How MBA schools are pushing students to build up their data literacy skills

Companies have more raw data on customers, markets and trends than ever before. MBA schools are training a new generation of execs to untangle it

 
Canada‘s Best MBA Programs 2017
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(Illustration by Chris Gash)

Every other weekday, Divya Tulapurkar rides a Via train from Toronto to Kingston, Ont., to go to school. It’s a sizeable commute­—about two hours one way—but she thinks it’s worth it. “Plus, it’s actually a really great time to get work done,” she says.

The reason for Tulapurkar’s frequent travel? During the week, she attends classes at the Smith School of Business at Queen’s University in order to earn her MBA degree. On evenings and weekends, she treks back to Toronto for classes in the school’s master of management analytics program. Tulapurkar’s fiancé took the same concurrent MBA-MMA program at Smith last year; in the end, he found himself choosing between five job offers. “There’s just so much demand for the talent right now—for people who can take statistical findings and apply them to business settings,” she says.

It’s no secret that data analytics skills are in demand. With the rise of the Internet, innovations in e-commerce and the ubiquity of mobile devices, companies are swimming in information about customer behaviour—more data than they know how to handle, really. Businesses are desperate for individuals who can comb through these large, unwieldy data sets (OK, we’ll use the buzz phrase: Big Data) and uncover new insights from which to profit. That’s why data scientist is one of the most in-demand jobs around right now.

Statistics and analytics have long been components of MBA programs, but some schools are placing a greater emphasis on bringing together management and data skills by introducing courses, specializations and joint degrees with Big Data themes. Tomorrow’s senior executives won’t need the skills of a data scientist, but they must be data literate. “Future business leaders need to understand how data and analytics work,” says Murat Kristal, director of the master of business analytics program at York University’s Schulich School of Business. “They need to be fluent to be able to communicate with data scientists. Failure to do that will cause them to lose their edge against the competition.”

Students should be aware that the material can be somewhat technical. In MBA-MMA programs, for example, students are taught to use computer-based data analysis tools such as SAS, R and Hadoop to manipulate data sets and apply the information to business case studies. Data visualization programs like Tableau, which helps simplify and display information, are also taught.

While some of the jargon might seem intimidating at first, MBA candidates should remember there are real-world applications for these tools—ones that can net companies serious profits. “A classic example is the way Amazon looks at your past purchases and makes recommendations about books you might like to read, thereby increasing their sales,” says Lorraine Dyke, associate dean of professional graduate programs at Carleton University’s Sprott School of Business. Sprott developed an analytics stream for its MBA program in 2013, aiming to “empower students to use data to enhance decision-making.” The MBA students pursuing that option also collaborate with their peers in the full-time master in data science program.

According to Dyke, there are countless examples of companies leveraging analytics to their advantage. To retain loyalty, T-Mobile monitors high-value customers and offers perks when they are experiencing service problems. Starbucks analyzes traffic patterns and demographic information to identify viable locations. And online advertisers, of course, dissect huge amounts of data to tailor ads to individual users.

Savvy students have taken note. After completing a bachelor’s degree in business administration, Lucas Calestini enrolled in Schulich’s master of business analytics to get an edge in a competitive job market. While the year-long program is not a traditional MBA, it does expose students to management, leadership and business concepts. Candidates attend MBA classes 40% of the time while studying with data science students for the remaining 60%. “From day one, the focus was always on getting us into the workforce,” Calestini says. Indeed, immediately after graduation he lined up a job at marketing and loyalty firm Aimia, where he works as an analytics consultant on recommendation algorithms. These sophisticated bits of code measure consumer preferences; if a customer likes popcorn, she might also be interested in soda. A supermarket can use that information to offer deals to that customer.

While MBA candidates aspiring to leadership positions likely won’t have to, say, build recommendation algorithms themselves, they will have to understand such concepts in detail. “Speaking the same language helps executives to get the most out of their data analytics teams,” says Kristal at Schulich.

That’s partly why the DeGroote School of Business at McMaster University launched an EMBA in digital transformation this year. “Executives of organizations of all sizes need to understand how data transforms their world at a strategic level,” says Milena Head, the program’s academic director. “We had observed how there are a lot of [masters of science] in data analytics, but they tend to be very technology-focused, and they don’t really tap into the managerial side of things,” she says. While the program will provide some hands-on experience with analytics programs and tools, the focus is less on developing actual technical skills and more on applying those tools and theories to better understand markets, trends and customers, and on communicating effectively with the data experts.

Michael Batch, chief innovation officer at Siemens Canada, saw a clear benefit in enrolling in the program. He had been thinking about signing up for an EMBA program for years, but the available options were “too traditional,” he says. “When I ended up finding the DeGroote program, the focus felt very relevant to my interests,” he says. “A company like ours has a huge customer base, and we traditionally haven’t utilized the data that comes from it. We feel that being able to gather and analyze that data gives us the potential to go far beyond where we are now.”

Take the company’s power generation equipment, for example, which can be found across Canada and is equipped with sensors. “We’re able to leverage this data to offer new insights to other players in the domain,” he says. “We’re able to offer them knowledge and capabilities they would not usually have.” Siemens can sell this data to third parties who might be interested in using it to, say, research wind speeds or pollution levels. Batch believes the skills he’ll learn during the program will also allow him to better work with the data scientists Siemens currently employs. “It’s all about how to best use the information we have to create value for our customers,” he says.

As more business decisions become data-driven, it’s essential that managers and leaders better understand the field, Head argues. “We must never lose sight of the importance of the human element to ask the right questions and manage this transformation,” she says. “The data can help answer questions, but knowing the right questions to ask can be much more challenging than any technological hurdles.”

Tulapurkar says her area of focus is workforce analytics, and she sees herself working as a chief human resources officer in the future. “I want to use analytics as an enabler for efficient HR planning and operations,” she says. Her cominbed MBA and MMA degrees will place her at a distinct advantage, she argues. “As I talk to people in the industry, I can see the value of an individual who can proficiently speak the language of not only business executives but also data scientists,” she says. “This, to me, is very important.”

There has undoubtedly been a lot of hype around the concept—witness how often the term “Big Data” is still thrown around—but Calestini, for one, has a straightforward view of the analytics boom. “At the end of the day, it’s about simplifying things for business needs,” he says. “It’s about finding out what the business needs to know and putting that information to work.”



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