Business Analytics and Big Data

Five years ago, the best-selling book "Competing on Analytics" made a case for the use of data to inform decision making. Despite evidence that analytics yields tremendous benefits across a range of industries and use cases, most organizations still don’t compete on analytics, said information systems department Chairman Michael Goul at the Deloitte Analytics Symposium, held recently in Phoenix. A sound first step? Developing a culture of experimentation, Goul said.
Which prospects are most likely to buy what you're selling? Who are your good credit risks? What will your customers buy next? Chances are, your company uses predictive models to answer these and other questions, or soon will. A recent study found that 38 percent of companies were using advanced analytics to create predictive models that can forecast consumer behavior, evaluate consumer credit-worthiness, pinpoint marketing targets and more, while 85 percent said they planned to start using such models by 2012. But sooner or later, most of these companies will experience model decay, in which performance lags and it's time to pick a new model. How can managers judge which new "challenger" model will outperform or, at least, replace the reigning "champion?" Information Systems professors Michael Goul and Sule Balkan think one smart bet would be to try a market approach. Recent research conducted by these scholars found that "prediction markets" proved to be an effective approach to selection of predictive models.
Which prospects are most likely to buy what you're selling? Who are your good credit risks? What will your customers buy next? Chances are, your company uses predictive models to answer these and other questions, or soon will. A recent study found that 38 percent of companies were using advanced analytics to create predictive models that can forecast consumer behavior, evaluate consumer credit-worthiness, pinpoint marketing targets and more, while 85 percent said they planned to start using such models by 2012. But sooner or later, most of these companies will experience model decay, in which performance lags and it's time to pick a new model. How can managers judge which new "challenger" model will outperform or, at least, replace the reigning "champion?" Information Systems professors Michael Goul and Sule Balkan think one smart bet would be to try a market approach. Recent research conducted by these scholars found that "prediction markets" proved to be an effective approach to selection of predictive models.