On Friday I got to pretend I was a student again. I sat in on Auroop Ganguly’s graduate class, Applied Time Series and Spatial Statistics for the second of two guest lectures on the subject of forecasting. Last time, it had to do with forecasting the financial impacts of natural disasters like Hurricane Sandy, which I wrote about with the help of two of Ganguly’s students. This time the guest was Mike Liebson of Oracle, a hardware and software company that designs advanced analytics tools to deal with big data. The topic was forecasting sales, demand and supply in the commercial industry.

At the beginning of the talk, Liebson showed two pictures of dapper young men and said “these guys made statistics cool.”

The guy on the right you probably recognize but may not know what he’s got to do with stats. The guy on the left may look less familiar but when I tell you his name you’ll know right away why he’s up there.

Brad Pitt portrayed Billy Bean in the movie Moneyball, which tells the story of how the Oakland A’s general manager revived the team on a budget using nothing but batting averages and other baseball stats. Nate Silver is the statistician who correctly predicted the outcome of the recent presidential election in all 50 states. He’s also a keen sabermetrician in his own right.

Characters like these two, Liebson said, have proven to the world that big statistics is powerful stuff. He told his own story, in which a Dunkin Donuts exec insisted for years that the coffee and sweet treat magnate could keep America running with the help of big data analytical tools like those provided by Liebson’s employer. But ultimately he convinced her that this was simply the direction the world is going and to stay on top of the market, DD would have to play ball.

In 1886, John Wanamaker, the father of the modern department store, said the following: “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” DD, Liebson said, wanted to figure out the solution to that seemingly impossible problem. They wanted to know whether an ad campaign promoting cool beverages in the summertime was actually improving sales or if the increased numbers were based purely on seasonality.

With millions of customers across the globe and as many transactions each day, crunching the data on this question is no easy task and requires some big time analytical tools. But it’s no longer impossible, because those tools are emerging from places like Oracle and SAP, their number one competitor.

The same tools allowed DD to figure out whether other products were being “canabalized” or experience “halo effects” due to the promotion, as well. That is, they could see whether lemonade sales would go down because of a promotion on coffee Coolattas, for example. Or if someone coming in for a Coolatta also left with a donut.

Big data isn’t just the messy unstructured amalgamation of tweets and other social media stats that we tend to think of, Liebson said. It’s also the increasing scale of information generated as large companies globalize and expand even further. While it seems like such a mind-boggling amount of data could never be properly parsed, advanced technologies are making it possible and actually changing the way business is done.

Baseball isn’t the only thing affected by big stats and cool statisticians. It’s just the way of the world now.