Ian Ayres book, Super Crunchers: Why thinking by numbers is the new way to be smart, is another book extolling the virtues of data-driven decision making. In that regard it is very similar to Competing on Analytics. The book focuses in on the power of data mining and other analytic techniques, especially when combined with random or double-blind studies and the kind of testing often called Adaptive Control. It asserts, and demonstrates with many studies and studies of studies, that this kind of data-driven decision making outperforms traditional experts essentially all the time.
While Ian is a little in love with the subject, and while he has created an unnecessary and irritating label (Super Crunchers) when he could have called these people Data Miners like everyone else, the book is well written and an easy read.
He has some fun examples – everything from the mathematical prediction of wine vintages to established stories like Harrahs and CapOne.
I liked the way in which he talks about the changing role of experts in this world. Not interpreting results but providing the subjective or face-to-face input that algorithms need to make better decisions. I think many organizations adopting Enterprise Decision Management will go through a similar progression. First they might adopt a purely rules-driven or expert-centric approach. Gradually as their data, and their understanding of it, improves they might tune these rules with analytic models. Ultimately they may well find that the rules are definitively subordinate to the models with most or even all of the decision making power coming from the models. Unlike the experts in Ian’s stories, one hopes the rules will not be upset by this!
One section also made a great point, highlighting in passing a potential advantage of adopting decision automation over more traditional forms of decision support. While people using decision support systems do better than people alone, they still don’t do as well as the analytic model would on its own. Decision automation, with its reliance on the model, would obviate this problem.
He does not spend enough time discussing the difference between causation and correlation nor does he talk much about the constraints that can be imposed through regulation or explicit company policy. His focus is often on one-off insight that changes how organizations do something rather than on the use of this kind of decision making in high-volume, transactional systems.
Finally I agree with him that the rise of automation in decision making will force consumers to retaliate by getting access to data, and the implications of that data, to resist the ability of companies to use data to their advantage.