Information Age recently published a feature article devoted to the R language, "Putting the R in analytics". Says author Pete Swabey:

Already popular in universities, there are signs that R is finding increasing adoption in the enterprise. This promises to lower the barriers of entry for advanced analytics, and may accelerate the mathemitisation of business management.

The article includes an overview of the history of R: its predecessor, the S language; the transition to open-source R; the pervasiveness of R in academia; and how this is driving an increasing rate of adoption in industry.

This popularity in academia means that R is being taught to statistics students, says Matthew Aldridge, co-founder of UK- based data analysis consultancy Mango Solutions. “We're seeing a lot of academic departments using R, versus SPSS which was what they always used to teach at university,” he says. “That means a lot of students are coming out with R skills.”

Finance and accounting advisory Deloitte, which uses R for various statistical analyses and to visualise data for presentations, has found this to be the case. “Many of the analytical hires coming out of school now have more experience with R than with SAS and SPSS, which was not the case years ago,” says Michael Petrillo, a senior project lead at Deloitte's New York branch.

Like many companies today, Deloitte needs to develop analytics with varied and large quantities of data, and is using Revolution R Enterprise for big-data analytics and API integration of R:

Deloitte is currently preparing a big data pilot using Revolution Analytics’ enhanced R product. “We are using the server-based version of Revolution R to investigate big data analysis capabilities,” says Petrillo.

“We are looking at integration options to [big data programming platform] Hadoop, as well as ability to integrate R code into other applications via a web services framework.”

I'm also quoted in the article discussing the big-data extensions of Revolution R Enterprise:

Smith argues that these enhancements are necessary if R is to be applied to ‘big data’, i.e. data whose volume, velocity and variability outstrip the capabilities of conventional relational databases.

For more on the history of R and its applications in business, read the complete article in Information Age at the link below.

Information Age: Putting the R in Analytics