SaaS data loss: The problem you didn’t know you had

Blog Recently I met the analytics firebrands Revolution Analytics at their Bay Area offices.

Outwardly, the facilities were conventional, and no one called me ‘comrade’. But what they showed me over the next couple of hours sparked my imagination and even a bit of revolutionary fervor.

The guys at upstart Revolution Analytics could be key players in this trend. They’ve taken the open source "R" statistical programming language, enhanced it, and packaged it in a user-friendly wrapper. The enhancements are significant. The Revolution Analytics distribution of R scales across multiple cores and processors, while the open source version doesn’t. Standard open source R also has built-in limitations on data size (limited to the size of RAM on the system). Revolution Analytics has fixed this problem with its version, Revolution R.

Statistically speaking

The demonstration of the speed and power of Revolution R was impressive. To me, as a layman, the easiest way to understand the difference between R (and Revolution R) and industry stalwarts such as SAS or SPSS is to realize that R is a statistical language, while the others are applications. This difference has a profound effect on what can be done and how quickly and easily it can be accomplished. SAS and SPSS are big and powerful, but as applications, they are black boxes.

You set up what you want to do, run it, and get the output when it’s done. If you want to make changes or try additional routines, you must either use their built-in routines or write your own scripts. With R, you simply specify what you want to do and do it. R commands are entered in a style that is geared toward statisticians, not computer programmers. Since it’s a language, not a program, researchers can use many statistical techniques on their data in whatever combinations they want.

The choices are much more constrained with applications like SAS or SPSS. Of course, there are downsides to this approach. The incumbents would say that R isn’t user-friendly, and that its very flexibility makes it more difficult to use.

For researchers and statisticians, this argument doesn’t wash – they know their way around stat routines and can hold their own with R. With somewhere around two million R users in academia and industry, and almost 3,000 task- or industry-specific plug-ins, it’s safe to say that users are seeing solid benefits from R. There are two arguments that the SASes of the world won’t use against Revolution R: price and performance. The cost of Revolution R is half or less than that of SAS, and performance improvements range from twice as fast to “much, much faster” – depending on what you’re doing.

We’re not just talking about execution speed here; we’re also talking about analyst productivity. With R, it’s quicker to set up analytical routines and much quicker to run multiple routines, or shift to different analytical techniques on the fly. The value of this is hard to quantify, of course, but it is significant. In the right circumstances, it could be profound.

But for analysts on the business side of the house who are more accustomed to using packaged apps for analysis, R might be a bit intimidating. Revolution R is working to remedy this with a browser-based GUI that will make the program much more intuitive and less scary for us business types. To me, the GUI might be the piece that moves Revolution R into the big time.

Of course, it could be argued that they’ve already edged into the big time with their recently inked agreement with IBM’s Netezza unit, which will integrate the Revolution R Enterprise product into Netezza’s TwinFin Data Warehouse appliance.

This gets Revolution R into a major vendor’s catalog and opens them up to a much larger and more diverse customer set. More importantly, this is a significant sign of credibility and stability that will serve to convince other customers to give it a shot. ®

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