Intelligent Enterprise

The gist of Ambuj Goyal's message in this Q&A interview is that predictive and statistical modeling — key offerings for the likes of SAS and SPSS — are overrated. IBM has what Goyal describes as better, cheaper alternatives in a mix of techniques developed for industry- and domain-specific challenges. Okay, I'm fine with challenging conventional wisdom and seeking the simplest possible solutions, but I also believe there's good reason SAS, SPSS, and a few other analytics specialists have grown large and stable businesses. What's more, I won't be surprised if and when IBM acquires one of these analytics vendors.

To hear Goyal tell it, SAS and SPSS are using their advanced analytics techniques as "a hammer," applying them to many scenarios in which they are not the best tool:

You won't be surprised to hear that IBM has bundled up all these and other "best way" techniques into prepackaged solutions (including 14 listed in the interview). These solutions will be attractive to many, I'm sure, particularly those who have yet to invest in analytics expertise and development environments. But the gurus already on staff may be skeptical about any claim to offer "the best" approach for everyone. These professionals — whether employed by banks, insurance companies, auto manufacturer, retailers or health care concerns — are used to testing multiple models, using their own data to prove which approach is best for their company-specific scenario. In fact, these people may pride themselves on developing a unique competitive advantage, so they may scoff at the idea of a "best way" for everyone.

Everyone should be receptive to Goyal's point that you have to look beyond just analytics when considering best-possible techniques and approaches. He cites an example of a firm that took an elaborate, and highly expensive, predictive modeling approach to tackle a problem that could have been solved much more simply and inexpensively. In these times in particular, it's a good idea to consider whether, say, a rules engine or another alternative might offer a better, faster, cheaper way of doing things. An analytics champion, after all, may not be inclined to think outside his or her own bag of tricks.

Could it be that IBM is attempting to tarnish the halo that predictive analytics seem to have earned in recent years? Mainstream interest was certainly stoked by Tom Davenport's book Competing on Analytics. Yet despite its $5 billion investment in Cognos, IBM doesn't offer anything akin to the all-purpose analytics development environments offered by SAS and SPSS. For now, Big Blue is expanding on its portfolio of industry- and domain-specific solutions. But if you peek at the "customers" tabs on the SAS and SPSS Web sites, it's easy to understand why IBM would be only too eager to add a hammer to its ever-expanding tool belt.

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