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Predictive Analytics are important no matter what IBM thinks


Doug Henschen had a blog post on IBM today that caught my eye – Will IBM Add Analytics to its Toolbelt? in which he quoted Ambuj Goyal (who heads up information management at IBM) as saying predictive analytics are overrated. Sadly this reminded me of the old days of IBM – when FUD (fear, uncertainty and doubt) was IBM’s reponse to anything they did not do well. Predictive analytics are not overrated, at least not by anyone who understands them. It is true that predictive analytics, like all good technologies, are sometimes overused by over-enthusiastic supporters and that they can’t do everything. IBM’s lack of this technology is a mistake as without it their solution set is incomplete and no amount of FUD will change that.

Now Ambuj is right about a couple of things:

  • He’s right about the need for a trusted data layer – bad or mistrusted data does not enable good decision making.
  • He is also right when he says, though not quite in so many words, that decision making takes more than data mining and predictive analytics – it takes optimization, business rules, adaptive control. This is why enterprise decision management takes all these components together. Where he is wrong is in not including predictive analytics and data mining (technology he does not have) as peers to the technologies he does have (or will have once IBM completes its ILOG acquisition).
  • He is also right about the power of vertical solutions and vertical templates to accelerate time to value and address issues of skills shortages.

To argue, however, as he does that Cognos is a platform for advanced, automated decisioning is disingenuous. Like any BI platform, Cognos is not the place for automating decision making as it is human-centric – designed purely to present information to a human decision-maker. It is aimed at decision support not decision management.

In the article Ambuy says:

Predictive modeling has become a hammer for these vendors, and in many cases it’s overkill. For example, we were recently talking to a client that has created a 100-terabyte model, using software from one of those vendors, to do prediction. They’re using an amazing amount of computing capacity because the only method that they had available was a statistical model coming from one of these vendors. But the problem could have been solved much more simply with a policy engine, which could have been created for less than $1 million rather than requiring tens of millions of dollars of expense.

Are they very good at constrained logic programming or Markov decision processes or rules engines? No, they are not.

Well of course this is why I talk about decision management not just analytics. The important thing is to focus on the decision and then figure out how to solve it – hence decision management.

Ann Milley of SAS commented also and said Hey, IBM, It’s About Competing on Analytics! Ann makes some great points:

  • She emphasizes “continuous learning and improvement” or what I call adaptive control
  • She reiterates that this is about “more than prediction. It’s all about delivering business value”

It is, in fact, not about competing on analytics, but about Competing on Decisions (to use a great phrase that Neil Raden came up with and on which we presented recently).Analytics matters a great deal to companies competing on decisions, which is where IBM is wrong, but it does take more than analytics, which is where SAS is wrong. Business rules, optimization, data mining, predictive analytics and adaptive control – the necessary ingredients for Enterprise Decision Management and business success.


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