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The convergence of machine learning and business intelligence is upon us, as BI tool makers increasingly are exposing ML capabilities to users, and users are performing ML activities in their BI tools. That's according to the latest Gartner report on analytics and BI tools, which was released this week. In its February 11 Magic Quadrant for Analytics and Business Intelligence (ABI) Platforms, the storied Stamford, Connecticut analyst firm did its best to quantify and qualify the trends in the sector. While BI and ML have largely existed on parallel tracks, with BI seeking to report what happened and ML seeking to predict what will happen, Gartner sees the two disciplines converging, at least as far as the toolsets are concerned. Not all ML work will occur within BI tools, of course.

The tools used for data science are rapidly changing at the moment, according to Gartner, which said we're in the midst of a "big bang" in its latest report on data science and machine learning platforms. "The data science and ML market is healthy and vibrant, with a broad mix of vendors offering a range of capabilities," Gartner says in its Magic Quadrant for Data Science and Machine Learning Platforms published January 28. "The market is experiencing a'big bang' that is redefining not only who does data science and ML, but how it is done." The analyst group defines a data science platform as an integrated place where data scientists, citizen data scientists, and developers can get all of the core capabilities that they need to not only build data science application, but to embed them into existing business processes and manage and maintain them over time. Integration and cohesion are keys, in Gartner's view, and applications that simply bundle various packages and libraries – especially open source offerings -- are not considered true platforms.

Gartner published its latest Magic Quadrant for data science and machine learning platforms last week. Sixteen vendors made cut for Gartner's report this year, the same number as last year. However, there were some important changes, including some vendors who made big jumps and some who lost ground. The biggest difference arguably was the addition of "machine learning" to the name of Gartner's report. "Although data science and machine learning are slightly different," the Gartner analysts write, "they are usually considered together and often thought to be synonymous."