Publications

In this interview, author Ian Ayres describes Super Crunching, the use of randomized experiments and data mining predictive modeling techniques to elevate the performance of business processes.

by Steve Miller, November 6, 2007

With a Yale law degree and a Ph.D. in economics from MIT, Ian certainly has more than enough intellectual horsepower for his joint faculty positions at the Yale Law School and the Yale School of Management. But he's starting to establish a name in the business world as well by his promotion of Super Crunching, the use of randomized experiments and data mining predictive modeling techniques to elevate the performance of just about anything where results of alternative courses of action can be measured. Despite being relatively new for business, these methods have been used successfully to improve the effectiveness of government programs, educational initiatives, and healthcare delivery for many years, so there's a lot of precedent to guide the way.

Ian's thesis is pretty simple: Though 'experts' bring a lot to their roles managing business, education, healthcare, sports teams, etc., there's a limit to such expertise that will generally cap performance -- test scores in education, standard of living in government, profits in business, morbidity and mortality in healthcare, for example -- without the assistance of Super Crunching. Experimentation with analytics can often predict and explain better than experts, and therein lies the rub. It's very difficult for many experts to acknowledge that their judgments can be enhanced by data, equations, and especially randomized experiments. A significant challenge, one recognized and addressed in Ian's work, is getting experts and Super Crunching to coexist peacefully -- and optimally.

In the business world, Super Crunching is, of course, closely aligned with business intelligence (BI). Today's data warehouse provides the input to tomorrow's predictive models. For Ian Ayres, a passive data warehouse is a nice start, but not nearly enough. The maximum benefit of analytics will only be reached when predictive models are combined with randomized experiments -- the full monty of Super Crunching -- to determine cause and effect of strategic decisions. Want to design the ideal web experience for customers? Conduct tests. Wish to optimize customer marketing offers? Do randomized experiments or campaigns. Wish to design the next big product winner? Field test alternatives. Want to improve HR initiatives on employee satisfaction and longevity? Test alternative hypotheses with experiments. Want to optimize an investment portfolio to fund retirement? Consider an element of random portfolio allocation. Wish to learn what football plays are best in which situations? Call plays randomly.

Keep up with Ian's work at http://www.supercrunchers.com.

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