"Those people are bringing their R expertise, their R background, and are asking for tools around R," he says. "Now what's interesting is, in an academic environment, for whatever reason, whether it's budget or familiarity, they are much more likely to be working with R without a GUI, without a strong graphical interface. And now they walk into a corporate world where their demands are higher, the turnaround frame for projects is faster, maybe ROIs are being tracked and so forth.

"Companies are able to say... what do you need to be more successful? How can we make you more productive? And they have a budget for these statisticians who may not, in the past, have had it."

If you can't beat them...

Paul Kent, vice-president of platform development at SAS Institute Inc., works for a company often seen as belonging to the opposite side of the big data divide, developing proprietary data analysis algorithms that are alternatives to those used in open-source languages like R.