preparation for analytics allows users to leverage auto-suggested relationships, JOINs, type casts, hierarchies, and reduces and clarifies data to make it easier for users to interpret and analyze data.

Business users with average technical skills can capitalize on integrated statistical algorithms like binning, clustering, and regression for noise reduction, and trend and pattern identification, and do it all without assistance. And, if your organization is concerned about data governance, there is no reason to worry. Your organization can promote data and reports created by business users to IT provisioned, and IT approved data sources, and identify these data sources with clear watermarks to provide an appropriate balance between agility, governance and data quality.

So, yes, you can have it all! Self-Serve Data Preparation is within your reach. If you want to take the Smarten approach and help your business users get to the next level, Contact Us.

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Is Self-Serve Data Prep the Same as Augmented Data Prep? https://www.smarten.com/blog/2018/02/27/february-27-2018/self-serve-data-preparation/ Tue, 27 Feb 2018 08:55:00 +0000 https://www.smarten.com/blog/?p=5617 Continued]]> Self-Serve Data Prep is Possible and Wonderful!

Self-Serve Data Preparation and Augmented Data Prep Go Hand in Hand!

I was talking to a friend the other day and she shared with me her experience at a recent business intelligence conference. She was a bit confused by some of the terminology and we spent a few minutes parsing the terms and talking about the concept of self-serve data preparation. She was confused by the fact that self-serve data prep and augmented data preparation are often mentioned in the same discussion.

“If something is self-serve,” she said. “How can it be augmented? Self-serve seems to me to mean that a person can do it alone without assistance.” She considers ‘augmenting’ to be assisting. Well, in some ways she is correct. But, let me tell you what I told her.

Augmented Data Preparation is designed to provide guidance, recommendations and auto-suggestions so that a user can work alone to get sophisticated results in a simple, intuitive, self-serve environment.

Self-Serve Data Preparation allows business users to perform data preparation and test theories and hypotheses by prototyping on their own. Users are not restricted to complex tools or forced to wait for programmers or data scientists, so in that respect, using the system is truly a self-serve experience!

Transform business users into Citizen Data Scientists with Self-Serve Data Preparation Tools that allow users to compile and analyze data and test theories and prototypes to support dynamic decisions and planning on their own. Augmented data prep allows for advanced data discovery with auto-suggested relationships, JOINs, type casts, hierarchies, etc. and it reduces and clarifies data so that it is easier to use and interpret the data for analysis. There is no need for advanced skills or technical knowledge to gather, compile and prepare the data. Users can leverage integrated statistical algorithms like binning, clustering, and regression for noise reduction, and trend and pattern identification.

While my friend didn’t really know about binning, clustering or regression, she did grasp the significance of having a tool to guide her through the process of preparing data and help her see the results and the relationships with more clarity. Yes, the system does ‘augment’ and support the user but the user is able to do the work on their own and that is great! It means users don’t have to wait for IT staff or analysts to help them. It means, the business intelligence solution is self-serve!

If this sounds like something you want to try, you can start here: Smarten Approach

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What is an Analytics Translator and Why is the Role Important to Your Organization? https://www.smarten.com/blog/what-is-an-analytics-translator-and-why-is-the-role-important-to-your-organization/ Fri, 23 Feb 2018 09:07:50 +0000 https://www.smarten.com/blog/?p=5613 Continued]]> What is an Analytics Translator and Why is the Role Important to Your Organization?

Today, enterprises recognize the critical value of advanced analytics within the organization and they are implementing data democratization initiatives. As these initiatives evolve, new roles emerge in the organization. The newest of these analysis-related roles is the ‘analytics translator‘. As the enterprise considers the relevance of this new role within the business, it is important to understand the responsibilities of an Analytics Translator, and how this role might help the organization to achieve its goals.

What is an Analytics Translator?

The Analytics Translator is an important member of the new analytical team. As organizations encourage data democratization and implement self-serve business intelligence and advanced analytics, business users can leverage machine learning, self-serve data preparation, and predictive analytics for business users to gather, prepare an analyze data. The emerging role of Analytics Translator adds resources to a team that includes IT, data scientists, data architects and others.

Analytics Translators do not have to be analytical specialists or trained professionals. With the right tools, they can easily translate data and analysis without the skills of a highly trained data pro.

Using their knowledge of the business and their area of expertise, translators can help the management team focus on targeted areas like production, distribution, pricing and even cross-functional initiatives.

With self-serve, advanced analytics tools, translators can then identify patterns, trends and opportunities, and problems. This information is then handed off to data scientists and professionals to further clarify and produce crucial reports and data with which management teams can make strategic and operational decisions.

Why is an Analytics Translator Important to Your Organization?

IT resources and data professionals are typically in short supply within an organization and, if the enterprise wishes to increase staff, the cost of these highly skilled professionals can be prohibitive. In the average organization, these resources are usually stretched thin and time is wasted on projects that are:

  • Too complex for business team members
  • Conceived or inappropriate for attention at the data scientist or IT level
  • Comprised of incomplete requirements
  • Required for day-to-day or immediate analysis or data sharing initiatives
  • Tactical or low-level operational in nature

The time it takes for a data professional or IT professional to review the project and assign a priority, will take them away from more strategic or more critical tasks and, in the process, the business user may miss day-to-day deadlines or information that is critical to them. Perhaps, the data professional may need more information on requirements, which will further delay the project. There are many examples of unnecessary or inappropriate data analysis requests and many instances where a business user with access to analytical tools might be able to do the work themselves. But, there are even more examples of projects or analytical requirements that fall somewhere between the skills of a business user and the skills of a trained data scientist and just as many examples of poorly understood or poorly translated data analysis that sends a business user off in the wrong direction.

That is where the Analytics Translator comes in. Using her or his knowledge of the industry, the organization, the team and the analytics tools, the translator can play a crucial role in understanding requirements, preparing data and producing and explaining information in a way that is accurate and clear. As this role evolves within your organization, you will find that, by allowing the average business user to work with the Analytics Translator, that business user will become more knowledgeable and skilled in interpreting and understanding data.

The Ideal Analytics Translator

When identifying possible candidates to perform the Analytics Translator role, the organization should look for skills that can be nurtured and optimized as an asset.

  • A power user of self-serve BI tools
  • Recognized as an expert in a functional, industry or organizational role
  • Comfortable with building and presenting reports and use cases
  • Works well with technical and management teams
  • Manages projects, milestones and dependencies with ease
  • Able to translate analysis and conclusions into actionable recommendations
  • Comfortable with metrics, measurements and prioritization
  • Acts as a role model for user and team member adoption of new processes and data-driven decisions

If this role is recognized as important to the organization, most enterprises will structure a logical program to identify and train candidates to ensure uniform skills and performance.

By combining domain, organizational and industry skills with self-serve analytical tools, the Analytics Translator can help the enterprise to achieve low total cost of ownership (TCO) and rapid return on investment (ROI) for its business intelligence and advanced analytics initiatives and can encourage and nurture data democratization and optimal analytical business results within the organization.

Citizen Data Scientists/Citizen Analysts play a crucial role in day-to-day analysis and decision-making, using self-serve business intelligence tools. Analytics Translators bridge the gap between IT, data scientists and business users, and move initiatives forward by acting as a liaison and topic expert to help the organization focus on the right things to achieve its goals.

As self-serve Advanced Analytics and data democratization becomes more common across industries and organizations, the role of the Analytics Translator will also become more important. As a power-user of BI tools and Self-Serve Analytics, the translator functions as a liaison between critical analytical and technical resources and the business user community and ensures that BI tools will be adopted and shared across the enterprise.

In our next article, we will consider the difference between the Analytics Translator and the Citizen Data Scientist or Citizen Analyst.

Original Post: What is an Analytics Translator and Why is the Role Important to Your Organization?

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Is Smart Data Visualization Really Smart? https://www.smarten.com/blog/what-makes-smart-data-visualization-different/ Fri, 09 Feb 2018 09:33:06 +0000 https://www.smarten.com/blog/?p=5603 Continued]]> What Makes Smart Data Visualization Different?

What’s So Great About Smart Data Visualization?

Exactly what is smart data visualization? Are we now dependent on a computer or a piece of software to decide how we see our data? Have we lost control of our reporting, data sharing and data views? The answer to those questions: a resounding ‘NO’. To understand smart visualization, let’s consider the old, traditional data visualization tools. Traditional data visualization is static and, while it may offer a choice of graphs and displays, it is not interactive. In other words, it is not smart. In the traditional scenario, visual illustrations, graphs, and displays are not useful. They don’t help business users to analyze and understand data or, more importantly, to choose the right method to display and report data for decision-making. The right view of data can make all the difference!

Great Software for Data Visualization allows users to gather various data components and tell a story. Whether you are preparing a presentation or sharing data with a team, the story you tell can illustrate a problem, highlight an opportunity or demonstrate a market shift. Augmented data discovery happens when data visualization tools reveal those all-important nuggets of information and make it easier for business users to reach confident decisions.

Your users must be able to interact with Data Visualization Software using guided visualization and recommended data presentation to illustrate underlying data and issues, and quickly, and efficiently produce the best possible visualization based on data type, volume, dimensions, patterns and nature of data, without technical skills or in-depth training.

Let your users leverage tools that recommend displays, views, and plotting to explore the best possible value from underlying data with better options, formats and views to help them understand data. Users can identify patterns and trends and get valuable insight.

Smart Data Visualization allows your users to visualize and plot for certain types of data, based on the nature, dimensions, and trends inherent in the data, with sophisticated tools in an easy-to-use, drag and drop interface, and NO advanced skill requirement.

If you want your business users to be empowered with tools that will make them more of an asset to the organization, take the Smarten approach with Smart Data Visualization, Contact us and discover the possibilities.

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Explore the Potential of Self-Serve Analytics https://www.smarten.com/blog/advanced-data-discovery-is-easier-and-more-popular-than-ever/ Mon, 05 Feb 2018 14:04:49 +0000 https://www.smarten.com/blog/?p=5599 Continued]]> Advanced Data Discovery Is Easier and More Popular Than Ever

Self-Serve Advanced Analytics is Moving and Growing and Changing!

The Gartner Magic Quadrant is a well-known report and tool both for vendors and for prospective customers. The Gartner Business Intelligence Magic Quadrant offers in-depth analysis of the current market and predictions of where the market is going. There is no doubt that the Business Intelligence market has moved toward Advanced Data Discovery tools and self-serve tools like Self-Serve Data Preparation, and Plug n’ Play Predictive Analysis.

The key to serving this ever-expanding market is to provide tools that are suitable for every business user so that every organization can encourage and sustain and environment where business users can become Citizen Data Scientists. There are important innovations taking place in the self-serve business intelligence market and the evolution of this market is very encouraging and, as business users and organizations embrace these tools, the scope of user creativity has expanded data popularity and resulted in the expansion of social BI.

The Gartner Magic Quadrant for BI and Analytics reflects the changing market and the continuation business user empowerment with Self-Serve Deep Dive Analytics. A user-centric, self-serve approach to business intelligence must also be designed to expand the boundaries of self-serve analytics while ensuring appropriate data governance and security.

To better understand the profound changes in this market and the potential for your organization and business users, it is important to work with an expert team that can help you to achieve your goals and plan for future growth. If you want to dive in and take advantage of the benefits of self-serve solutions, explore the Smarten approach with Data Visualization Tools for Augmented Data Discovery and clear, insightful data visualization.

Contact Us and find out more about the ever-expanding world of self-serve analytics.

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Smart Data Visualization Makes Users Smarter https://www.smarten.com/blog/your-users-will-love-smart-data-visualization-tools/ Tue, 30 Jan 2018 13:29:23 +0000 https://www.smarten.com/blog/?p=5594 Continued]]> Your Users Will Love Smart Data Visualization Tools

Smart Data Visualization Guides Users to Clear Data in the Right View!

Imagine if your advanced data discovery tool could take the guesswork out of data preparation and visualization and allow your business users to see past the obvious to find the true insight and subtle patterns, trends and data that will help your business to achieve success. Features like smart data visualization make it easy to see what is happening, where the source of a problem lies and what market opportunities may be ripe for the taking.

The right Data Visualization Tool incorporates cutting-edge technology on the backend, and an understanding of the user experience on the front end, so your business users get a helping hand and are guided with suggestions and recommendations to tell them how best to view their data and to personalize data displays.

Smart Visualization and Data Visualization Software makes it possible for business users to respond to ever-changing needs and alleviates the problem of the organization trying to anticipate exactly what every user needs on a daily basis.

With tools like Auto-Recommend and Auto Suggest the user can view recommended displays, views, and plotting and find the best way to view and use the data to share information and to make crucial, confident decisions. They can see options, formats and alternate views and make choices without the assistance of a technical team member or a data scientist.

If your users need sophisticated tools in an easy-to-use environment, you will want to explore the Smarten Approach with Smart Data Visualization tools for augmented data discovery and clear, insightful data visualization.

Contact us and discover the possibilities for your business users and your organization.

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What is Clickless Analysis? Can it Simplify Adoption of Augmented Analytics? (Part 1 of 3 articles) https://www.smarten.com/blog/what-is-clickless-analysis-can-it-simplify-adoption-of-augmented-analytics/ Thu, 25 Jan 2018 11:23:16 +0000 https://www.smarten.com/blog/?p=5590 Continued]]> What is Clickless Analysis? Can it Simplify Adoption of Augmented Analytics? (Part 1 of 3 articles)

The concept of Clickless Analytics is one that will be happily embraced by business users and by the business enterprise. The reason is simple! Clickless Analytics allows users to find and analyze information without specialized skills, by using natural language.

In this, the first of a three-part series we discuss Clickless Analytics and how it can simplify user adoption of augmented analytics.

What is Clickless Analytics?

Clickless Analytics incorporates Natural Language Processing (NLP) and takes augmented analytics to the next level with machine learning and NLP in a self-serve environment that is easy enough for every business user. Business users can leverage sophisticated business intelligence tools to perform advanced data discovery by asking questions using natural language. The system will translate that search analytics language query into a query that the analytics platform can interpret, and return the most appropriate answer in an appropriate form such as visualization, tables, numbers or descriptions in simple human language. Clickless Analytics interprets natural language queries and presents results through smart visualization and contextual information delivered in natural language.

Can Clickless Analytics Simplify Adoption of Augmented Analytics?

Clickless analytics, NLP and search analytics provides true data democratization of advanced analytics. Clickless Analytics incorporates NLP within a suite of Augmented Analytics features, leveraging computational linguistics, data mining, and analytical algorithms to provide a self-serve, natural language approach to data analysis. Search Analytics and NLP filters through mountains of data to answer a question in the way a user can understand, thereby simplifying and speeding the decision process and ensuring clarity.

Clickless Analytics suggests relationships and offers insight to previously hidden information so that business users can ‘discover’ subtle, crucial business results, patterns, problems and opportunities. Clickless Analytics provides maximum results and business user access with minimum implementation time and minimal training.

Clickless Analytics and an NLP approach to augmented analytics utilizes a Google-type interface where business users can enter a question in human language, i.e., ‘what is our best selling product in Arizona’ or ‘who is the best performing salesperson in this year as compared to previous year.’ The ease-of-use assures user adoption and the clarity of analysis and reporting achieved by the enterprise results in an environment where the team, managers and executives can achieve rapid, accurate results without the assistance of IT or business analysts.

The evolution of search analytics and the application of NLP search within the confines of a business intelligence solution have allowed the average organization to leap forward with advanced data discovery and the incorporation of these crucial tools into a self-serve environment for user empowerment and accountability. Clickless Analytics and NLP help businesses to achieve rapid ROI and sustain low total cost of ownership (TCO) with meaningful tools that are easy to understand, and as familiar as a Google search. These tools require very little training, and provide interactive tools that ‘speak the language’ of the user.

Clickless Analytics, NLP and Search Analytics are a crucial component of business intelligence and Augmented Analytics, and are essential to business success and to building and sustaining a competitive advantage.

Watch for Part II and Part III of this article series: ‘What is Search Analytics and Can it Improve Self-Serve Data Discovery?’ and ‘What is Natural Language Processing & How Does it Benefit a Business?’

Original Post: What is Clickless Analysis? Can it Simplify Adoption of Augmented Analytics? (Part 1 of 3 articles)

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What is Augmented Data Discovery? https://www.smarten.com/blog/2018/01/15/january-15-2018/augmented-data-discovery/ Mon, 15 Jan 2018 11:32:54 +0000 https://www.smarten.com/blog/?p=5585 Continued]]> Augmented Data Discovery Supports Every User

Wondering if Augmented Data Discovery is for You? Wonder No More!

If you have heard of Augmented Data Discovery (aka Smart Data Discovery), but you don’t quite understand what it is and its value to average business, then we really need to talk!

Augmented Data Discovery allows your business to go beyond data monitoring and helps users discover the more subtle yet crucial factors that affect business success. It identifies hidden issues and patterns within the data so the organization can address challenges, capitalize on competitive and market advantages and plan for the future with more confidence. A well-conceived augmented data discovery solution includes tools and features that are simple enough for business users and allow users with average skills to perform augmented data preparation, drawing data from various repositories to test theories and hypotheses without the assistance of IT or analysts to prepare the data.

Augmented Data Discovery also includes augmented analytical tools that provide data insight. With an advanced augmented data discovery solution, the organization can share, manipulate and present data with clear results and access to sophisticated tools. These tools ensure that the organization makes decisions based on fact rather than opinion and that users get real insight and can act on data quickly and accurately.

With Augmented Data Discovery, the organization can notate and highlight data, share data with other users and most importantly, the enterprise can identify critical ‘ah hah’ data points and information hidden within the data to accurately plan, forecast and solve problems.

Augmented Data Discovery (Advanced Data Discovery) enables gathering, preparation, integration and analysis of data and allows users to address strategic, operational and tactical activities. These easy-to-use tools and solutions provide important support, suggesting relationships, identifying patterns, suggesting visualization techniques and formats, highlighting trends and patterns and helping users and organizations to forecast and predict results.

With augmented data discovery, the enterprise has a simple way to manage data overload and overwhelming workload, and to provide important information and answers to every team member with sophisticated tools in an easy-to-use, drag and drop interface, which requires no advanced skill or knowledge of statistical analysis, algorithms or techniques.

When you understand the value proposition inherent in these tools and the way this type of solution supports business users with sophisticated, easy-to-use tools, it becomes clear just how important this type of analytical tool is to your business. If you want to take advantage of augmented data discovery tools, you can start here: Augmented Data Discovery Tools

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Self-Serve Data Prep Puts the Power in Hands of Your Users https://www.smarten.com/blog/augmented-data-preparation-is-in-your-future/ Wed, 10 Jan 2018 13:45:42 +0000 https://www.smarten.com/blog/?p=5580 Continued]]> Augmented Data Preparation is in Your Future!

Self-Serve Data Preparation is Easy as Pie!

Data preparation tools come in many shapes and sizes and not all are equal. If your business is interested in implementing a self-serve environment where its business users can become a greater asset to the organization, and use data preparation for analytics, you definitely need to consider ease-of-use for your users and flexibility, sophisticated functionality and mobile accessibility for the good of the organization.

When you adopt the right self-serve BI tools, your users can enjoy elegant Self-Serve Data Preparation and ETL for business users. ETL (AKA extraction, transformation and loading) solutions assist the average user and make the process of gathering, compiling, sorting and managing data a much easier task. These self-serve tools allow your business users to engage in ETL without the assistance of IT staff or a data analyst, so that they can see, use and share data more quickly and easily, and understand what that data is illustrating and what decision is best for the organization.

Augmented Data Preparation provides access to crucial data and allows you to connect to various data sources – personal, external, cloud, and IT provisioned You can mash-up and integrate data from disparate data sources and view it in a uniform, interactive display. Your users have access to clear data and your organization can balance agility with data governance to ensure that data security and data access are appropriate and well managed.

Take the Smarten Approach to Self-Serve Data Prep and see how easy data preparation software can be.

Contact us and find out how you can reap the benefits of these tools.

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Self-Serve Data Preparation Doesn’t Mean Traditional ETL is Dead! https://www.smarten.com/blog/self-serve-data-preparation-does-not-mean-traditional-etl-is-dead/ Thu, 04 Jan 2018 08:30:42 +0000 https://www.smarten.com/blog/?p=5574 Continued]]> Self-Serve Data Preparation Doesn't Mean Traditional ETL is Dead!

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting. It offers high quality data, which otherwise resides in poorly structured heterogeneous, complicated data sources.

Think of your data warehouse as an active repository that is ever changing as new data sources keep on getting added and existing data sources keep on getting updated. In order to manage the environment, an organization must dedicate resources to monitor and track ETL process, its data flow, data integration and data updates.

It is no wonder that the average enterprise is cautious when any suggestion is made to change the process they have in place. So, the idea of data access by business users may cause concern, and the IT staff may wonder whether this access signals the end of the true ETL process along with the comprehensive maintenance and data governance policies.

But, before the organization discounts the democratization of data and the concept of Self-Serve Data Preparation, it is important to understand the need for (and benefits of) comprehensive ETL policies and maintenance AND the flexibility, agility and accessibility provided by business user access to self-serve data preparation tools.

When an enterprise provides a solid ETL foundation with appropriate monitoring, management, integration processes and skills and adds the flexibility of self-serve data preparation for business users, the organization can acquire good data quickly and offer insight to business users and ultimately to the entire business organization. This approach ensures that the users and the organization can make better business decisions, and make them more quickly. By combining traditional ETL, data warehouse management and technical skills with self-serve data preparation and business user access to ETL and cube management, your organization can balance and optimize quality vs agility to create an agile analytical environment.

Let’s look at the benefits of, and the need for, traditional ETL as well as self-serve data preparation performed by business users.

Give the Power to Business Users

When an organization invests in a business intelligence solution that provides flexible options for data access and management, users can choose the option that is best for a use case scenario. They can use self-serve data preparation tools to connect to data sources like databases, OLAP cubes and spreadsheets using simple wizard based connection interface.

Today’s business users and managers face the daunting task of compiling and analyzing data simply and easily and using that data to make confident decisions. Users need a scalable, high-performance solution that can integrate data from data warehouses, data marts, ERP, best-of-breed applications, CRM and any number of operational system databases, spreadsheets, cloud based data sources and legacy systems and leverage Deep Dive Analytics and self-serve data preparation to compile and analyze data quickly and easily.

The key focus in this scenario is agility. When business users need information they don’t always need exacting, detailed data extraction and analysis. Many times, they are testing a hypothesis or creating a prototype to analyze options or to see how things will play out in a particular set of circumstances, and in those cases, 100% accuracy of data is not necessary. By providing access to data and simple data preparation tools, the organization can keep things moving and allow for empowerment and creativity.

Preserving Traditional ETL

Data Extraction, Data Transformation and Data Management solution provides the foundation for an enterprise to extract, load and transform (ETL) data. While it is important to provide business users with access to sophisticated, easy-to-use tools, the foundational work of ETL must be preserved.

If the integrity of enterprise data and data integration is to be preserved, the data warehouse environment must integrate, and consolidate data from a disparate group of systems and allow for data extraction and compilation across the enterprise. Highly skilled, trained professionals manage these systems with a thorough understanding of the source of the data and how to manipulate and report on that data to create complex reports and provide support for business users and managers.

When combined with in-depth IT and analytical knowledge, this complex data environment and resource pool provides crucial support to refine and cleanse data and to ensure that only the necessary data is extracted and analyzed so that critical resources are not wasted and data is not misinterpreted. There are times when an organization must look to these foundational processes and team members to clarify data and to ensure that hardware, networks, and data sources come together to optimize extraction and reporting and assure appropriate interpretation.

Traditional ETL performed by experts produces great data quality at the right level of aggregation to ensure stable performance and integrity of data governance. Because these tasks are performed by IT certified professionals, the organization can be sure of the reliability and quality of the data. But these resources should be optimized to optimize the special skill set and knowledge of these team members and to prevent competing priorities, and delays in crucial data analysis and timeliness.

By combining the traditional ETL processes and skills with business user access to self-serve data preparation tools, your organization can optimize skilled resources and move quickly to addresses opportunities, challenges and trends.

When the organization needs very precise, comprehensive ETL, your trained team can provide those services. When a business user requires information, but does NOT require exacting precision, self-serve data preparation tools can speed decisions and empower users to analyze data for day-to-day tasks and discussions.

The rationale for any organizational or process change is always the bottom line. When you balance self-serve data preparation with traditional ETL processes and skills, you get the best of both worlds and improve resource optimization and the bottom line. By balancing speed and agility with quality and data governance, the organization can save its skilled resources for the most important projects and business users can prepare and analyze data for those projects and decisions that do not require high-level, dedicated attention.

Original Post: Self-Serve Data Preparation Doesn’t Mean Traditional ETL is Dead!

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Are You Ready for Simpler, Smarter Data Discovery? https://www.smarten.com/blog/augmented-data-discovery-is-simpler-than-it-sounds/ Mon, 18 Dec 2017 12:59:54 +0000 https://www.smarten.com/blog/?p=5562 Continued]]> Augmented Data Discovery Is Simpler Than it Sounds

Advanced Data Discovery for Every Business User. No Special Skills Required!

There are many facets to Advanced Data Discovery. The processes are sophisticated and the algorithms are exacting, but your business users need not worry about those things. To make the best use of augmented data discovery tools, you need to implement an advanced analytics solution that gives your team sophisticated tools and true insight into data, with flexible, personalized dashboards, and reporting that are easy enough for the average user to adopt.

With tools like smart data discovery, self-serve data preparation, smart visualization and plug n’ play predictive analysis, the average user can answer questions, gain insight, address the root cause of challenges and capitalize on business opportunities  all without the assistance of IT staff or data scientists.

The right Augmented Data Discovery tool allows users to leverage a drag and drop interface, with no advanced skill requirement for statistical analysis, algorithms or technical knowledge, so they can gather, prepare, integrate and analyze data, find patterns and trends, share findings and apply to strategic, operational and tactical activities. With smart data discovery, your users can enjoy the benefits of a truly intelligent solution that suggests relationships, identifies patterns, suggests visualization techniques and formats, highlights trends and patterns, and presents predictions. These auto-suggestions and recommendations allow users to explore data and reveal subtle, important actors and considerations that will change the landscape of your business.

You can explore the Smarten BI Advanced Data Discovery and find out how to make your business users more productive, accountable and empowered.

Contact us and find out how easily and quickly your business can implement these tools in your organization.

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Predictive Analytics Can Guide the Organization to Success https://www.smarten.com/blog/is-assisted-predictive-modeling-right-for-my-business/ Fri, 15 Dec 2017 08:54:47 +0000 https://www.smarten.com/blog/?p=5557 Continued]]> Is Assisted Predictive Modeling Right for My Business?

Plug n’ Play Predictive Analysis for Accurate Forecasting!

There are numerous considerations when a business looks at upgrading or acquiring an analytical solution. One very important capability is Put n’ Play predictive analysis. Assisted Predictive Modeling and predictive analysis tools should include sophisticated functionality in a simple environment that is easy for every business user.

Organizations should carefully review solutions to ensure that the prospective solution includes predictive algorithms (associative, decision trees, classification, clustering and other techniques) that will allow business users to explore hypothesis and assumptions using Advanced Data Discovery and provide recommendations to perform early prototyping, and drastically reduce the time and cost of analysis and experimentation.

Predictive Analytics is crucial to forecasting and predicting the future of its market, and a business operation, as well as market competition, and customer buying behavior. Plug n’ play predictive and forecasting tools allow an enterprise to transform business users into Citizen Data Scientists by enabling users with sophisticated predictive algorithms and allowing these users to leverage the tools without the expertise and skill of a trained data scientist.

When users are given dynamic, powerful tools, it is easier for them to achieve goals, share information, make confident decisions, understand trends and quickly adjust processes and activities to achieve the best results.

Explore the Smarten approach to plug n’ play predictive analytics and take the shackles off your business users.

Contact us and find out how easily and quickly your business can implement these tools in your organization.

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Uncover the Data ‘Nuggets’ with Smart Visualization https://www.smarten.com/blog/full-insight-for-business-users-with-smart-visualization/ Wed, 13 Dec 2017 07:01:24 +0000 https://www.smarten.com/blog/?p=5553 Continued]]> Full Insight for Business Users with Smart Visualization

Smart Data Visualization Helps Your Users to Gain Insight!

Organizations that are currently considering and upgrade or a new business intelligence solution, should focus on advanced analytics capability that is simple enough for its business users to improve accountability and empowerment and cascade information throughout the organization to improve planning, results and competitive advantage. Included in this solution is Smart Data Visualization. This feature suggests the best options for visualizing and plotting for a particular set or type of data, based on the nature, dimensions and trend of data. Tools like these can allow the average business user to leverage sophisticated tools without the knowledge and skill of an IT pro or a data scientist.

Smart visualization and augmented data discovery tools allow users to gather data from various sources clarify a problem, identify an opportunity or make a decision. To complete this type of analytics, the data visualization tool must be flexible and interactive enough to allow for guided visualization and recommend the data presentation technique that will best illustrate data, trends, patterns, challenges and opportunities.

Smart Visualization allows the enterprise and its users to select and display data in a way that assures accurate interpretation and assumes no technical skill or need for in-depth training. Smart Data Visualization allows your business users to analyze, share and present information without waiting for technical or data visualization expert assistance.

Take the Smarten approach to Smart Visualization. Contact us and find out how we can help your business and your users to leverage these dynamic, powerful tools with easy-to-use, cost-effective solutions that are quick to implement.

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Augmented Data Discovery Provides Users with Crucial Answers https://www.smarten.com/blog/automated-analytics-tool-for-user-and-business-success/ Mon, 11 Dec 2017 11:29:02 +0000 https://www.smarten.com/blog/?p=5548 Continued]]> Automated Analytics: A Tool for User AND Business Success

Advanced Data Discovery Can and Should Be Available to All!

Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist. Advanced Data Discovery ensures data democratization and can drastically reduce the time and cost of analysis and experimentation.

With the right Data Discovery Software, business users have the freedom to imagine and leverage data without limiting the user’s ability to analyze and visualize data with restricted views and dashboards.

The concept of advance analytics and advanced data discovery can and should include augmented analytics and augmented data discovery. These tools are designed to simplify the user experience by providing easy-to-use guides and sophisticated (yet simple) tools that will lead the user to use the right technique for analysis and to present the data they are analyzing in the right format to make the results as clear as possible.

Advanced Data Discovery allows business users to quickly and easily prepare and analyze data and to visualize and explore data, notate and highlight data and share data with others. Business users can use Advanced Data Discovery to identify the important ‘nuggets’, buried in traditional data, and to connect the dots, find exceptions, identify patterns and trends and better predict results. A business user with average skills can do all of this without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

The Smarten approach to advanced data discovery can help your organization and business users with simple tools that are cost-effective and easy to implement and use.

Contact us and find out how easily and quickly you can satisfy your analytical needs.

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What is Self-Serve Data Preparation and How Can It Support Business Users? https://www.smarten.com/blog/what-is-self-serve-data-preparation-and-how-can-it-support-business-users/ Thu, 07 Dec 2017 14:14:23 +0000 https://www.smarten.com/blog/?p=5542 Continued]]> What is Self-Serve Data Preparation and How Can It Support Business Users?

Self-Serve Data Preparation is the next generation of business analytics and business intelligence. Self-serve data preparation makes advanced data discovery accessible to team members and business users no matter their skills or technical knowledge.

What is Self-Serve Data Preparation?

In the past, preparing data for analysis was a time-consuming process, a task that was relegated to the IT team and involved complex tasks like Data Extraction, Transformation and Loading (ETL), access to data warehouses and data marts and lots of complicated massaging and manipulation of data across other data sources. Today’s organization does not have the time or the money to provide IT resources or to satisfy the day-to-day data inquiry requirements of its ever-changing organization and its business market.

Enter Self-Serve Data Preparation, a set of sophisticated tools, designed for ease-of-use and access by business users in a self-serve environment. Self-Serve Data Prep provides business users with powerful capabilities to explore, manipulate and merge new data sources – all without the assistance of IT staff.

Self-service data preparation solutions make it possible for business users to access data, integrated from multiple sources and to prepare that data using drag and drop features and a simple, intuitive interface. Users can perform data preparation, test theories and hypotheses, and prototype to test price points, analyze changes in consumer buying behavior, anticipate changes in the competitive landscape and otherwise leverage data for analytical purposes, all without the assistance of IT or analysts.

Self-Serve Data Preparation solutions provide tools that are flexible so the user is not restricted to dashboards or interfaces that are designed by someone else. The user can use the power of self-serve data preparation to compile and prepare data, test hypotheses, visualize and share data, drill-down and drill-through data using selected data elements to prepare for and execute analysis.

How Can Self-Serve Data Preparation Support Business Users?

The best way to support business users is to give them tools that are flexible enough to be personalized to their needs, their role and the issues of the day. No matter how hard IT teams and analysts try, they cannot anticipate every possible requirement and the new, rapidly changing business landscape of today makes it impossible for business users to know what they will need tomorrow.

A May 24, 2016 Gartner report (ID G00274731) , entitled ‘Embrace Self-Service Data Preparation Tools for Agility, but Govern to Avoid Data Chaos’, offers the prediction that, ‘By 2017, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis.’

As the market shifts and more and more organizations adopt these crucial tools, it must be noted that, those businesses that do not take advantage of self-serve data preparation are likely to fall behind the competition and the market.

With Self-Serve Data Preparation tools business users can gain insight into buying behavior, analyze supply chain issues, identify new markets, locations and opportunities and anticipate resource and training needs using selected data to easily illustrate results and uncover patterns and trends.

With self-serve data prep, business users can avoid the delays and out-of-date, inaccurate reports that sometimes result from incomplete requests or overworked IT teams. The business users gets access to data from numerous data sources and can easily select data, execute searches and analyze data to make timely, accurate decisions. The organization achieves impressive ROI and TCO, and better business results, and the business user is empowered to get the job done right.

Supporting business users with powerful tools that are meaningful to their role and goals is critical to every organization. Self-Serve Data Preparation takes the complexity out of the data prep and analytical process and results in better data discovery.

Original Post: What is Self-Serve Data Preparation and How Can It Support Business Users?

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Assisted Predictive Modeling Guide Users Through the Maze https://www.smarten.com/blog/can-assisted-predictive-modeling-work-for-business-users/ Mon, 04 Dec 2017 12:58:21 +0000 https://www.smarten.com/blog/?p=5538 Continued]]> Can Assisted Predictive Modeling Work for Business Users?

What is Assisted Predictive Modeling?

Anything that can help your business users to understand, interpret and analyze data is a help! Users have many tasks to perform in a given day and while analysis may not be their forte, they definitely need clear, concise data to share, to make decisions and to see opportunities, challenges and patterns.

To be a positive asset to the business, your business users must be able to accurately plan and forecast everything from budgetary needs to team members and resources, new suppliers, new locations, new products, etc.

Assisted Predictive Modeling is a great way to provide support for your users and your organization. Users get sophisticated tools and algorithms and suggested techniques, and formats, to accurately analyze and understand data in a way that is meaningful to their role and to the type of data and audience with whom they will share that data.

Assisted Predictive Modeling and Predictive Analysis Tools are key to swift analysis and a comprehensive understanding of data across the enterprise, in every department and business unit. Yes, plug n’ play predictive analysis must truly be plug and play! It must not require attention from IT or a data scientist. If you want your business users to adopt and use these tools, you must select predictive analytics tools that are simple; tools that will quickly reveal the trends and allow users to plan and forecast with precision and accuracy. Predictive analysis does not have to be tortuous or confusing.

The Smarten approach to assisted predictive modeling and plug n’ play predictive analytics allows users to forecast and predict results with true insight into the business and the market.

Contact us and find out how easily and quickly you can satisfy your data preparation and data analysis needs.

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What is Advanced Analytics and How Can it Advance Your Organization? https://www.smarten.com/blog/what-is-advanced-analytics-and-how-can-it-advance-your-organization/ Wed, 22 Nov 2017 11:20:16 +0000 https://www.smarten.com/blog/?p=5532 Continued]]> What is Advanced Analytics and How Can it Advance Your Organization?

The term ‘Advanced Analytics’ can seem daunting to the average business user and even to the average business enterprise but the process is not as complex as it may seem.

What is Advanced Analytics?

Advanced Analytics is comprised of numerous sophisticated analytical techniques, designed to parse, explore and analyze data and produce results to support business decisions. Fortunately, today’s new self-serve business intelligence solutions allow for ease-of-use, bringing together these varied techniques in a simple interface with tools that allow business users to utilize advanced analytics without the skill or knowledge of a data scientist, analyst or IT team member. Advanced Analytics provides a 360 degree view of data from data marts, data warehouses, best-of-breed, legacy systems and other data sources. The analytical techniques and algorithms are designed to identify patterns and trends, pinpoint issues and the root cause of these issues and allow the enterprise to capitalize on opportunities and accurately plan for the future.

The concept of Advanced Analytics and cutting-edge BI solutions has evolved to include IT-enabled analytic processing and data manipulation and management, built on a foundation of flexible, scalable architecture that allows business users to perform in-depth analysis across the entire spectrum of analytical tasks from access to integrated data from multiple sources to data preparation, analysis, drill-down, reporting and sharing.

Self-Serve Advanced Analytics provides a roadmap to guide you through a maze of organizational data with tools and models that will bring together data from disparate sources and enable sophisticated yet simple analytics. These tools allow the enterprise to establish and monitor key metrics and to objectively assess results. But, perhaps the most important aspect of advanced analytics is the flexibility of these tools and the ability to go beyond restricted reporting or standardized analytical reports and processes to design and execute unique analysis that will address a particular issue or question for a specific project, or to address a newly discovered problem or question. These self-serve tools allow business users to dive deep into data and enable analysis and reporting that is as unique and creative as the user.

How Can Advanced Analytics Advance Your Organization?

With the right advanced analytics solution, your enterprise does not need advanced analytical skills. The advent of new and improved self-serve business intelligence solutions means that advanced analytics is easier than ever for the average business user to adopt and leverage. Your business users can perform advanced analytics, using sophisticated tools in an easy-to-use, drag and drop interface, with no advanced skill requirement for statistical analysis, algorithms or technical knowledge. Empowering your users to gather, prepare, integrate and analyze data, find patterns and trends, share findings and apply to strategic, operational and tactical activities means that users are more accountable for results and that the organization can more easily cascade objectives and goals and allow users to see and understand how their role and activities affects these objectives and goals. Your enterprise can go beyond data monitoring to ‘discover’ subtle and important factors that will identify issues and patterns, and help the organization capitalize on opportunities.

Advanced Analytics allows the organization to leverage Advanced Data Discovery features and enjoy the benefits of a truly intelligent solution that suggests relationships, identifies patterns, suggests visualization techniques and formats, highlights trends and patterns, and presents predictions.

Tools like Plug n’ Play Predictive Analysis, Smart Visualization and Self-Serve Data Preparation allow the enterprise to transform the average business user into a Citizen Data Scientist and to better plan and forecast and make confident decisions. Your organization can test theories and hypotheses in a risk-free environment without making a market misstep and predict the outcome of pricing changes, new product introductions, supplier changes, added locations and other crucial proposed changes. With the power to test competitive and market strategies, management and business users can see and leverage the hidden value within the organization and find the small problems before they become large issues.

In the Gartner report entitled, ‘Technology Insight for Modern Analytics and Business Intelligence Platforms’, published on September 12, 2017 (ID: G00331857), Gartner analysts predicted that, by 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be automatically generated.

Clearly, the trend toward self-serve Advanced Analytics is going to build in popularity as organizations recognize the significant benefits to the business user and the enterprise.

Advanced Analytics is an invaluable set of tools to help the enterprise engage team members, resolve issues and capitalize on market and competitive opportunities using factual, objective metrics and data for strategic, operational and tactical results.

Original Post: What is Advanced Analytics and How Can it Advance Your Organization?

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Predictive Analytics for Business Users https://www.smarten.com/blog/2017/11/15/november-15-2017/predictive-analytics-for-business-users/ Wed, 15 Nov 2017 06:45:06 +0000 https://www.smarten.com/blog/?p=5528 Continued]]> Assisted Predictive Modeling Is Easier than it Sounds

Leave it to the Software! Predictive Analytics for the Faint of Heart!

Assisted Predictive Modeling, Predictive Analytics. Those terms strike terror in the hearts of most business users, and that is understandable. These mysterious-sounding terms sound complicated and lead business users to imagine confounding, confusing algorithms, and endless strings of numbers.

What business users want is to find the cause of a problem, discover an opportunity that will help the business, and solve problems. They don’t want to have to try to unravel the complicated world of data analytics and be forced to choose forecasting techniques or predictive models. Your business users aren’t data scientists or analysts. They have a role to play in the organization, and that role increasingly requires them to understand data.

If you give your business users the right predictive analytics tools, if you give them true Plug n’ Play Predictive Analysis, they will be forever grateful and, more importantly, your organization will reap the benefits with improved decisions, and better business results.

Predictive analytics for business users should provide guides to auto-suggest analytical techniques, and reports and graphical displays that will best accomplish the user goals by looking at the type of data and other factors. So your users don’t have to be data scientists! They can depend on the advanced data discovery solution to provide suggestions and recommendations with built-in algorithms to do the heavy lifting!

Why burden your business users with overwhelming tools or tasks that require them to go beyond their professional skills or knowledge in order to get the answers you need to improve your business and challenge your competitors? It should all be so much simpler than that, and it can be! Plug n’ play predictive and forecasting tools help businesses to create Citizen Data Scientists by enabling the average business user to leverage sophisticated predictive algorithms without the expertise and skill of a trained data scientist, so users who are not statisticians or predictive algorithm experts, can leverage self-service plug n’ play predictive tools to achieve their goals.

It’s time for you to simplify predictive analysis and make it accessible to your business users. Plug n’ Play Predictive Analysis

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Smart Data Discovery: A Treasure Map to Success https://www.smarten.com/blog/advanced-analytical-tools-that-are-smart-and-simple/ Tue, 07 Nov 2017 11:44:53 +0000 https://www.smarten.com/blog/?p=5523 Continued]]> Advanced Analytical Tools that are Smart and Simple

Smart Data Discovery Will Guide Your Users Through Advanced Analytics!

Your enterprise and business users do not have the time or the inclination to sift through complex data or wade through instructions to learn how to use complicated business intelligence or advanced analytics tools. They don’t want to document requirements and wait for IT or analysts to produce a report (only to find out that the data is incomplete or that the data was delivered too late to make a confident decision).

What your organization needs is advanced data discovery that is easy enough for every business user with sophisticated tools that will guide them through the analysis process and suggest data analysis techniques, and report formats that clearly display the data. These Advanced Data Discovery tools produce high-quality results that allow users to discover crucial nuggets of information and to identify trends and patterns that would otherwise be hidden in mounds of data.

Done right, data discovery should result in just that: discovering data. The word discovery means ‘the act or an instance of discovering’. It doesn’t mean confusing numbers, nor does it mean pretty pictures that tell you nothing. Discovery allows one to learn, adapt and share, and Smarten BI.

Smarten Business Intelligence is the smarter way to analyze data. Give your business users the right tools to gather, prepare and present data, with auto-suggest options and recommendations to make analysis and report formats simpler and more concise.

Contact us and find out how easily and quickly you can satisfy your most complex advanced analytics needs.

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Advanced Data Discovery and Augmented Analytics: Simple, Sophisticated Tools for Business Users https://www.smarten.com/blog/advanced-data-discovery-and-augmented-analytics-simple-sophisticated-tools-for-business-users/ Fri, 03 Nov 2017 07:30:06 +0000 https://www.smarten.com/blog/?p=5519 Continued]]> Advanced Data Discovery and Augmented Analytics: Simple, Sophisticated Tools for Business Users

The world of business analytics has changed dramatically in the past few years. If your business is looking to upgrade BI tools or to begin implementing an analytics solution, the solution must be user friendly for business users. The tools exist today for augmented analytics, augmented data discovery, self-serve data preparation and other features and modules that provide sophisticated functionality and algorithms in an easy-to-use dashboard and environment that is designed to support business users, as well as data scientists and IT staff.

The concept of Advanced Data Discovery allows business users to leverage advanced analytics and helps the organization to create Citizen Data Scientists. Business users can quickly and easily prepare and analyze data and visualize and explore data, notate and highlight data and share data with others to identify the important ‘nuggets’, buried in traditional data, and to connect the dots, find exceptions, identify patterns and trends and better predict results.

While Augmented Analytics may seem too advanced for business users, the key to user adoption, rapid return on investment (ROI) and low total cost of ownership (TCO) is to select tools that are specifically designed to support data and analytical sharing, auto-recommended report formatting and auto-suggested analytical techniques based on the type of data the user is considering.

These tools should include:

  • Self-Serve Data Preparation – Allows business users to perform Advanced Data Discovery and auto-suggests relationships, reveals the impact and importance of key factors, recommends data type casts, data quality improvements and more!
  • Smart Visualization – Smart Data Visualization, suggests the best options for visualizing and plotting for a particular set or type of data, based on the nature, dimensions and trend of data.
  • Plug n’ Play Predictive Analysis – Assisted Predictive Modeling and predictive algorithms (associative, decision trees, classification, clustering and other techniques) lets business users explore hypothesis and assumptions using Advanced Data Discovery and recommendations to perform early prototyping, and drastically reduce the time and cost of analysis and experimentation. Empowers business users with access to meaningful data to test theories and hypotheses without the assistance of data scientists or IT staff.

The benefit of auto-suggestion and auto-recommendation is easy to understand. Business users are more likely to leverage these tools and to gain valuable insight and make confident decisions and predictions if they can use tools that support average skills and do not require advanced technical or analytical experience and knowledge.

To be successful in business, every organization must find a way to accurately forecast and predict the future of its market, and its internal operations, and better understand the buying behavior of its customers and prospects. Advanced Data Discovery and Augmented Analytics tools help businesses to create Citizen Data Scientists by enabling the average business user to leverage sophisticated tools without the expertise and skill of a trained data scientist. These tools are necessary to business success in today’s market.

Original Post: Advanced Data Discovery and Augmented Analytics: Simple, Sophisticated Tools for Business Users

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Smarter, Better, Faster Data Discovery https://www.smarten.com/blog/2017/10/25/october-25-2017/advanced-data-discovery/ Wed, 25 Oct 2017 10:47:33 +0000 https://www.smarten.com/blog/?p=5514 Continued]]> Business Users Want MORE: Simple, Advanced Data Discovery

The Next, Even Better Gift: Advanced Data Discovery Tools!

Have you ever noticed that when you give someone something, they often want more? This is especially true when they really like what you gave them! As the concept of business analytics becomes more main stream and business users embrace the possibilities, they (and their managers) want and expect even more tools and more potential. They want better solutions. BUT, don’t make the mistake of interpreting the need for more sophistication and detail as a need for more complexity. Business users are still business users and, as organizations pursue the natural progression of business analytics, the emergence of  Advanced Data Discovery enables those same business users to enjoy the benefits of more sophistication, more possibilities and more data sharing without the need for IT staff or data scientist involvement.

Happily, Advanced Data Discovery Tools have evolved, and with this natural progression, business users can now leverage advanced analytics and become true Citizen Data Scientists. Advanced Data Discovery allows business users to quickly and easily prepare and analyze data and visualize and explore data. They can notate and highlight data, share data with other users and most importantly, they can identify those crucial ‘ah hah’ nuggets of information that are buried in the mountains of traditional data within every organization.

My team uses augmented data preparation and self-serve data prep, and tools like smart data visualization, plug n’ play predictive analysis and assisted predictive modeling to connect all the dots, find those all-important exceptions, and identify patterns and trends so they can get ahead of the game and help us in the market.

And, the best part is that a business user with average skills can do all of this without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

It’s really no surprise that they want more! After all, when you can make their job easier and make them more successful, they will always adopt the tools and jump in with both feet. Don’t rest on your laurels! Give your users (and your enterprise) the gift of advanced data discovery and watch them shine! Advanced Data Discovery

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Self-Service Data Prep: A Tool for Business Users https://www.smarten.com/blog/self-serve-data-preparation-makes-data-analysis-easy-fast/ Fri, 29 Sep 2017 12:25:29 +0000 https://www.smarten.com/blog/?p=5508 Continued]]> Self-Serve Data Preparation Makes Data Analysis Easy & Fast

Self-Serve Data Preparation Makes Business Users More Productive and Efficient!

There is too much to do in a day to wait for someone else to give you the data you need. But, when it comes to data preparation and data analytics, business users are usually not comfortable with complex solutions that are time consuming or that require professional analytical skills. You pay your team members to have a certain type of expertise and that doesn’t always include technology or analytical knowledge. So, if you want them to be a real asset to the organization, you have to give them easy-to-use self-serve data preparation tools. Self Serve data prep allows business users to quickly gather and prepare data for analysis and to answer questions and solve problems without delay.

Your business users know what they need to know but it isn’t always easy to get that information. Data preparation tools that are suitable for the average business user will move your organization along at a faster pace with the right information so you are making the right business decisions. Self-Service data prep must be intuitive, mobile and flexible so that business users can leverage data to find problems, create solutions, identify patterns and trends and move departments, teams and the organization forward. Don’t sacrifice sophistication for ease-of-use. You can have both. Business users can enjoy data preparation and connect to various data sources to mash up and integrate data, and merge data in a single, uniform, interactive view with smart suggestions and auto-suggested relationships for JOINS, type casts, hierarchies and more. Users can clean, reduce and clarify data using sophisticated algorithms in an easy-to-use environment that is agile and provides accurate, dependable data governance to satisfy the IT team.

The Smarten approach to self-serve data preparation allows you and your users to leverage sophisticated analytical techniques without the assistance of an analyst or an IT professional. If you want to give your business users the right tools to gather, prepare and present data, contact us and find out how easily and quickly you can satisfy your data preparation and data analysis needs.

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Smart Data Visualization Walks You Through to Success https://www.smarten.com/blog/smart-data-visualization-helping-hand-for-business-users/ Tue, 26 Sep 2017 12:25:38 +0000 https://www.smarten.com/blog/?p=5504 Continued]]> Smart Data Visualization: A Helping Hand for Business Users

Take the Guesswork Out of Analytics with Smart Data Visualization!

Smart data visualization takes the guesswork out of data analysis. Why ask your business users to use cumbersome, difficult tools to analyze data or expect them to wait for professional analysts or IT staff to satisfy their analytical needs. They have a job to do and you hold them accountable for results but if they don’t have the right data visualization tools, they can’t get the most out of data.

The key is to give your users Smart Data Visualization that will offer recommendations on how to present and visualize data, suggesting the best options for visualizing and plotting for a particular set or type of data, based on the nature, dimensions and trend of data, based on the type of data the user is analyzing and on other factors. The software for data visualization should do the work!

Smart Visualization tools allow users to gather various data components and tell a story that will clarify a problem, identify an opportunity or help to make a decision. In order to build and tell that story, the business user must be able to interact with their analytics software and build the story through guided visualization and recommended data presentation to best illustrate the underlying data and issues. Business users can quickly and efficiently produce best possible visualization of underlying data based on data type, volume, dimensions, patterns and nature of data.

Traditional data visualization is static and, while it may offer a choice of graphs and displays, it is not interactive and is not ‘Smart’ enough. In the traditional scenario, visual representations are not useful in helping users to analyze and understand data or choose the right method to display and report that data for decision-making. Smart Visualization allows the enterprise and its users to select and display data in a way that assures accurate interpretation and assumes no technical skill or need for in-depth training.

If you want to take the Smarten approach and leverage smart data visualization tools, we encourage you to contact us for more information.

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Predictive Analysis Tools are Not Just for Analysts https://www.smarten.com/blog/predictive-analytics-puts-you-ahead-of-the-competition/ Tue, 19 Sep 2017 10:51:59 +0000 https://www.smarten.com/blog/?p=5499 Continued]]> Predictive Analytics Puts You Ahead of the Competition

Plug n’ Play Predictive Analytics Solutions for Every Business User!

Predictive analysis is very important to your organization. If you can dependably predict and forecast results, you can stay ahead of the competition, address changing customer needs, capitalize on opportunities and solve problems before they negatively impact your enterprise.

But, your market is no different than any other market. Things are changing fast and you don’t have the time or the money to hire additional professional analytical resources and wait for those limited resources to satisfy the daily, changing needs of your user base. If you are to fully leverage predictive analytics, you must provide easy-to-use assisted predictive modeling tool. These plug n’ play predictive analytics solutions allow your business users to dive in and participate in predictive analytics. Your business users do not need to be professional analysts in order to take advantage of these predictive analytics tools so user adoption and user satisfaction is never an issue. This predictive analytics software is fast and easy to implement and provides impressive ROI and TCO.

Most importantly, out-of-the-box predictive analysis tools are flexible so expanding the user base, and growing with the natural ebb and flow of the organization is not a problem. Business users will become Citizen Data Scientists who can find and analyze information on a daily basis to solve problems and create meaningful reports to share with other users so the organization is more empowered and accountable and every team member in every role can see how their goals fit and support the overall enterprise objectives.

Your team members have a lot of things to juggle in a day. Don’t ask them to use tools that are cumbersome or frustrating. Give them predictive analysis tools that are mobile, easy to use and provide plug n’ play predictive analytics to identify trends and patterns and help users to discover those unique ‘nuggets’ of information that will help the organization rise about the competition.

We encourage you to take the Smarten approach to Plug n’ Play Predictive Analytics. Contact us to find out how!

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Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed? https://www.smarten.com/blog/advanced-analytics-if-you-dont-know-what-you-need-how-can-you-succeed/ Fri, 15 Sep 2017 08:34:15 +0000 https://www.smarten.com/blog/?p=5495 Continued]]> Advanced Analytics: If You Don't Know What You Need, How Can You Succeed?

Every enterprise is talking about Business Intelligence and Advanced Analytics. Every enterprise has considered the benefits of implementing self-serve analytics across the organization and involving business users in the process. Panel discussions at technology conferences and hallway conversations among executives and IT staff encourage the trend and create anxiety within the organization that has not yet embraced advanced analytics at the grassroots level.

But, before your organization selects and deploys a solution, there are numerous important considerations. Choosing and implementing a solution for advanced analytics and augmented data discovery is not as simple as buying team t-shirts for your company baseball team. If you do not take the time and effort to do it right, your enterprise may spend a lot of money and time on a solution that reaps little to no benefit.

Don’t become a failure statistic! Ask yourself this: If you don’t know what you need, how can you succeed?

Requirements Planning for Data Analytics

Many organizations are so anxious to get into analytics that they fail to consider the depth and breadth of their needs. While it is true that advanced analytics can help every type and size of business, it is important to remember that YOUR organization is not like any other enterprise. Take the time to develop detailed requirements that consider business user skills, use cases for day-to-day analytics needs and for strategic use, the need for mobile access, scalability, data source integration and other needs. Will you need the flexibility to customize or personalize dashboards? What kind of training or guidelines will your organization need in order to access and leverage the analytics solution and provide the kind of results and user adoption you expect? What kind of statistical data, report capability and security will you need? How will you manage growth?

Curated Data Provides Answers, NOT More Questions

Nearly every organization is overwhelmed by the volume of data and the number of disparate sources and data structures it must manage. Your business users, data scientists and IT staff NEED data, but they need the right data and they need to easily integrate and manage that data if they are to use it to their advantage. One of the crucial success factors for advanced analytics is to ensure that your data is clean and clear and that your users have a good understanding of the source of the data so that they can put results in perspective. Social BI and the advent of smart visualization, augmented data modeling, self-serve data preparation makes it easy to gather and analyze the data but insight and perspective is key to success. Be sure to consider the location, condition and accuracy of your data and to select a solution that will connect various data sources (personal, external, cloud, and IT provisioned). Your organization can enjoy an interactive view and clean, clear data so that it is easier to use and interpret to provide data quality and clear watermarks to identify the source of data.

Data Governance and Self-Serve Analytics Go Hand in Hand

Many businesses step away from the concept of self-serve analytics because they believe that the concepts of self-serve and data governance are at odds but nothing can be further from the truth. If you select the right solution, you can ensure data and personal security and provide appropriate access at all levels of the organization. Give your business users the access they need with streamlined data governance to balance provisioned/approved data sources, watermarked/certified data and user-created data while assuring data provenance and dependability. if a user has access to an advanced analytics solution that integrates and delivers data from multiple legacy, best-of-breed and ERP systems, with the right solution, the enterprise can decide what type of data and access each user is entitled to enjoy. You need to understand your governance requirements and goals and select a solution that is flexible enough to deliver access to the type of depth of data your users need.

Collaboration Results in the RIGHT Analytical Solution

We already discussed the need for detailed requirements but it is also important to point out the importance of involving all teams in the requirements process. Business users will have different requirements from data scientists, IT staff and executives. If you are to succeed in implementing and leveraging an advanced analytical solution, you need to understand the perspective, role and needs of each group and select a solution that will meet those needs, one that is flexible enough to accommodate changing requirements, location and organization growth, and the organizational goals for cost and project timelines. Don’t make assumptions. Collaboration among users and teams is key to your success.

Data Analytics Literacy MUST Exist at All Organizational Levels

Finally, it is important to include data literacy considerations. This does not mean that your users have to become skilled data scientists. It simply means that, if users at every level of your organization are going to adopt and effectively use advanced analytics to meet enterprise goals, each user must understand:

  • The objectives and goals the organization has set forth for the advanced analytics initiative
  • How this initiative impact their role
  • How these tools can provide meaningful assistance in helping them to achieve THEIR goals.
  • How individuals and teams can share advanced analytics and create a social BI environment
  • How advanced analytics and metrics can and will reveal answers, issues and opportunities
  • How and when to involve IT and data scientists when strategic, next level, analytics is required
  • The guidelines and basics of the advanced analytics environment, e.g., various data sources, reports and types of analysis available for their use

When we talk about data analytics literacy, we must include literacy goals at every level of the organization, including senior executives. Team members are more likely to take this initiative seriously if senior executives use and understand these tools and can engage in meaningful discussions when presented with reports or when discussing issues or opportunities revealed by data analytics.

This article provides a summary discussion of some of the important factors involved in the consideration of an advanced analytical solution implementation. This planning process is key to the successful selection, implementation, deployment and management of an advanced analytical solution.

Original Post: Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

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Plug n’ Play! Analytics that Leads Business Users to Success https://www.smarten.com/blog/2017/09/11/september-11-2017/assisted-predictive-modeling/ Mon, 11 Sep 2017 14:05:57 +0000 https://www.smarten.com/blog/?p=5490 Continued]]> Assisted Predictive Modeling Leads the Way with Ease

Plug n’ Play Predictive Analysis: Sophisticated, Yet Easy for Every User!

Oh, the confusion of advanced analytical terminology. Read a report, attend a conference and your head is swirling with terms like ‘assisted predictive modeling’, plug n’ play predictive analysis, smart visualization, augmented data discovery and augmented data preparation. Add to that, the sophisticated concepts of auto-suggest, auto-recommend, time series forecasting, causation and prediction and classification techniques and you may feel that you need a degree in data science to do your job.

But, if you get the right Advanced Analytics Tools, you don’t have to worry about all of that because the tool will do the work for you. Take plug n’ play predictive analytics for example. Your organization can create and enable Citizen Data Scientists by enabling the average business user to leverage sophisticated predictive algorithms without the expertise and skill of a trained data scientist, so users who are not statisticians or predictive algorithm experts, can leverage self-service plug n’ play predictive tools to confidently make business decisions.

Select the data you want to analyze and see how easy it is to get results and produce reports that point you in a clear direction, illustrate patterns and trends and even auto-suggest and recommend analytical techniques to help you sort it out in the best way possible.

With the right combination of sophisticated tools and easy-to-use navigation, drag and drop flexibility and personalized dashboard capabilities, every one of your users can have the power and flexibility of predictive analytics without the help of data analysts or IT staff. Business users will have the tools to make the right decision in a timely fashion without guessing or worrying that they may be missing something or that the data just doesn’t give them a clear answer. It’s like having an expert data scientist standing at your shoulder, pointing out the best way to organize and analyze the data. What could be better than that?

Make it simple, using sophisticated tools! Assisted Predictive Modeling

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Data Discovery Tools Help Your Discover Success https://www.smarten.com/blog/smart-data-discovery-is-the-answer-to-so-many-questions/ Wed, 06 Sep 2017 09:57:59 +0000 https://www.smarten.com/blog/?p=5486 Continued]]> Smart Data Discovery is the Answer to So Many Questions!

Advanced Data Discovery to Advance Your Business!

Advanced Data Discovery is only possible with the right data discovery tools. If you want to encourage smart data discovery, you don’t need smarter users – you need smarter tools. Advanced Analytics Tools should be simple enough for every business user to perform advanced data discovery using auto-suggested relationships and revealing the impact of decisions and changes within the organization.

The ideal advanced analytics software should allow your users to drag and drop data without advanced IT skills and it should enable them to analyze data without a degree in statistical analysis or advanced knowledge of algorithms or technology. Using advanced analytics tools your users should be able to gather and prepare data, integrate and analyze data and discover trends, share findings and apply appropriate decisions to operational, tactical and strategic processes, activities and goals.

If organized and presented properly, your data should allow users to discover the more subtle, but nevertheless important, aspects of data and results and predict outcomes and impacts based on decisions about new products, pricing, additional resources, partnerships, competitive decisions, etc.

We encourage you to take the Smarten approach to data discovery tools. Contact us to find out how!

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Data Preparation Tools Should be Self-Serve https://www.smarten.com/blog/self-service-data-preparation-help-yourself/ Tue, 22 Aug 2017 10:40:01 +0000 https://www.smarten.com/blog/?p=5477 Continued]]> Self Service Data Preparation! Help Yourself!

Why is Self-Serve Data Preparation So Important?

What is self-serve data preparation? It isn’t a method of making up data. It is a method of preparing data that allows the average business user to compile and analyze data without the assistance of a data scientist or an IT professional.

In so many enterprises, the idea of bringing together data from varied data sources and actually making it accessible, clear and understandable is a foreign idea at best. But with data preparation tools that are easy enough for every user, your organization is free to get the job done, understand results, solve problems and plan for the future without employing the services of dozens of data scientists!

Data preparation software can be cumbersome and difficult to use, but it doesn’t have to be! A true self service data preparation tool gives your users the flexibility to determine what data they want to see, how they want to see data. They decide how they want to share and analyze that data, so the entire enterprise can work together, collaborate on solutions and capitalize on business opportunities with complete insight and a clear understanding of what the data means.

Don’t restrict what business users can do! IT enabled self-serve data preparation puts data in the hands of business users with sophisticated, easy-to-use tools to compile and prepare data, test hypotheses, perform visualization and create and share reports, and create custom alerts and other information.

That is just part of the Smarten approach to analytics! If you want to know more, contact us!

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Smart Visualization Paves the Way for the Right Decision https://www.smarten.com/blog/smart-data-visualisation-helps-users-see-the-answer/ Fri, 18 Aug 2017 12:06:55 +0000 https://www.smarten.com/blog/?p=5471 Continued]]> Smart Data Visualisation Helps Users SEE the Answer!

Don’t Ask Users to Analyze BEFORE They Analyze! Use Smart Visualization!

Smart Data Visualisation is a smart choice for your organization. Not every user can anticipate the best way to display and analyze data. Not every user can gain enough insight into the data in advance to understand and recommend how best to visualize data to reveal problems, opportunities or results.

Smart visualization is the way to go! Don’t expect your users to analyze BEFORE they analyze. Take the shackles off your users with a dynamic tool for data visualization. Imagine if your users had a ‘guide’ to recommend the best way to present data and visualize that data to illustrate results and help define actions. Imagine if that guide could help a business user with data visualization by looking at the type of data, the volume of data, the dimensions and the patterns, trends and logical conclusions the data reveals.

If your data visualization tools could make those suggestions, your users would not have to analyze before they analyze! The guiding hand would allow users to gather and analyze data and move on to make decisions and take on the many tasks they must perform in the course of a day!

If you want to find about the Smarten approach to smart data visualisation, contact us today!

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Why will data visualisation fade away? https://www.smarten.com/blog/why-will-data-visualisation-fade-away/ Wed, 16 Aug 2017 14:26:52 +0000 https://www.smarten.com/blog/?p=5467 Continued]]> Why will data visualisation fade away?

Why do we need a graph? Me personally, have never needed a chart to check my sales data. I have used graphs to impress my seniors, who I am not sure if they get impressed with weak numbers presented in innovative graphs.

As the numbers get larger and inconsistent, a graphical display will help. I do not want to dilute your vision by giving an example, but the temptation is too high. – World population and comparison of per capita GDP and the number of people who are HIV positive on a 5.5-inch cell phone screen. Geo-mapping is the likely and the only way you see it at the moment. I agree that there is no other way. And I hope you can visualise some hypothetical data on a cell phone and sense how it will look with filters and options.

When I ask you the question, why are you looking at the graph? It is quite likely that your objectives are in three broad areas. One, compare how one country you are connected is doing in comparison with the others. Two, you want to see if the money you want to send or have sent is going into affected areas. Third, you are doing research. Research could be for selling product or service. Alternatively, there can be an educational objective.

Your eventual result or take away from the exercise is never graphical. In all possibility, it will be three lines which include text and data. Data may be in terms of adjectives or raw numbers.

Usually, if the takeaway is in more than three lines, there will be more analysis which is required.

I am limiting the discussion to a graphical analysis. A tabular report can be used to derive more analytics and drill down or drill through to make a detailed plan.

So what are we getting at? It is quite likely that you have a direct question you want to ask your data. Conventional funnel based click to filter model never permitted this direct question. So there was an unusual demand for graphics and not answers. Trends and not a prediction. Clusters and not absolute numbers.

Here is a question: Which is the best country to launch an HIV drug?

The question you may be asking, and which you will use the funnel based filtering is, which countries have the highest per capita income and high incidence of HIV positive people in absolute numbers.

On second thought, you add, closest to me to the above question.

Your answers will be three countries with some data in tabular form.

You could also see it on a full map on a desktop, but if you are using a smaller device, would you like to see the world map with three countries highlighted?

Similarly, if you are doing sales analysis which involves, geography, product groups, sales teams and marketing initiatives how many clicks and filters do you want on your cell phone? Or you would like to ask – Under which marketing campaign did sales peak and get one single line answer on your cell phone…

Smart Data Visualization has its power, and ElegantJ BI provides dazzling graphics.

For how long the graphical analytics will be necessary with a combination of predictive analytics with NLP and NLQ on the horizon is a question we are pondering.

We are headed for a Clickless Analytics world!

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Smart Data Visualization: Simpler, Better, Clearer, Faster https://www.smarten.com/blog/2017/08/16/august-16-2017/data-visualization-tools/ Wed, 16 Aug 2017 11:20:20 +0000 https://www.smarten.com/blog/?p=5463 Continued]]> Smart Data Visualization is Here and It is SIMPLE and CLEAR

Smart Visualization Tools: Analysis and Data Displays Made Simple (and Clear)

Smart Data Visualization! This concept seems alien to some people. Is it data that can read your mind and automatically display itself in a way that will help you understand? Is it a method you use to see data in a clear way; a technique you learn in a class? The answer is yes…and no.

Smart visualization is made possible by using advanced analytical tools and business intelligence solutions that are designed to use sophisticated analytical algorithms for analysis and offer data visualization that is easy to understand and use. To meet this objective, data visualization tools must allow your users to gather and display data in a way that will allow them to exploit the opportunities and identify the problems and to get to the ‘meat’ of the issues quickly and easily.

Smart visualization should include auto-recommend, and auto-suggest data displays to recommend displays, views, and plotting to explore the best possible value from underlying data. It should display and analyze data In a way that is useful to users and to their role, and it should provide clear, concise displays and options and formats to help users understand that data. It should also analyze, forecast and predict so that users can identify patterns and trends and get valuable insight.

In short, Smart Data Visualization goes beyond data display to suggest options for visualization and plotting for certain types of data, based on the nature, dimensions and trends inherent in the data, so users can leverage sophisticated tools in an easy-to-use, drag and drop interface, with no advanced skill requirement or technical knowledge.

If this sounds good to you, I would suggest that you start your journey to Smart Visualization here: Smart Data Visualization

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Clickless Analytics – Disambiguating George (Part 2 of ∞) https://www.smarten.com/blog/clickless-analytics-disambiguating-george/ Thu, 10 Aug 2017 12:43:52 +0000 https://www.smarten.com/blog/?p=5458 Continued]]> Clickless Analytics – Disambiguating George (Part 2 of ∞)

Does not look like a technical heading? It is as serious as my other stuff. And I have forgiven the definition of technical for being ambiguous.

The problem for working in a Clickless world starts with the meaning as meant in the input. So, a simple phrase “come on George” Could mean multiple things. A classic problem which the bot engine developers are resolving by using patterns and machine learning. “Come on” said by a mother, boyfriend, boss, colleague, store keeper, the barista could mean entirely different things at different times depending on the previous sentences.

Ok. You have heard this before. But when it comes to analytics, this takes a different turn. Here the bigger problem is not “Come on”, but it is “George”. If you are designing natural language query for analytics, “come on George” would have George as your primary concern for disambiguation rather than “come on” which would be a key area to address. Why? In Analytics, the first step we have to get out of the way is that is George Town, Mr George Mason, George and Company or George Cream or Georges garage which is sprinkled all over your data.

But wait it is more than that!

The steps to choose the right “George” depends on where “George” is in the sentence and if George is alone or is with some other words.

If I differentiate between Machine Learning and Analytics Learning, in machine learning, the index or the data is being built on interaction and interpretation. In Analytics, it is rebuilt on each step when data is indexed or added from the source.

Analytics world comprises of Dimensions and Matrices. For Natural language query, we usually classify the structure as Dimensions, Dates and Time and Matrices. The classification is purely to differentiate the value under each and process them appropriately. We will discuss this more in the future articles on Clickless Analytics.

Before we go further, let us acknowledge and put in two lines of how difficult it is for someone to understand “Come on George” unless the previous few sentences have been contextualised. The statement on its own has no power without previous statements even if there is extensive data in the learning store. There are multiple levels of contexts, like from the environment – for example, it is a pizza store, or user data, like the age group and shopping habits on a general e-commerce site. But by and large, if there is no previous input the sentence will hang fire for a long time unless there is some more input.

On the other hand, “Come on George” can be the only input into the analytics engine, and it will be okay.

In the Analytics NLQ after we classify, tokenize and get the POS, our need to get the sentiment is minimal. Our need is to disambiguate each token and put it in the right context.

In our world of Clickless Analytics from Smarten, we start with “come” a verb, “on” a preposition and “George” a noun. You can have an exclamation mark after the phrase, but that will have a negligible impact than an operator like <. While in ML based architecture the exclamation mark could play a detrimental role. This we will discuss in another article.

Smarten next looks for dependencies based on our unique indexing and aggregation engine. So “Come”, other than it being a verb would result in a part of a product name if we are selling books, like a book called “City Come A-Walking”. “On” would be treated as a preposition. A preposition is dependent on the following noun, but in our engine, the noun could be anywhere.  “George” would be a customer name, salesman’s name and part of the city name Georgia in our indexing.

What does “come on George” mean in this dataset and what data should be displayed on executing this?

Yes, I am going to answer this. But like a real Author, I must keep you interested. We will be describing what is called the dependency engine in the coming article.

Why does one need a different NLQ engine in Analytics? The data changes!

In learning engines, data in the past along with the context are put in learning matrix. In this, the book with the title including the word “come” did not exist. The new data does. “George” joining the company this month as a salesman and “George Town” is his territory.

Now add one more complication to this. There is a dimension called City and a value called City. We will look at this too in the coming articles.

Debate on this is welcome since this the second part of the series of article.

Read more articles on “Clickless Analytics”:

Clickless Analytics – Augmented Analytics

Clickless Analytics, कुछ कुछ बोलता है {Part 1 of ∞}

Clickless Analytics

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What is Augmented Analytics and Why Does it Matter? https://www.smarten.com/blog/what-is-augmented-analytics-and-why-does-it-matter/ Wed, 09 Aug 2017 12:15:52 +0000 https://www.smarten.com/blog/?p=5454 Continued]]> What is Augmented Analytics and Why Does it Matter?

If your role in business demands that you stay abreast of changes in business analytics, you are probably familiar with the term Smart Data Discovery. You may also have read the recent Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics’, Published 27 July 2017, by Rita L. Sallam, Cindi Howson, and Carlie J. Idoine, highlighting the importance and benefits of augmented analytics, augmented data preparation and augmented data discovery.

For those who are managing an analytical solution implementation, or trying to select a solution for business users, it is important to understand the terms and the features and function of these solutions so that you can select the appropriate solution for your users, your data analysts and your IT team.

So, let’s dive into the definitions first!

Smart Data Discovery goes beyond data monitoring to help business users discover subtle and important factors and identify issues and patterns within the data so the organization can identify challenges and capitalize on opportunities. These tools allow business users to leverage sophisticated analytical techniques without the assistance of technical professionals or analysts. Users can perform advanced analytics in an easy-to-use, drag and drop interface without knowledge of statistical analysis or algorithms. Smart Data Discovery tools should enable gathering, preparation, integration and analysis of data and allow users to share findings and apply strategic, operational and tactical activities and will suggest relationships, identifies patterns, suggests visualization techniques and formats, highlights trends and patterns and helps to forecast and predict results for planning activities.

Augmented Data Preparation empowers business users with access to meaningful data to test theories and hypotheses without the assistance of data scientists or IT staff. It allows users access to crucial data and Information and allows them to connect to various data sources (personal, external, cloud, and IT provisioned). Users can mash-up and integrate data in a single, uniform, interactive view and leverage auto-suggested relationships, JOINs, type casts, hierarchies and clean, reduce and clarify data so that it is easier to use and interpret, using integrated statistical algorithms like binning, clustering and regression for noise reduction and identification of trends and patterns. The ideal solution should balance agility with data governance to provide data quality and clear watermarks to identify the source of data.

Augmented Analytics automates data insight by utilizing machine learning and natural language to automate data preparation and enable data sharing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately.

Why is all of this important to your organization?

Here are a few reasons you should consider advanced analytics and augmented data preparation for your enterprise:

  • These solutions allow the data scientist and IT community to focus on strategic issues and special projects.
  • Accessible augmented analytics creates Citizen Data Scientists and improves accountability and empowerment.
  • Advances in smart data discovery and other sophisticated techniques and solutions can positively impact ROI and TCO.
  • These solutions produce better decisions, more accurate business predictions and measurable analysis of product and service offerings, pricing, financials, production and other aspects of business.
  • Augmented data preparation and related tools will improve user adoption, data popularity, social BI integration and data literacy.

It is not always easy to stay abreast of the terms, techniques and solutions in the analytics domain but it is well worth the effort. This market is changing rapidly with new tools and improvements introduced every year.

Whether you are an IT consultant, an in-house IT professional, a middle manager or a senior executive, it is important to monitor the progress of business analytics and the related technology. Every business needs to understand how these solutions can and will affect users, processes and workflow.

Giving your team the right tools, and a simple way to manage the overwhelming flow of data and information, is crucial to your business success and will make every team member an asset to your organization.

Original Post: What is Augmented Analytics and Why Does it Matter?

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Predictive Analytics on Small Data https://www.smarten.com/blog/predictive-analytics-on-small-data/ Tue, 08 Aug 2017 10:49:26 +0000 https://www.smarten.com/blog/?p=5450 Continued]]> Predictive Analytics on Small Data

This is a small note on small data. I hope it has a big impact. The common understanding of the world is that one should use predictive and prescriptive data on big data. A vast amount of data, classified and grouped, running analytics to predict what will be the next event that one or more elements of the group will take. Predictive analytics like this allows pushing of right products to e-commerce shoppers. I am sure you all have experienced this on the large e-commerce site and enjoyed it.

In the world or predictive and prescriptive analytics on small data for big impact, one needs to work hard on acquiring the small data and ensuring its validity. The power of processing now available at a scale never experienced before, some traditional methods which were given up due to the limitation of processing power can be revived now. So no new approaches here.

For the sake of this small note, we will take the electrical fittings segment like switches. If we want to predict how this market is moving, the only data you can pull from your ERP is the past sales of different types of switches in a hierarchy of geographical information.

Public data will allow one to get the regional demographics and the GDP. Also, one will be able to get a sense of public participation by election data and public initiatives.

Some data, like Government spending in the region, may or may not be available.

Now our problem is to predict, which price band of electrical switches is going to grow in the next three years if there is no disruptive change in technology.

So the first step is to get two or more products which have a visible connection with switches and have data published. This could be sales data of publicly listed companies, compiled data from companies who are in the business of selling data and finally the data provided as market size by your sales team.

With the above data, we have to create a polynomial equation which can stand the test of multiple variables. That sounds outrageously simple, and it is. We want to know the factors which we can multiply the company’s sales in currency, less or add a factor, be equal to an equation of three or more variables, say, production of paint, production of cement and per capita GDP. The only way we are going to get to this by trying a significant number of options. You may have 20 variables and all you have to try it again to match the perfect balance.

Now you have the computing power and Smarten – Advanced Data Discovery tools which can make a difference.

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Clickless Analytics – Augmented Analytics https://www.smarten.com/blog/clickless-analytics-augmented-analytics/ Fri, 04 Aug 2017 12:36:09 +0000 https://www.smarten.com/blog/?p=5444 Continued]]> Clickless Analytics – Augmented Analytics

Gartner recently released a paper on Augmented Analytics which is described as “An approach that automates insights using machine learning, and natural-language generation.”

So as a gyani disseminating free gyan on the internet, I have chosen to write a few words on this.

This blog post of mine is an extremely biased and opinionated post. This is because any attempt to be fair and unbiased would not be thought provoking. We believe in clickless analytics and path to that includes learning and responding, predicting and suggesting, warning and solving. So, some of the points in this article may sound contrary to these but are not.

There are a few forms of viewing analytic which are taking the rounds, I want to address them briefly, and then I would love to get into more details.

An Unnecessary Story

I believe that nobody likes an unnecessary story. A narrative created from data which is normal without the context is a powerless narrative. The usually story like you walked 2% more than last month or your sales was up by 4% over the last quarter and 7% over the last month is as powerless as saying that the weather was warm on Sunday but it rained on Monday.

Data does not know the story. Your running data does not know about the weather and your work load. Your sales data does not know about GST and demonetization.

The result would be a misguiding narrative.

Even the regular business data which compares time periods is the least of the wonders from the analytics world.

One Graph too much

If your data to be viewed is not extremely large or extremely dispersed you do not need a graph. You may need a graph to just view large data, not analyse it.  In the world where big data is the word for the season, we are looking at small aggregated data for real decisions. Let us take an example.

The total number of Wallmart stores is 4672, the SKU each store handles hovers around 150 000, the revenue totals US$ 482 billion. The person who wants to see the data for all 4672 store will not like to see the data per SKU in a graph. The person who wants to see the moment in SKU would need not see the data of 4672 stores.

Let us visualize the growth over a period of 10 years in the graph. What will be the next analysis the person will go to from this graph?

Clickless Analytics – Augmented Analytics

For example, will someone be looking for root-cause analysis will the person jump to another graph from here?

While the data remains humungous, the analysis is always in combination and contexts. Charts which has come to epitomize the analytics industry are not exactly what the people may be striving for.

Here the alternatives, which we are suggesting in the clickless world. We will write in detail about them and hope this is what may be the classic definition of Augmented Analy