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In a January 2009 article from the NYTimes, the director for technology product marketing at SAS was quoted as saying of R, “We have customers who build engines for aircraft. I am happy they are not using freeware when I get on a jet.”

Unfortunately for this marketing director, I’m afraid that freeware is already being used to build aircraft engines.  I’m curious if this SAS director ever used Google Maps to get arrive safely at a destination?  I’m wondering if she ever used the internet, where, the Apache web server dominates the internet with over 60% market share as of 2011 (per Netcraft.com).  The closest proprietary competitor in the web server space is Microsoft, which sports a 20% market share.

This list of industries where freeware has overtaken commercial competitors is lengthy and beyond the scope of this article.  However, the above quote should stimulate thought within organizations about to purchase statistical computing software.  Why would a leading commercial statistical software vendor bring up price as a weakness of R (it’s free) rather than the technical merits of the technology?  Furthermore, since when does a product being open-source suddenly make it bad?  Don’t we as readers need to know more?

Having done extensive work with both SAS and R, we are in a unique position to say something about the field of commercial statistical software and how it compares to the open-source competition, namely R. Put bluntly, we believe that commercial statistical computing software languages may be in trouble… but with some caveats.  With worthy, free competitors entering the marketplace such as R, the reasons why companies would want to pay for use of an analytical programming language have been diminished.  If the open-source technology was less capable or less reliable, then this story would be different.  However, our company has found that R works equally as well (if not better) in cases where we would have historically used SAS and/or another commercial product.  For example, we’ve had the opportunity to develop and re-develop a marketing model for our clients – both in SAS and R.  Both the development time and outcome of using both SAS and R were about the same, give or take a bit.  However, R was free to use.  Additionally, due to licensing restrictions with the proprietary solution, we were able to get our R clients moving faster into exposing their models to the web.  Furthermore, we were able to get more users working with the R model since we could freely install it on as many machines as we pleased.  This would not be possible with commercial offerings without paying a price.  Cheaper and faster with the same outcome is a good deal in our industry.

So, is there any hope for commercial statistical software?

We believe the answer is yes, but will require that these vendors adapt to changing times.  Commercial statistical software vendors, SAS in particular, are in a unique position having helped clients use their software to address business challenges for more than 30 years.  People don’t buy statistical software as the finished product.  Instead, people buy statistical software to develop solutions that address problems.  Who has better knowledge about the field of statistical solutions than the vendors who created the underlying technology?  No one – this is where commercial statistical computing vendors should focus and indeed where the market is headed.

We see analytical vendors who focus on solutions as being in a very good position going forward.  However, there is one caveat.  Focus will be key here.  But what do we mean by focus?  Vendors who offer thousands of different products to disparate markets will likely fall behind.  Most customers are not looking for vendors that can be everything to everyone.  Most customers are looking for the vendor that best understands and can solve their specific problem.  Vendors need to show discipline, restraint, and sharp resolution in their product offering.  Apple Inc has mastered this field.  While Apple has many other traits that enabled it to become one of the most successful technology companies ever to exist, Steve Jobs always had the restraint to only focus on a small set of problems that his company could become best at solving.

In summary, we see R and open-source technologies becoming the standard for statistical computing over the next ten years.  However, we believe that this shift has created new opportunities for proprietary statistical vendors, and that these vendors are best-positioned to embrace these opportunities – namely in the solutions market.   To be successful, these opportunities will likely require strong discipline and focus to ensure customers are being delivered crisp, sharp solutions that attack their problems head-on.

3 Responses to “SAS and R – Is SAS frightened of R?”

  1. Dave Garbutt December 13, 2011 at 6:57 pm #

    R can certainly lead for programming, but for many users a GUI is vital – and in this area R is a mess.

    Athough it has great graphics the interactive graphics are _not_ so great…

    To me the big growth area will be graphical analysis and reporting and well designed stats graphics that can be created easily and re-used.
    Great tho’ ggplot2 is, for most people using JMP is a more productive option.

    • admin December 14, 2011 at 11:25 am #

      Graphics, particularly interactive graphics, is an area where many statistical technologies need improvement. Indeed, having used JMP for many years, I’d agree that JMP is great for interactive data exploration to the point where drilling through layers of plots can become mesmerizing. Using graphics as a step to suggesting further analysis also adheres to good statistical technique. However, does such technology hold promise beyond this? Eh. I’m not convinced. Many good patterns can’t be visualized well. Also, doesn’t this, to some extent, perpetuate corporate data overload, leading to more poking and prodding in an effort to unearth magical golden nuggets?

      Rather, I think interactive graphics tools may remain more of an appetizer than the main course. Here’s why:

      1. Limited solution scope – Some very important and typical data patterns found in commerce cannot easily be visualized due to dimensionality, interactions, etc..
      2. Lack of automation – Graphical discovery makes the process of pattern recognition more time-consuming than alternatives, and time is money.
      3. Human bias – Graphical analysis opens the door to further human touch-points in an analysis, leading to increased potential for interpretive error and biases.
      4. Big data overload – When dealing with big data, interactive graphics often fail to suggest areas to focus attention, further perpetuating corporate data overload.

      In our work with bleeding edge companies that highly leverage analytical methods in their daily operations, I have not seen a single one move in the direction of building an analytical infrastructure centered around interactive graphics-based modeling.
      The underlying approach simply falls short. I think a more likely scenario is that tools like R will expand their interactive graphics offering, but that these capabilities will remain a piece of a much larger and flexible package. Considering that Ggplot2 was first started in 2005, it is simply amazing how fast it has developed. Best of all, its free!

    • Charles Li April 10, 2012 at 7:37 am #

      To a certain extent I agree about R lacking a proper GUI, but development moves quickly and nowadays there are one or two R Guis that are useful, even decent.

      Have a look at: the GUI section of the SciViews.org wiki:


      The Deducer Gui gives you decent point-and-click analysis functionality, as does the RCommander Gui.

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