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Winners and Losers from Gartner's Data Science and ML Platform Report


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."

Gartner's 2020 Magic Quadrant For Data Science And Machine Learning Platforms Has Many Surprises


Enterprise decision-makers look up to Gartner for its recommendations on enterprise software stack. The magic quadrant report is one of the most credible, genuine, and authoritative research from Gartner. Since it influences the buying decision of enterprises, vendors strive to get a place in the report. Gartner recently published its magic quadrant report on data science and machine learning (DSML) platforms. The market landscape for DS, ML and AI is extremely fragmented, competitive, and complex to understand.

What's Changed: 2018 Gartner Magic Quadrant for Data Science and Machine-Learning Platforms


Analyst house Gartner, Inc. has officially released its 2018 Magic Quadrant for Data Science and Machine-Learning Platforms. In this report, Gartner adds machine learning as a major component of data science tools, adding that the topic warrants specific attention when evaluating the space. The re-naming of this Magic Quadrant speaks to the momentum machine learning currently exhibits in the data science marketplace. Data science and machine learning platforms are defined as a "cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution." Data science and machine learning platforms support data scientists in their efforts for data access, data preparation, data visualization, and a variety of predictive analytic functions.

Gartner's 2018 Take on Data Science Tools


I've just updated The Popularity of Data Science Software to reflect my take on Gartner's 2018 report, Magic Quadrant for Data Science and Machine Learning Platforms. To save you the trouble of digging though all 40 pages of my report, here's just the new section:

ML and BI Are Coming Together, Gartner Says


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.