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)