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Breaking Down the Market for Data Science Platform Solutions

IBM, SAS, RapidMiner, and KNIME lead the pack in Gartner's new "Magic Quadrant for Data Science Platforms."

Gartner's first ever "Magic Quadrant for Data Science Platforms" isn't exactly new.

It's a rebranding of the market watcher's "Magic Quadrant for Advanced Analytics Platforms."

Why the change? For one thing, it's a reflection of changes in marketing. The inaugural "Magic Quadrant for Data Science Platforms" report acknowledges that Gartner previously called these platforms "advanced analytics platforms."

The report states, "Recently, however, the term 'advanced analytics' has fallen somewhat out of favor as many vendors have added 'data science' to their marketing narratives. This is one reason why we now call this category 'data science platforms,' but it is not the main reason.

"Our chief reason is that it is commonly 'data scientists' who use these platforms."

Not Just a Rebranding

The new "Magic Quadrant for Data Science Platforms" isn't just a rebranding. The market watcher says it also revised the criteria used to evaluate vendors for inclusion in the report -- much as it (in)famously did with last year's "Magic Quadrant for Business Intelligence and Analytics Platforms."

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"[W]e have completely revamped the inclusion criteria to give us more flexibility to include the vendors -- both established and emerging -- that are most relevant and representative in terms of execution and vision. This revamp has resulted in the inclusion of more innovative -- and typically smaller -- vendors," the report explains.

"In our judgment, however, even the lowest-scoring inclusions are still among the top 16 vendors in a software market ... that is becoming more heated and crowded every year."

Who's Leading the Data Science Pack?

How did Gartner's changes affect the ranking of vendors large and small?

In the "Leaders" quadrant, which is reserved for vendors with the highest scores in both "Completeness of Vision" and "Ability to Execute," Gartner once again has IBM and SAS leading the pack. The two other vendors in the 2017 Leaders quadrant -- KNIME and RapidMiner -- were there last year, too.

The relative plotting of all four vendors might have changed -- e.g., Gartner has RapidMiner leapfrogging rival KNIME in both metrics -- but they continue to lead the pack.

One vendor that dropped out of the Leaders quadrant is the former Dell, now Quest.

Gartner has Quest as one of its "Challengers" -- vendors that score highly in Ability to Execute but perform less well on Completeness of Vision. The reversal is mostly a result of Dell's divestiture (following its acquisition of EMC) of its software assets, including Statistica. In addition to this shake up, Gartner notes, Statistica continues to "lack ... some [of the] product improvements central to native cloud and some Spark capabilities."

Those capabilities are on the Statistica road map, however: "The platform's large customer base and all-around strength on premises across the production refinement, business exploration, and advanced prototyping use cases merit a position as a strong Challenger," the report says.

Of Visionaries, Challengers, and Niche Players

Other Challengers include MathWorks, Alteryx, and Angoss. The last was in roughly the same position (and plotting) last year. Microsoft is also more or less where it was in 2016: virtually cutting the line that separates Visionaries from Leaders. Visionaries are vendors with high Completeness of Vision scores that lack correspondingly high scores in Ability to Execute.

"During the past three years, Microsoft has undertaken a remarkable revamp in the context of machine learning," Gartner notes. "It entered the market with a very limited product offering and remains a Visionary for its market-leading data science cloud solution. The omission of a comparable on-premises offering continues to pose challenges for customers and significantly reduces Microsoft's Ability to Execute."

The Gartner report includes a few new entries, too. Dataiku, Domino Data Lab,, MathWorks, and Teradata are all new to this year's Magic Quadrant.

The first four are vendors that specialize in data science and/or artificial intelligence (AI) technologies; Gartner has Dataiku, Domino Data Lab, and as Visionaries and MathWorks as a Challenger. Teradata, by contrast, is best known as a giant in data warehousing. Not just data warehousing anymore, Gartner notes. "[Teradata] is a Niche Vendor, largely due to its low level of adoption and lack of broad usability and applicability," according to the report.

"However, it excels in situations where Aster Analytics fits into an organization's existing infrastructure and there are significant requirements for high performance."

Several vendors were also dropped from this year's report: Accenture, Lavastorm, Megaputer, Predixion, and Prognoz.

As Gartner noted, the market for data science products and services is becoming more "heated and crowded." Next year's "Magic Quadrant for Data Science Platforms" will likely feature further significant changes.

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