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REvolution Computing Blog, March 09, 2010, by REvolution Computing CEO (and co-founder of SPSS) Norman Nie
The R Project: despite there being over 2 million users of this open-source language for statistical data analysis, you might not have heard of it ... yet. You might have seen this feature in the New York Times last year, and you might have heard how REvolution Computing is enhancing and supporting R for commercial use. Because what was once a secret of drug-development statisticians at pharmaceutical companies, quants on Wall Street, and PhD-level statistical researchers around the globe (not to mention pioneers at Web 2.0 companies like Google and Facebook) is suddenly becoming mainstream. The reason? The perfect storm of a deluge of data, open-source technology, and the rise of predictive analytics.
Predictive analytics -- the process of being able to infer meaningful relationships and predictions from vast quantities of data -- is disrupting industries in every sector. You've probably seen the impact of predictive analytics yourself: ever been surprised by Amazon apparently "reading your mind" on a suggested purchase, or by LinkedIn being able to figure out who you know, but aren't yet connected with? That's predictive analytics in action. By applying advanced statistical models to data, product designers, marketers, sales organizations -- basically, anyone who needs to understand the present or predict the future -- are able to draw value from the data they've collected like never before.
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In the coming months and years, I predict that open-source software will continue to be the driving force in analytical innovation. Open-source platforms like Hadoop, coupled with innovations in open-source file-systems, are able to adapt to the rapidly-evolving data storage and processing requirements. And it's open-source environments like R, with its world-wide community of researchers collaborating to push the boundaries of statistical analytics, that are most likely provide the novel predictive techniques required to tease yet more accurate predictions from these huge information-age datasets. Tie that with the backing of a commercial company to provide the scalability, usability, and integration into Web-based systems that businesses require to deploy predictive analytics, and you've truly got a REvolution in the making.
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