Chief Information Officers and Chief Data Officers have their hands full with big data challenges as the amount of content grows exponentially at a rapid pace daily. Many conversations around big data in the media are focusing the “3 Vs,” which include volume, variety and velocity. However, what is not being addressed is the user experience for big data and the analytics that can drive business intelligence.

Large enterprise organizations, often with big budgets, are implementing solutions that address the user experience, but there is room for the evolution of creating a better, self-supporting big data business intelligence for any size company. Potential seekers of big data solutions often fear that project implementation will be never-ending and require constant and expensive developer support. Others are daunted by the thought of using and training complex, sophisticated systems with AI or supervised machine learning tools for data aggregation and analysis. As we speak with various organizations, we are learning that many companies would like business analysts and departmental users to operate a system without having to hire a data scientist or developers.

Take a deep breath. Insurmountable big data projects now have the best of both worlds – an easy, user experience, with low maintenance, with the advantage of using a robust, complex, innovative technology platform that focuses on supervised machine learning algorithms. Solutions like Ephesoft Insight, a big data document and analytics platform was purposely designed for business users in a self-supporting environment.

Platforms such as Ephesoft Insight mine and crawl through big data repositories to detect trends, patterns, anomalies and business intelligence. The system then visually and graphically illustrates correlations, graphs, mind maps, dashboards and other types of charts that can be easily digested by any type of user.

The concept of self-supported big data begins when an end-user creates a learning model for capturing valuable data from unstructured content stored in documents and records. With minimal sample files, the user can point-and-click to define textual values for extraction and drag-and-drop document pages to define content categorization rules.

So, what’s really happening technically behind this simple point-and-click or drag-and-drop model? Ephesoft Insight takes a user’s input and applies it to processed content through multi-dimensional analysis, powered by supervised machine learning algorithms. Without having to understand how supervised machine learning works or even what an algorithm is, business users can build smart capture models quickly and easily with their documents and records. Features like outlier reports, filters, searches and relationship links are simple to create.

Big data projects and the solutions used to capture and aggregate document data don’t have to be intimidating or overwhelming. And, they certainly aren’t exclusive to giant corporations anymore.  Products like Ephesoft Insight can help organizations of all sizes, with IT departments of all skill levels, achieve valuable content analysis quickly and intuitively.

Learn more about Ephesoft Insight in this brief demo: