February 8, 2012

Comments on SAS

A reporter interviewed me via IM about how CIOs should view SAS Institute and its products. Naturally, I have edited my comments (lightly) into a blog post. They turned out to be clustered into three groups, as follows:

Comments

18 Responses to “Comments on SAS”

  1. Philip Gove on February 10th, 2012 2:30 am

    In terms of scale-out-analytic DBMS integration, the SAS products available today (with more to come shortly) are, SAS High Performance Analytics, SAS Scoring Accelerator and SAS Analytics Accelerator. All enable exploitation of massively parallel databases (MPP). They scale to 100s of nodes, 1000s of cores. They meet the needs of any “big data” analyst.

  2. Chris on February 11th, 2012 3:05 pm

    Not sure what you mean by “mahout has been one of the less successful Hadoop projects” and how that affects SAS

  3. Curt Monash on February 11th, 2012 7:46 pm

    Had things gone differently, Mahout — with its integration into an important data store/ETL engine — might be a major threat to SAS right now.

  4. Thomas W Dinsmore on February 12th, 2012 8:11 am

    SAS currently has no reference customers for High Peformance Analytics.

    Market acceptance for Scoring Accelerator is also relatively low because it can only be used with SAS Enteprise Miner, while most analytic users continue to use SAS/Stat. Since SAS/Stat does not export PMML (or anything else), most firms opt to manually recode scoring jobs into something that will scale.

    Several vendors compete with SAS for the use cases SAS projects for HPA. The difference is that while HPA works with structured data only, SAS’ competitors bridge traditional warehousing and Hadoop. Analytics that incorporate unstructured data outperform analytics that do not; this is settled science. SAS has not yet shipped an ACCESS engine for Hadoop, so it strikes me that they are a day late and a dollar short.

  5. Thomas W Dinsmore on February 13th, 2012 8:11 am

    Couple of additional comments on SAS alternatives:

    (1) SPSS has a “legacy” in the sense that it builds on existing technology and has a customer base. In the 1990s, SAS focused effort on client-server technology, while SPSS focused on the desktop; as a result, SAS developed a reputation for heavy number-crunching, while SPSS developed a reputation for usability. Given the direction of analytics, SAS’ legacy looks increasingly like a bug, while SPSS’ legacy is a feature.

    (2) KXEN’s focus on Marketing reflects an understanding that nobody else is willing to drink their black-box “automated” Kool-Aid. There is nothing in the product that actually facilitates the kind of analysis Marketers do

    (3) Not sure it helps to dismiss R as “maturing”; show me a technology that isn’t “maturing” and I’ll show you a technology that is dead. R is rapidly penetrating commercial analytics, especially so in the health and life sciences vertical. While most organizations use R to supplement other tools, a major global life-sciences company expects to move 100% of its analytics to R by 2014.

    (4) In-database analytic packages generally don’t have the breadth of algorithms featured in server-based packages, but the 80/20 rule applies to analytics: the vast majority of analytic use cases can be covered with exactly four methods. There is no question that SAS has more “stuff” than a typical in-database package; the real question is whether or not you need all that stuff and are willing to pay the price for it.

    Also, in-database analytics developed separately from R and are not derived from it. This is true for all database platforms. Running R in-database supplements native capabilities.

  6. Curt Monash on February 13th, 2012 12:51 pm

    Thomas,

    What are the “four methods” that you feel cover the vast majority of use cases, and which do you feel KXEN lacks?

  7. Thomas W Dinsmore on February 13th, 2012 6:05 pm

    Curt,

    That’s two separate questions:

    (1) Any analytics tool needs to be able to classify, estimate, cluster and associate. Reasonable people can agree or disagree about which algorithm is best for each task, but if I had to limit the choice I would go with CART for classification and estimation, k-means for clustering and fpgrowth for association.

    (2) The issue with KXEN isn’t the algorithms they have or don’t have, but the overall black-boxiness of the affair. Not that there’s anything wrong with black-box analysis per se, but when analytics vendors are unwilling to disclose their algorithms, it’s likely because they’re using the same stuff that everyone else uses.

    That said, if KXEN is able to get better results than competing tools, nobody will care how they do it — they can put trained crickets inside the box. But tinkering around with analytic tooling rarely produces results that translate to significant business benefits, for reasons I’ll spell out in a future blog post.

  8. Thomas W Dinsmore on February 14th, 2012 8:24 am

    Additional thoughts on KXEN — no issue with KXEN’s product, which looks like a pretty good analytics platform if you can ignore the opaqueness.

    The value proposition is puzzling, though. If you’re selling to the CMO, you need to do something the CMO cares about, like media mix optimization or audience optimization. The CMO does not care about analytics, but might be interested in a solution that happens to include analytics.

    Unica figured this out fifteen years ago, when they morphed the Predictive Analytics Workbench into what is now the leading application for marketing automation.

    KXEN rightly figures that they can’t sell into hard dollar analytics fields, such as risk, fraud, actuarial, health/life sciences or capital markets, where the quants don’t value “ease of use” and won’t tolerate black boxiness. That leaves Marketing, but it’s sort of a default positioning.

  9. John Ball on February 17th, 2012 1:41 pm

    First with respect to “black-boxiness”, we have published white papers years ago outlining the fundamental approach we take (structured risk minimization). Furthermore, we have several blog posts explaining it for non PhDs in mathematics (see here http://www.kxen.com/blog/). The only people who seem to get nervous about the fine level details of our implementation tend to be competitors, not our customers (which include plenty of data scientists as well as marketers).

    Second, we have plenty of reference accounts outside of CRM (fraud, operations, risk etc) but we have focused on CRM use cases because of the required agility, productivity, and data volumes (not just rows but columns) which play to our core strengths. We do get significant performance gains over traditional approaches as seen by the numerous customer testimonials you see on our web site (http://www.kxen.com/). And I would say that to state that marketers don’t care about analytics or optimizing their marketing operations is naive at best. I can count hundreds of our customers where it simply isn’t true (does your statement reflect Netezza positioning?)

    More to come on our blog regarding the specific challenges in marketing that are in fact MORE demanding, not less, than some of the other domains.

    John

  10. Thomas W Dinsmore on February 17th, 2012 5:26 pm

    Couple of points for the record:

    (1) it’s structurAL risk minimization
    (2) my company does not compete with KXEN

    Since the great majority of citations for SRM are linked to KXEN, it doesn’t really help to give it a name; it’s still a black box.

    Marketing executives do not care about analytics for the sake of analytics; they do care about optimizing marketing operations. Those are two very different things, the distinction is clear in my previous comment, and requires no further elaboration.

  11. Curt Monash on February 18th, 2012 4:05 am

    Thomas,

    Since your company sells SPSS and quasi-sells R, it’s fair to regard you as a KXEN competitor.

  12. Thomas W Dinsmore on February 18th, 2012 11:34 pm

    My company sells a lot of different things, and in some cases both competes and partners at the same time in some categories, including analytics. We do not compete with KXEN.

  13. Curt Monash on February 19th, 2012 4:09 am

    I disagree with the claim that SPSS does not compete with KXEN.

  14. Thomas W Dinsmore on February 19th, 2012 2:29 pm

    SPSS is a completely different business unit. Netezza competes in the data warehouse appliance space. I spend 100 percent of my time working with customers who use other tools, mostly SAS. And if our customers want to use KXEN that’s fine with us, too.

    Doubtful that the folks over at SPSS think KXEN competes in the same league, either, but I don’t speak for them.

    In any case, the assertion that any critique of KXEN must be competitor FUD is simply silly.

  15. Curt Monash on February 19th, 2012 10:07 pm

    Thomas,

    I don’t recall anybody making that suggestion. But “I’m not sure whether Product X meets requirement Y” can be less compelling when it comes from another vendor than when it comes from a user, or even from a prospect who’s gone through a full sales cycle and indeed cares about the requirement in question.

    I think this is one of those cases. So far as I can tell, KXEN provides enough transparency into its models for most purposes (gut feel validation, intuition as to how to enhance the modeling, and so on). If there’s one transparency area in which it may fall short, that would be regulatory compliance — which to date hasn’t been much of an issue in the marketing area.

    (I’m not wholly convinced that regulatory compliance will remain such a small issue in the marketing area, but that’s a subject for other threads.)

  16. Thomas W Dinsmore on February 20th, 2012 12:50 am

    Curt,

    You asked for my opinion. And you knew my employer before you asked.

    TD

  17. Curt Monash on February 21st, 2012 12:39 am

    Thomas,

    It sounded like you were saying that your comments on KXEN should be regarded as more authoritative than the company’s CEO’s, because he had vendor bias and you didn’t. That’s what got you a bit of pushback.

    Everybody is biased by, if nothing else, the skews in their available information. http://www.strategicmessaging.com/money-analyst-attention-and-implied-analyst-endorsement/2011/02/28/ If one’s conscientious, one can reduce that bias; but it’s very hard to eliminate entirely.

  18. What matters in investigative analytics? | DBMS 2 : DataBase Management System Services on October 7th, 2013 1:24 am

    [...] SAS has an exceptionally broad feature set. But few parts of the SAS product line offer much in the way of [...]

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