Tom Snee, Office of Strategic Communication, 319-384-0010 (office), 319-541-8434 (cell)
Majoring in big data
Majoring in big data
Majoring in big data
Big data is now big business and big money, as companies work to translate vast amounts of data into knowledge about their customers and their people, the things they make and how they make it.
So big that the Department of Labor projects 25 percent growth in the need for workers trained in analytics through 2018. The University of Iowa’s Tippie College of Business hopes to help meet that demand by establishing one of the first undergraduate business school majors to address big data.
The major— Business Analytics and Information Systems(BAIS)—will teach students to manage vast amounts of data, teasing out what secrets it holds, and then use that information to strengthen their business.
Using data to manage a business is nothing new, of course. Companies have relied on various statistics and figures for years to track their business and make informed plans for their future. But with new and improved data gathering and storage methods, businesses have gone beyond simply descriptive statistics into predictive and prescriptive analytics, which manipulates all this data to unearth revelations unseen before.
“BAIS has a strong focus on ‘predicting’ outcomes and ‘prescribing’ solutions instead of simply ‘describing’ and ‘explaining’ the past as is often done with statistics,” says Gautam Pant, associate professor of management sciences.
Jeffrey Ohlmann, associate professor of management sciences, says BAIS combines the technology to gather, store, and access the data so that it can be examined using mathematical and statistical know-how.
“In a sense, the BAIS major combines topics from computer science, industrial engineering, mathematics, and statistics and teaches them through the prism of business problem solving,” says Ohlmann.
Perhaps the field’s best known advocate is Billy Beane, the general manager of the Oakland A’s who built a competitive team in a small market by using numbers in a way that no other team was using, a story captured in the Michael Lewis book and Brad Pitt movie Moneyball. Beane had seas of data to choose from thanks to baseball’s penchant for keeping records for most everything any player ever does, even if no team had thought to use it to gain a competitive edge before he did. Similarly, data that can help business gain an edge is gathered virtually everywhere today.
Credit card companies track purchases to see where their customers shop and what they buy; retailers use loyalty cards to send coupons based on a customer’s purchase history. Companies use radio-frequency identification tags that track products to design more efficient supply chains, and websites track click-throughs to figure out the best way to advertise online.
When customers buy something online, they leave behind massive amounts of useful data. Trackers record what websites were visited, which products were considered, what was purchased and what wasn’t, what credit card was used, what else was purchased with that card, what warehouse it came from, and how quickly it moved from the warehouse to the customer.
Researchers are now moving analytics into areas of business where they hadn’t been used before, including workforce planning and employee training. Barrett Thomas, associate professor of management sciences, and Ken Brown, professor of management and organizations, were recently awarded a $218,000 grant from the National Science Foundation to develop new models and methods for organizations to improve their workforce training and on-the-job learning to better compete in a global economy. The research combines data mining and operations research, as well as organizational psychology.
“The models and techniques created in this project will allow companies to learn more effectively than their competitors, leading to more effective use of human capital and providing a sustainable competitive advantage,” says Thomas, just like Beane has done with the Oakland A’s.
Thomas says work of this type would have been difficult in the past, when the most effective way to gather productivity data was to send a team of researchers onto the shop floor with stopwatches and clipboards and actually time how long it took to do something. Now, all of that data is measured automatically and stored by technology, waiting for researchers to sift through it.
“Computers track how long it takes for a factory worker to complete a task, or how much time operators spend on the phone with callers in call centers,” he says. “This data is out there. We just need to make use of it.”
Thomas says developing more effective methods of worker training is especially important now, as more and more employers report difficulty finding workers with the right skills for job openings. If analysts are right in their prediction that many manufacturing jobs will be coming back to the United States in the next few years, a skilled workforce needs to be waiting for them.
Thomas says the goal of their project is to figure out how to incorporate employee training into workforce planning and job assignment, so businesses can avoid putting the wrong worker into the wrong position.