Analytics, augmented by artificial intelligence, offers a means to deliver self-service business intelligence (BI) environments.
In recent times, a cry has gone out from many enterprise users: why can’t our business intelligence (BI) and analytics tools be as fast, easy and intuitive as Google searches? Yes, Google has spoiled us.
BI and analytics tools have been around for decades, their value to enterprises has been limited. In 2021, “Gartner found that BI and analytics adoption among all employees was 30%.,” according to Wayne Eckerson, writing in a recent report out of Eckerson Group. “That is only slightly better than the 20% rate I discovered when running similar studies in the 1990s.”
Enterprises have attempted to boost adoption with more user training, while vendors have spruced up and simplified their user interfaces. Still, that didn’t get to the heart of the issue — the need to enable greater self-service analytics with up-to-the-minute insights.
See also: Augmented Analytics: A New Dimension to Analytics & BI
With the onset of artificial intelligence, these tools are becoming more powerful and accurate. Still, they require strong business cases and quality data to live up to their purpose. These “augmented analytics” — as defined by Eckerson — employ artificial intelligence to make BI and analytics tools “easier to use to generate insights not possible with earlier generations of products. At the same time, not everyone can benefit from these capabilities at the same time, Eckerson cautions. The key is to “understand the target audience for these features before rolling them out broadly.
Still, even when augmented by AI, these tools or platform need to be trustworthy and accurate. “To ensure widespread adoption, there’s a need “to populate the tools with timely, relevant, and high-quality data,” he cautions. “BI and analytics tools can be unfairly tarnished if business users don’t trust the data.”
AI-augmented analytics offer a means to deliver self-service BI and analytics environments. These environments are built upon the following technologies:
- Natural language queries (NLQ): “NLQ generates SQL queries from text that business users type into a search box and returns a result, usually as a table or chart,” Eckerson explains.
- Assisted analytics: “When business users click on a metric in a chart or dashboard, assisted analytics
functionality automatically kicks off a correlation analysis that surfaces and explains the factors driving
that metric in natural language.” - Business monitoring: “Extends assisted analytics to run continuously on designated business metrics, intelligently alerting users to relevant changes that impact business outcomes and their root causes.”
- AI modeling wizards.: “Step business analysts through the process of creating an analytic model using regression, classification, or decision tree algorithms.”
AI-augmented business intelligence and analytics “promise to bring business users out of the dashboard desert into the modern world of ad-hoc queries, iterative analysis, intelligent alerting, and data science,” says Eckerson.
