April 8, 2011

Revolution Analytics update

I wasn’t too impressed when I spoke with Revolution Analytics at the time of its relaunch last year. But a conversation Thursday evening was much clearer. And I even learned some cool stuff about general predictive modeling trends (see the bottom of this post).

Revolution Analytics business and business model highlights include:

Revolution Analytics’ top market sector by far appears to be financial services, both in trading/investment banks/hedge funds and in credit cards/risk analysis. Pharma/life sciences is second, but sales cycles are slow. There’s also been at least a little activity each in a variety of internet/media/entertainment/gaming/telecom sectors.

When I asked Revolution Analytics why one would use R rather than, say, SAS, Revolution cited three reasons that seemed to be driving customer interest:

Revolution Analytics’ parallelized-R story starts something like this:

Like Netezza with nzMatrix or Greenplum (now EMC) with its sparse vector routine, Revolution has some useful underpinnings to help with parallelization/scale-out as well. The main one seems to be a variance/covariance matrix, which can be arbitrarily large and can be computed in a very distributed way. Revolution notes that you can use this not just on data but also, for example, on parameters.

One analytic approach — if not meta-approach — that Revolution sees as hot is ensemble learning. Specifically mentioned was Max Kuhn’s caret package, which evidently automates ensemble techniques. Also specifically mentioned was the Netflix Prize, which I gather was won by an ensemble approach. The idea behind ensemble techniques is that, rather than pick a particular kind of model, you throw a bunch against the wall. The first benefit is that you get to see what works best. The second benefit is that you can combine results and hopefully outperform any one of the models.

Obviously, ensemble techniques can require vastly more performance than just running a single model. I wouldn’t be surprised if, going forward, they turned out to be one of analytics’ biggest performance challenges.

Comments

2 Responses to “Revolution Analytics update”

  1. Ajay Ohri on April 8th, 2011 11:44 am

    R basically has 2396 packages http://cran.r-project.org/web/packages/. SAS admits R is more extensive in terms of statistical functionality and offers extensions from JMP/SAS /IML, and there are SAS language clones WPS with a Bridge To R extension.
    As hardware expands from PC/Server to Clouds with on demand resources, SAS’s main advantage of faster processing of data disappears. However it has much more maturity and size in business intelligence.
    Students prefer to learn SAS than R, though this is changing with newer R GUIs. One reason is Job Market gives a premium to SAS skills. Even SPSS skills are more preferred by career mind students.
    The innovation is R in graphics and data analysis and GUI are coming from community, 2011 was the year Revolution was going to launch their own GUI (from an IDE currently)
    There is inherent tension between Revolution contributing 6-10 packages and claiming credit and sales for remaining 2380 packages.
    given the cost savings from a correct analytics solution , price is not a reason for SAS to get worried from bottom feeders

  2. High Performance Analytics « DECISION STATS on April 22nd, 2011 2:32 pm

    [...] Revolution Analytics update (dbms2.com) [...]

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