Data Science Software Reviews: Forrester vs. Gartner

Update: an earlier version of this post included figures that I’ve removed at the request of Forrester, Inc.

In my previous post, I discussed Gartner’s reviews of data science software companies. In this post, I describe 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 a leading global research and advisory firm 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.

The Forrester Wave: Multimodal Predictive Analytics and Machine Learning Solutions, Q3, 2018 covers software that is controllable by various means such as menus, workflows, wizards, or code (as of 23/22/2019 available free here). Forrester plans to cover tools for automated modeling in a separate report, due out in 2019. Given that automation is now a widely adopted feature of the several companies covered in this report, that seems like an odd approach.

Forrester divides the vendors into four categories: Leaders, Strong Performers, Contenders, and Challengers.

In the Leaders category, they include IBM, while Gartner viewed them as a middle-of-the-pack Visionary. Forrester and Gartner both view SAS and RapidMiner as leaders.

The Strong Performers category includes 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. Forrester has KNIME and SAP next to each other in this category, while Gartner had them far apart, with KNIME a Leader and SAP a Niche Player. Dataiku is here too, with a similar rating to 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 includes World Programming, which sells SAS-compatible software, and Minitab, which purchased Salford Systems.  Neither were considered by Gartner.

 The Forrester Wave: Notebook-Based Solutions, Q3, 2018 reviews software controlled by notebooks, which blend programming code and output in the same window (as of 3/22/2019 available here).

Forrester rates some of the notebook-based vendors very differently than Gartner. 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 category are Databricks and H2O.ai, in very similar positions compared to Gartner. Civis Analytics and OpenText are also in this category; neither were reviewed by Gartner. Cloudera is here as well; it too was left out by Gartner.

Forrester’s Condenders category 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!