In my previous post, I discussed Gartner’s reviews of data science software companies. In this post, I show Forrester’s coverage and discuss how radically different it is. As usual, this post is already integrated into my regularly-updated article, The Popularity of Data Science Software.
Forrester Research, Inc. is another company that reviews data science software vendors. Studying their reports and comparing them to Gartner’s can provide a deeper understanding of the software these vendors provide.
Historically, Forrester has conducted their analyses similarly to Gartner’s. That approach compares software that uses point-and-click style software like KNIME, to software that emphasizes coding, such as Anaconda. To make apples-to-apples comparisons, Forrester decided to spit the two types of software into separate reports. Figure 3c shows the results of The Forrester Wave: Multimodal Predictive Analytics and Machine Learning Solutions, Q3, 2018. By “multimodal” they mean controllable by various means such as menus, workflows, wizards, or code. Figure 3d shows the results from The Forrester Wave: Notebook-Based Solutions, Q3, 2018 (notebooks blend programming code and output in the same window). Those are the two most recent Forrester reports on the topic. Forrester plans to cover tools for automated modeling in a separate report. Given that automation is now a widely adopted feature of the several companies shown in Figure 3c, that seems like an odd approach.
Both plots use the x-axis to display the strength of each company’s strategy, while the y-axis measures the strength of each’s current offering. Blue shading is used to divide the vendors into Leaders, Strong Performers, Contenders, and Challengers. The size of the circle around each data point indicates the “presence” of each vendor in the marketplace, weighted by 70% by vendor size and 30% by ISV and service partners.
In Figure 3c, we see a perspective that is radically different from the latest Gartner plot, 3a (see previous post). Here IBM is considered a leader, instead of a middle-of-the-pack Visionary. SAS and RapidMiner are both considered leaders by Gartner and Forrester.
In the Strong Performers segment, we see KNIME, which Gartner considered a Leader. Datawatch and Tibco are tied in this segment while Gartner had them far apart, with Datawatch put in very last place by Gartner. KNIME and SAP are next to each other in this segment, while Gartner had them far apart, with KNIME a Leader and SAP a Niche Player. Dataiku is here too, with a similar rating from Gartner.
The Contenders segment contains Microsoft and Mathworks, in positions similar to Gartner’s. Fico is here too; Gartner did not evaluate them.
Forrester’s Challengers segment World Programming, which sells SAS-compatible software, and Minitab, which purchased Salford Systems. Neither were considered by Gartner.
The notebook-based vendors shown in Figure 3d is also extremely different from Gartner’s perspective. Here Domino Data Labs is a leader while Gartner had them at the extreme other end of their plot, in the Niche Players quadrant. Oracle is also shown as a leader, though its strength is this market is minimal.
In the Strong Performers segment are Databricks and H2O.ai, in very similar positions compared to Gartner. Civis Analytics and OpenText are also in this segment; neither were reviewed by Gartner. Cloudera is in this segment as well; it was left out by Gartner.
The Condenders segment contains Google, in a similar position compared to Gartner’s analysis. Anaconda is here too, in a position quite a bit higher than in Gartner’s plot.
The only two companies rated by Gartner but ignored by Forrester are Alteryx and DataRobot. The latter will no doubt be covered in Forrester’s report on automated modelers, due out this summer.
As with my coverage of Gartner’s report, my summary here barely scratches the surface of the two Forrester reports. Both provide insightful analyses of the vendors and the software they create. I recommend reading both (and learning more about open source software) before making any purchasing decisions.
To see many other ways to estimate the market share of this type of software, see my ongoing article, The Popularity of Data Science Software. My next post will update the scholarly use of data science software, a leading indicator. You may also be interested in my in-depth reviews of point-and-click user interfaces to R. I invite you to subscribe to my blog or follow me on twitter where I announce new posts. Happy computing!
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