Gartner’s 2017 Take on Data Science Software

In my ongoing quest to track The Popularity of Data Analysis Software, I’ve finally decided to change the title to use the newer term “data science”. The 2017 version of Gartner’s Magic Quadrant for Data Science Platforms was just published, so I have updated my IT Research Firms section, which I repeat here to save you from having to dig through the entire 40+ page tome. If your organization is looking for training in the R language, you might consider my books, R for SAS and SPSS Users or R for Stata Users, or my on-site workshops.

IT Research Firms

IT research firms study software products and corporate strategies, they survey customers regarding their satisfaction with the products and services,  and then provide their analysis on each in reports they sell to their clients. Each company has its own criteria for rating companies, so they don’t always agree. However, I find the reports extremely interesting reading. While these reports are expensive, the companies that receive good ratings often purchase copies to give away to potential customers. An Internet search of the report title will often reveal the companies that are distributing such copies.

Gartner, Inc. is one of the companies that provides such reports.  Out of the roughly 100 companies selling data science software, Gartner selected 16 which had either high revenue or lower revenue but high growth (see full report for details). After extensive input from both customers and company representatives, Gartner analysts rated the companies on their “completeness of vision” and their “ability to execute” that vision. Figure 3 shows the resulting plot. Note that purely open source software is not rated by Gartner, but nearly all the software in Figure 3 includes the ability to interact with R and Python.

The Leader’s Quadrant is the place for companies who have a future direction in line with their customer’s needs and the resources to execute that vision. The four companies in the Leaders quadrant have remained the same for the last three reports: IBM, KNIME, RapidMiner, and SAS. Of these, they rate IBM as having slightly greater “completeness of vision” due to the extensive integration they offer to open source software compared to SAS Institute. KNIME and RapidMiner are quite similar as the are driven by an easy to use workflow interface. Both offer free and open source versions, but RapidMiner’s is limited by a cap on the amount of data that it can analyze. IBM and SAS are market leaders based on revenue and, as we have seen, KNIME and RapidMiner are the ones with high growth.

Figure 3a. Gartner Magic Quadrant for Data Science Platforms

The companies in the Visionaries quadrant are those that have a good future plans but which may not have the resources to execute that vision. Of these, Microsoft increased its ability to execute compared to the 2016 report, and Alpine, one of the smallest companies, declined sharply in their ability to execute. The remaining three companies in this quadrant have just been added:, Dataiku, and Domino Data Lab.

Those in the Challenger’s quadrant have ample resources but less customer confidence on their future plans. Mathworks, the makers of MATLAB, is new to the report. Quest purchased Statistica from Dell, and it appears in roughly the same position as Dell did last year.

The Niche Players quadrant offer tools that are not as broadly applicable.

In 2017 Gartner dropped coverage of Accenture, Lavastorm, Megaputer, Predixion Software, and Prognoz.

This entry was posted in Analytics, Data Science, R, SAS, SPSS, Uncategorized. Bookmark the permalink.

One Response to Gartner’s 2017 Take on Data Science Software

  1. Pingback: Gartner’s 2017 Take on Data Science Software - Use-R!Use-R!

Leave a Reply