Data Quality and Data Insight Tools Improve Outcomes
When a business sets out to initiate data democratization and improve data literacy, it must choose the right approach to business intelligence and select an augmented analytics product that is self-serve, intuitive, easy to implement and easy for business users to embrace. Transitioning business users into the role of a Citizen Data Scientist can be challenging.
By some estimates, bad data costs global organizations more than five trillion USD annually, and at the enterprise level, the quality of data can be a burden on IT, analysts and business users and acceptance of bad data can be inherent in business processes. Improving the overall quality of data increases confidence in decisions, reporting, strategies and the adoption of dependable analytical models across the organization.
When a business implements Data Quality, Data insight and Data Quality Management tools and techniques it can establish a comprehensive process with a solid set of tools to identify errors, enhance data quality, and boost productivity. Business users can leverage intuitive tools to uncover hidden insights and improve the overall quality of data with actionable recommendations to take prompt action.
Benefits:
- Ease-of-Use and intuitive tools for business users and team members – no technical skills required
- Improved accuracy and dependability of data for confident decision-making
- Data Quality supported by statistics and machine learning to assure credibility
- Improved data insight without delays or re-work
- Assured agility and decentralization of analytics
- Consistency of data quality and availability
- Improved User Adoption
Data insight takes data to the next level by providing comprehensive data analysis and quality assurance features that empower business analysts and users to quickly and easily identify errors, enhance data quality, and boost productivity. The business can harness the power of statistics and machine learning to uncover those crucial nuggets of information that drive effective decision, and to improve the overall quality of data. This approach allows users to let the system do the work for them and make confident decisions.
A foundational augmented analytics solution with machine learning, natural language processing and automation within an advanced analytics solution suite can improve results and support its team with augmented analytics designed as self-serve solutions for business users. Users can gather and analyze information with assurance of sustained data quality and produce results that are clear and concise.
Advanced data management features ensure data quality and provide crucial data insights with tools like Column Analysis, Feature Importance, Missing Value Analysis and Observations. Tools that support data insight include numerous data quality management techniques. These tools allow users to see and work with datasets in a way that is targeted and provides clear, actionable information for decisions and strategies.
If your business wishes to improve the easy of analytics and Quality Of Its Data and achieve data insight in a timely, dependable manner, find out more by watching this free Smarten Webinar: ‘Improving Data Quality With Data Insights,’ and read our free blog article, ‘Balance Data Quality With Data Agility.’ Explore our Smarten Augmented Analytics Products And BI Tools.
Original Post : Use Data Quality and Data Insight Tools to Avoid ‘Bad Data’!
Augmented Analytics, Business Intelligence, Data Governance, Data Insight, Data Integrity, Data Quality, Data Quality (DQ), Data Quality Management, Data Quality Tools
