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Tableau Software has acquired AI startup Empirical Systems in a bid to give users of its self-service BI platform...
more insight into their data. The Tableau acquisition, announced today, adds an AI-driven engine that's designed to automate the data modeling process without requiring the involvement of skilled statisticians.
Based in Cambridge, Mass., Empirical Systems started as a spinoff from the MIT Probabilistic Computing Project. The startup claims its analytics engine and data platform is able to automatically model data for analysis and then provide interactive and predictive insights into that data.
The technology is still in beta, and Francois Ajenstat, Tableau's chief product officer, wouldn't say how many customers are using it as part of the beta program. But he said the current use cases are broad and include companies in retail, manufacturing, healthcare and financial services. That wide applicability is part of the reason why the Tableau acquisition happened, he noted.
Catch-up effort with advanced technology
In some ways, however, the Tableau acquisition is a "catch-up play" on providing automated insight-generation capabilities, said Jen Underwood, founder of Impact Analytix LLC, a product research and consulting firm in Tampa. Some other BI and analytics vendors "already have some of this," Underwood said, citing Datorama and Tibco as examples.
Empirical's automated modeling and statistical analysis tools could put Tableau ahead of its rivals, she said, but it's too soon to tell without having more details on the integration plans. Nonetheless, she said she thinks the technology will be a useful addition for Tableau users.
"People will like it," she said. "It will make advanced analytics easier for the masses."
Tableau already has been investing in AI and machine learning technologies internally. In April, the company released its Tableau Prep data preparation software, with embedded fuzzy clustering algorithms that employ AI to help users group data sets together. Before that, Tableau last year released a recommendation engine that shows users recommended data sources for analytics applications. The feature is similar to how Netflix suggests movies and TV shows based on what a user has previously watched, Ajenstat explained.
Integration plans still unclear
Ajenstat wouldn't comment on when the Tableau acquisition will result in Empirical's software becoming available in Tableau's platform, or whether customers will have to pay extra for the technology.
"Whether it's an add-on or how it's integrated, it's too soon to talk about that," he said.
However, he added that the Empirical engine will likely be "a foundational element" in Tableau, at least partially running behind the scenes, with a goal that "a lot of different things in Tableau will get smarter."
Unlike some predictive algorithms that require large stores of data to function properly, Empirical's software works with "data of all sizes, both large and small," Ajenstat said. When integration does eventually begin to happen, Ajenstat said Tableau hopes to be able to better help users identify trends and outliers in data sets and point them toward factors they could drill into more quickly.
Augmented analytics trending
Various vendors are embedding machine learning tools into their software to aid with data preparation and modeling and with insight generation, according to Gartner. The consulting and market research firm said the augmented approach "has the potential to help users find the most important insights more quickly, particularly as data complexity grows."
Such capabilities have yet to become mainstream product requirements for BI software buyers, Gartner said in the February 2018 report. But they are "a proof point for customers that vendors are innovating at a rapid pace," it added.
The eight-person team from Empirical Systems will continue to work on the software after the Tableau acquisition. Tableau, which didn't disclose the purchase price, also plans to create a research and development center in Cambridge.
Senior executive editor Craig Stedman contributed to this story.