When you hear the words “artificial intelligence,” (AI) what’s the first thing you think of: robots doing backflips, Alexa, medical diagnostic innovations or something else? If you’re a business intelligence (BI) and analytics application user, it’s likely that “data-driven insight to the masses” will soon be top-of-mind.

Machine learning will transform BI and analytics

Any business leader already knows that AI is a large field. Machine learning (ML) is an application of AI that gives systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning has two imminent, profound implications for individuals and companies using BI and analytics applications.

  1. BI and analytics will become available to more people than ever before: While BI and analytics technologies have evolved significantly over the last 25 years—having gone from a very IT-centric model to one in which end users are able to do more with data on their own—the reality is that BI is still primarily the domain of skilled IT experts and data analysts.

Some data discovery vendors tout that they already deliver “self-service to the masses,” but that’s a dubious claim. Yes, today a user with no training can take a dashboard that someone else built, make choices from drop-down menus to filter the data, double click on a chart to drill down into it, and other basic actions. For the vast majority of information workers, this is the definition of self-service analytics.

But to build that dashboard, someone has to assemble all the components, the key performance indicators (KPIs), the data visualizations, and all of the dashboard’s data feeds. This requires a specialized skillset to extract data from a number of different sources, aggregate it at the right level and cleanse the data—and then another skillset to build the dashboard itself.

In other words, while the number of people who are using BI and analytics today is greater than in the IT-centric old days, there still remains a very large segment of the user population that’s unable to work with data entirely on their own because they don’t possess the necessary expertise to extract and prepare data, or build dashboards. The barrier of entry to BI remains high.

Now, with AI, ML and related technologies such as natural language recognition (think Alexa), casual users can generate a visualization automatically based on what the user says or types. Instead of needing to understand how the data is structured, how to find it, and how to assemble it in the tool, a user will just speak or type, “Show me sales in North America for this quarter compared to the same quarter last year, as a bar chart.” And the answer will be presented.

  1. BI and analytics will make you smarter: The human brain can only process so much information and make logical connections between so many data points. Until the arrival of AI- and ML-fueled business intelligence, BI consisted of the user asking the tool a predefined question that the tool would then answer, as in the example above.

Two factors have converged in today’s modern BI and analytics: unimaginable amounts of low-cost computing power, and the ability for the BI system to self-discover insights. Let’s break that down:

  • A machine can perform a greater number of calculations at a faster rate than a person can. When you multiply that axiom by the infinite amount of computing power available in the cloud, it’s easy to see why machine learning makes us more productive. ML allows us to analyze vast amounts of data and find connections between that data at a rate simply not possible for humans.
  • Users will no longer be required to ask a predefined question of the BI system. The machine can simply look at vast amounts of data and intelligently derive insights from that data, presenting them to the user. The BI system can discover business-critical relationships in the data and automatically build visualizations and dashboards.

For example, in a health insurance environment, the BI system could pinpoint which precursor conditions are most closely associated with a medical event—a heart attack or a specific type of cancer—across its entire insured population, providing insight for preventive care, claims and premium analysis. Until now, this inductive type of analysis was only available to data scientists working with very large, expensive systems. Casual business users soon will have access to this level of data and analytic BI power.

Birst is driving innovation with broad technology patents

Birst has already laid the technology foundation for transformational BI and analytics. Underlying all of this AI and ML wizardry, automation is the common thread. Last summer, we announced that Birst has secured two broad patents for technologies enabling the smart preparation, discovery and visualization of data. Both patents have automation at the core.

Birst has leveraged these patents to deliver capabilities that help business users work with data and discover insights much faster and with less effort. Our solutions use machine learning to intelligently discover business-critical relationships in the data to automatically build visualizations and dashboards. We then apply advanced algorithms to take raw data and instantly structure it in an organized, consistent set of business metrics and attributes. And there you have it: the foundation of data-driven insight for all business users.

Find out more about how augmented analytics are transforming the BI world by reading this blog by Gartner Research Vice President Rita Sallam, “Just Buying Into Modern BI and Analytics? Get Ready for Augmented Analytics, the Next Wave of Market Disruption.” And follow Birst, an Infor company, on Twitter @BirstBI.