After long use in academia, R only recently began to appear in the business world. Among the vendors bringing R into the commercial realm are SAS, Netezza (NYSE: NZ), Revolution Analytics and IBM (NYSE: IBM), which acquired SPSS.

In just two years, Nie's new company has won blue-chip customers such as Bank of America, Motorola and Pfizer.

The company says R users will be able to process, visualize and model terabyte-class data sets in a fraction of the time of legacy products, without the need for expensive or specialized hardware.

It's incredibly diverse. It ranges from pharmaceuticals for drug developments, biostatistics, gene analysis, genetic analysis into finance. It's very heavily used in hedge funds, quantitative finance risk, credit analysis, fraud detection, understanding customer churn, understanding customer behavior, pricing optimization forecasting. We have customers who are entertainment or gaming companies using it to understand buying patterns and effectiveness of different media campaigns. So it ranges anywhere from life sciences to finance to Internet to telecom.

Part of what we are doing is helping to consumerize or democratize analytics. Because we can provide this software so much more cost effectively, it's giving opportunities for individuals, startups, small and medium businesses to come in and have equal analytic capability and processing power both from a software standpoint, but also from the more cost-effective hardware that we're able to run on to give them the same tools that these giant organizations do. So now all of a sudden we've started to level the playing field a little bit.

Revolution Analytics provides value by taking the most recent release from the R community and adding scalability and other functionality so that R can be implemented and seamlessly work in a commercial environment. Revolution R provides a development environment so that data scientists can write and debug R code more effectively, and web service APIs that integrate with other BI tools and dashboards so that R can work with business intelligence tools and visual discovery tools. In addition, Revolution Analytics makes money through professional and support services.

For example, IBM’s PureData System for Analytics (IBM’s new name for its MPP Netezza appliance) uses the in-database approach with an R instance running on each processing unit in the database, each of which is connected to an R server via ODBC. The analytics are invoked as the data is served up to the processor such that the algorithms run in parallel across all of the data.

I’ve heard that Revolution R isn’t always the easiest software to use and that the experience isn’t exactly seamless, but it can be argued that in the cutting-edge field of big data analytics a challenging environment is to be expected. If Revolution Analytics can address some of these useability challenges, it may find its pie growing even faster than it is now. Regardless, I anticipate that Revolution Analytics will continue its fast growth (already its headcount is doubling year-over-year). Furthermore, I anticipate that in-database analytics (an area where R really shines) will become the de-facto approach to big data analytics and that companies that take full advantage of that trend will reap benefits.

It's available as an Oracle 11g database option and costs $23,000 per processor license.

It's not surprising that Oracle has moved to support R, which "has become a real ubiquitous force in advanced analytics," said Forrester Research analyst James Kobielus. "It's everywhere. Enterprise adoption of it has been growing steadily. When we ask our customers what they're using for statistical modeling they'll say SAS or [IBM's] SPSS, but they increasingly say R in the same breath."

It's not surprising that Oracle has moved to support R, which "has become a real ubiquitous force in advanced analytics," said Forrester Research analyst James Kobielus. "It's everywhere. Enterprise adoption of it has been growing steadily. When we ask our customers what they're using for statistical modeling they'll say SAS or [IBM's] SPSS, but they increasingly say R in the same breath."