Steve Miller, Information Management Blogs, April 27, 2010
([Norman] Nie is perhaps best known as co-founder, then President, and finally CEO of SPSS, the Statistical Package for the Social Sciences, a statistics/analytics software company started in 1975 that grew to a successful public offering in 1993. ... Recently he'd become CEO of REvolution Computing, the commercial open source vendor of the R Project for Statistical Computing.)
This week's Part 1 gives Nie's take on the evolution of statistical software. Part 2 will outline the strategy of REvolution Computing to make R more suitable for a business/BI portfolio, in so doing providing a foundation for the platform to compete with current proprietary leaders SAS and IBM SPSS.
Steve Miller: Experts vs. Analytics - your assessment from 40+ years in the business.
Norman Nie: I think you're talking about the degree to which decision making in enterprise and scientific research is based on expert opinion versus empirical information and data collection. Over the past 40 years, we have seen a consensus emerge that to ensure efficient operations in enterprise, empirical data must be applied to virtually all fields.
If you look at the last 40 years of university curriculum, SPSS - the product I helped build - has been the dominant player, even becoming the common thread uniting a diverse range of disciplines, which have in turn been applied to business. Data is ubiquitous: tools and data warehouses allow you to query a given set of data repeatedly. R does these things better than the alternatives out there; it is indeed the wave of the future.
SM: You built a highly-successful statistical analysis software company, SPSS, over 40 years ago, and have enjoyed great success in both business and academia during that period. Now, much more than financially secure, what motivates you to take the helm of commercial open source R, REvolution Computing?
NN: REvolution offers an extraordinary opportunity to remake and reenergize an exploding field in predictive analytics. According to analysts, the predictive analytics market is set to experience double-digit growth; to be able to do something like this twice in one's lifetime is a feat motivating in its own right.
What lessons, if any, from the successes and failures of SPSS can be applied to R and Revolution Computing?
NN: My partners and I designed SPSS in an era when being a programmer was a very rare talent. Data was only being specially designed for a given project, volume was limited, and the number of procedures that people would commit to were limited and unrelated. For years, I urged SPSS to re-do their system to deal with the larger amounts of data that were flooding them.
When the opportunity of R and REvolution came to me, it was obvious that I had found the right tool to finally accomplish this feat: a fully programmable statistical language that would allow us to take the next great leap forward. Now, we are putting analytical capabilities in the hands of the individual rather than the organization as a whole. This way, the product is customizable and allows experts to better tailor programs to their specific needs.