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.
"Polymath" is the Greek word for Renaissance man -- one who excels at many things. But if, centuries ago, society needed a Da Vinci or a Franklin, the grand challenges of today call for teams of experts. Author Vinnie Mirchandani in his book by the same name describes "The New Polymath" as an organization that gathers together diverse teams of specialists and multiple strands of technology to resolve not only our daily needs -- for a smart car for example -- but also meet the "Grand Challenges" that face us -- like assuring an ample supply of clean water. He urges that companies as well as individuals begin to think in terms of "And not OR." But why should enterprises expand their aspirations? Mirchandani shows that by embracing the truly big picture, these enterprises are able to identify market opportunities as they are emerging. Mirchandani was the guest of the CABIT research center at the W. P. Carey School of Business recently. Center Director and Information Systems Professor Julie Smith David talked with Mirchandani after his presentation.
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.