Managers, listen up: Big data is not just for data scientists. Measurement does not equal insight. And technology alone won’t make analytics work for your organization.

That’s because at its core, “analytics is not a data science problem,” says Florian Zettelmeyer, the Nancy L. Ertle Professor of Marketing. “It’s mostly a managerial problem.”

Zettelmeyer points to three reasons: Analytics requires managerial judgment, it demands process and incentive changes, and it must be problem-driven to be effective.

“One of the great promises of big data analytics is that it gives you new ways to visualize information through analytics dashboards,” Zettelmeyer says. “It brings data close to managers so they can incorporate data analytics into their day-to-day decision making. But it also presents a real challenge, because now it’s up to you — the manager — to understand what the data tells you and to judge what questions to ask.”

Data created incidentally through the course of business (e.g., transaction data or Web analytics) often do not give organizations the information they need to fully understand customers, solve problems or improve processes. That’s why managers play such an important role: With their 360-degree view of customers, managers can help design measurements around customer interactions and processes that lead to the most valuable data generation. “That’s something that a data scientist alone can’t do for you,” Zettelmeyer says.

Moreover, managers can approach analytics by working backward — that is, by starting with a problem, figuring out what data they need to solve that problem and determining whether any of that data already exists. “Very often, this leads you to modify certain processes in order to be able to collect the data that you wouldn’t otherwise have,” he notes.

So if you’re a manager who’s new to analytics, where do you begin? Quite simply, Zettelmeyer says, by developing a working knowledge of data science. Hire a data scientist, ask them questions and demand that they teach you what they know. That doesn’t mean, however, you need to learn econometrics or advanced statistics.

“What very few people understand is that the most important skills in analytics are not technical skills at all,” he says. “They’re thinking skills. It takes quite a bit of practice to learn how to apply those thinking skills, but they’re not, in fact, highly technical.”

The educational investment is worthwhile, considering the far-reaching impact that analytics can have on organizations — from improving production efficiencies to creating new revenue streams to launching new product and service lines.

“I think of analytics as being really transformational,” says Zettelmeyer. “It’s a very big deal.”

Learn more about Zettelmeyer’s research at: kell.gg/si-kmci

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