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Don’t Fear Big Data: NYU Stern Professor

Deepak Hegde Photo-2NYU Stern School professor Deepak Hegde used to spend a great deal of time and effort coming up with the best-possible lecture with the best possible delivery. But soon after he began to teach, about a decade ago, he started to realize that the best classes weren’t those in which he delivered the best lecture, or regurgitated the most facts, but those in which students could most actively discuss and argue about the subject at hand.

“Focusing on the questions that you want to ask students, that really helped me become a better teacher,” Hegde says. He also learned how important it was for a professor to listen to students, and focus on what they were taking away from class content, to be sure they had the necessary understanding, and to adjust the content to maximize learning, he says. And finally, Hegde says, it’s important for B-school instructors to avoid teaching the answers to questions and problems. “Students are both more likely to enjoy learning and to enjoy lessons by discovering it themselves.”


A desire for discovery makes for the best students, he says. “They have to be excellent learners,” Hegde says. “The single most important thing there is intellectual curiosity: the willingness to have an open mind, to listen to both what the professor says as well as what their classmates have to say, and being willing to change their opinion based on what they hear from the professor and the other students.”

With big data becoming a crucial asset for most companies, business schools are ramping up offerings on data analytics. But there’s a common misperception that members of a management team need to possess “deep quantitative skills” in order to analyze data, says Hegde, a former software engineer at Bosch and currently a visiting scholar at the U.S. Patent and Trademark Office, where he researches the U.S. patent system.

While big data can bring tremendous advantages to companies, important information seams can be mined effectively by managers without deep quant expertise, Hegde maintains. A working knowledge of Excel spreadsheets enables productive data exploitation, particularly in identifying patterns, he says. “You can do much better in your decision making even by looking at the data and doing very simple things to it,” Hegde says. “The first thing is not to get intimidated. We need to look at data to guide decision-making processes.”


More and more, Hegde is seeing students who want to add to their knowledge of specific fields, such as accounting or marketing, but are also looking for ways to understand the “big picture,” he says. For example, when students are learning what they contribute to a firm, they want to know about effects beyond profit-making, and even beyond the companies where they will work, he says. “We are increasingly seeing students who want to understand what the consequences of their actions are going to be, not just for the firms, but for the wider society they operate in.”