I received a press release announcing that REvolution Computing, a provider of software and support for the open source "R" statistical programming language had appointed R co-creator, Robert Gentleman, to its board of directors. The press release was a great impetus for me to look at R again.
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Ashlee Vance offers some background about R:
...R is a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca partly because data mining has entered a golden age, whether being used to set ad prices, find new drugs more quickly or fine-tune financial models. Companies as diverse as Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell use it.
Zack previously suggested that R could become an alternative to SAS or IBM/SPSS's offerings in the business intelligence space. However, it seems that both SAS and SPSS have recognized the opportunity presented by R.
For instance, Jon Peck outlines his use of SPSS:
Starting with Version 16, SPSS offers a free plug-in that lets users run R code within SPSS having full access to the active SPSS Statistics data, and writing its output to the SPSS Statistics Viewer. With Version 17, we began creating dialog box interfaces and SPSS-style syntax for R packages we thought would be interesting to SPSS users...We see the SPSS-R connection as a way for users to take advantage of the large number of R packages without the pain part of R.
As Ashlee points out, R is being used by academics, university students, and enterprises. If ignored, R could very well have become a threat to the SAS and IBM/SPSS franchises. IBM has a history of utilizing open source for competitive advantage. Instinctively, I thought SPSS decided to support R after being acquired by IBM. I'm encouraged to learn that SPSS made the decision to support R well before the IBM acquisition. It's also great that SAS has followed suit.
I suspect that SPSS and SAS made their individual decisions based on three factors. First, they likely both realized that based on the penetration of SAS and SPSS in the statistical community, neither was going away anytime soon. Second, adding R support enabled both vendors to take advantage of the community of users building extensions and new statistical methods for R. Finally, both vendors likely realized that customers have different skills and analysis needs, and as such, R would be used in conjunction with SAS and SPSS's programming languages for statistical analysis.