The ten vendors in the report are, in Forrester's ranked order, SAS, IBM, SAP, Tibco, Oracle, StatSoft, KXEN, Angos Software, Revolution Analytics and Salford Systems. The rankings are based on 51 evaluation criteria rolled up into 13 overall scores on current product offerings, company strategy and market presence. SAS has the largest market presence with more than 3,000 predictive analytics customers, versus more than 1,500 for IBM, but the product and strategy ratings are what drove the rankings, according to Forrester's report. (A free copy of the report, which was not sponsored by any single vendor, is available from SAS without registration requirements.)
SAS offers "a broad set of tools for predictive analytics, an architecture that supports multiple platforms, in-database analytics and in-memory analytics," the report notes. To stay on top, the analyst firm says SAS needs to "provide more sophisticated solutions for real-time analytics, such as stream processing [and] offer predictive modeling tools that business analysts find more usable."
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Forrester gave IBM high marks for its worldwide footprint and broad analytics portfolio, which includes SPSS and the PureData System for Analytics (formerly Netezza) in-database capabilities among other assets. Forrester encouraged the vendor to make its total portfolio "less confusing" and to create more solutions that "customers can use out of the box." Reading between the lines, that suggests less dependency on IBM consultants to integrate multiple products.
One surprise in the assessment, according to Forrester analyst and report author Mike Gualtieri, was that SAP scored as well as it did considering that the vendor is relatively new to advanced analytics. The company introduced an advanced analytics module three years ago based on SPSS software and then introduced a replacement, called Predictive Analysis, in 2012.
SAP has a modeling tool (akin to SAS Enterprise Miner and SPSS Modeler) and an analytics library (akin to those offered by Teradata and IBM PureData for Analytics). Forrester's Wave report describes SAP's strategy of running analytics inside the Hana in-memory database as bold. Gualtieri told InformationWeek that Forrester's Q3 2012 review found that SAP's technologies were more scalable for big data use than those offered (at the time of the review) by Tibco, then next vendor in the ranking.
Another surprise in the rankings was that products focused on the open source R programming language, such as those from Oracle and Revolution Analytics, did not fare better. R has received a lot of attention and support from multiple vendors, but Gualtieri said that this powerful programming language is difficult to learn and only appropriate for direct use by data scientists and high-level analytics professionals.
"The idea of business types using R to develop predictive analytics would be like asking them to use Java to write BI reports -- it's just not going to happen," he said.
In addition to providing abstracted, business-user-oriented interfaces, the path to eliminating complexity from predictive analytics -- whether from commercial products or open-source environments like R -- will be to take advantage of automation and machine-learning, Gualtieri said. The idea is to automatically test a variety of algorithms and implement the most effective ones without forcing users to go through complex, iterative testing.
"We're not there yet, but lots of vendors are working on this approach," he said.
The list of companies pursuing automation and/or machine learning includes analytics vendors Alpine Data Labs and KXEN, BI vendors Alteryx, Birst and Pentaho, and process-management vendors Pegasystems and Rage Frameworks.
Revolution Analytics' David Smith, VP of marketing, said he's glad to see a significant report focused on the hot predictive analytics arena. "It's a topic that has gone from being a side issue into the core focus of companies now that they've figured out the value of collecting data and applying predictive analytics to that data," he said.
Revolution has focused on scaling up R-based analytics and providing management tools for data scientists working on predictive analytics and big data problems. Revolution's customers are building company-specific analytic applications to bring the power of prediction to business users, Smith says. That's a contrast to Forrester's vision of automation- and machine-learning-driven tools placed directly in the hands of business users.
"Data scientists will always play a key role because every company is different, every problem is different, and companies have different sources and types data," Smith said. "Data scientists can use all available analytical techniques to solve unique problems as opposed to hoping for a one-size-fits-all solution."
There's no doubt that the cost of talent and time required for custom analysis and development will figure in the growing competition that's brewing between packaged tools and applications and custom analytics projects.