A few comments:
(1) While the “Wave” analysis purports to be an assessment of “Predictive Analytics Solutions for Big Data”, it is actually an assessment of vendor scale. You can save yourself $2,495 by rank-ordering vendors by revenue and get similar results.
(2) The assessment narrowly focuses on in-memory tools, which arbitrarily excludes some of the most powerful analytic tools in the market today. Forrester claims that in-database analytics “tend to be oriented toward technical users and require programming or SQL”. This is simply not true. Oracle Data Mining and Teradata Warehouse Miner have excellent user interfaces, and IBM SPSS Modeler provides an excellent front-end to in-database analytics across a number of databases (including IBM Netezza, DB2, Oracle and Teradata). Alpine Miner is a relatively new entrant that also has an excellent UI.
(3) Forrester exaggerates SAS’ experience and market presence in Big Data. Most SAS customers work primarily with foundation products that do not scale effectively to Big Data; this is precisely why SAS developed the high performance products demonstrated in the analyst beauty show. SAS has exactly one public reference customer for its new in-memory high-performance analytics software.
(4) SAS Enterprise Miner may be “easy to learn”, but it is a stretch to say that it has the capability to “run analytics in-database or distributed clusters to handle Big Data”.
(5) Designation of SAP as a “leader” in predictive analytics is also a stretch. SAP’s Predictive Analytics Library is a new product with some interesting capabilities; however, it is largely unproven and SAP lacks a track record in this area.
(6) The omission of Oracle Data Mining from this report makes no sense at all.
(7) Forrester’s scorecard gives every vendor the same score for pricing and licensing. That’s like arguing that a Bentley and a Chevrolet are equally suitable as family cars, but the Bentley is preferable because it has leather seats. TCO matters. As I reported here, a firm that tested SAS High Performance Analytics and reported good results did not actually buy the product because, as a company executive notes, “this stuff is expensive.”