So MassMutual launched an innovative collaboration between the company’s data scientists and its line managers. They created a new role, product managers, who act as translators between the data analysts and the day-to-day decision-makers who run the company’s various lines of business. At the outset, the product managers gathered information from each department—life, disability, long-term care, and so on—and explained to data analysts exactly what was needed to spot, and thwart, fraud in each area. The data scientists then culled and customized the relevant numbers, which the product managers helped line managers translate into specific antifraud moves.

Now MassMutual relies on this approach companywide, far beyond fraud detection. It works “in every process, in every line of business from marketing to underwriting to claims,” says Merritt. “The results have been really impactful.”

The collaboration between data scientists and line managers has pinpointed inefficiencies and identified new pathways to growth. That has boosted MassMutual’s revenues and profits by “tens of millions of dollars,” says Merritt, and the product managers “have been crucial to making it happen.”

Data is proliferating at warp speed, but data literacy among managers and executives hasn’t caught up. That’s why data translators have, according to the Harvard Business Review, become a “must-have analytics role.” Even if you’ve never heard the term data translator, you may already be working with one. Because they go by so many different titles—like MassMutual’s product managers—no one knows how many translators exist right now. But there’s no doubt that people who are adept at interpreting data for practical use in the real world are a hot commodity. By 2026, the McKinsey Global Institute predicts that there could be a demand for 2 million to 4 million translators in the U.S. alone.

At the moment, hiring translators isn’t easy. That’s partly because the job requires a unique combination of skills, usually including both a strong grounding in data science and a talent for boiling complex ideas down to clear, practical choices. They’re so rare that translators “belong to a category recruiters call ‘unicorns’,” notes Brad Stillwell, vice president of product strategy at Birst, a unit of global cloud software giant Infor.

Stillwell has hired a number of translators in his 18-year career. He notes that though artificial intelligence can be used to advise line managers on some issues and answer some of their data-related questions, it can’t replace humans. “There is still an art to it,” Stillwell says. “Business decisions often have to be made based on incomplete information, using intuition and creativity, and without much time. So the ideal translator is equally adept at both left- and right-brain thinking.”

That’s why “liberal arts graduates, collaborating closely on a team with data analysts, often make great translators,” Stillwell says.”Someone who majored in history may not know how to do a linear data progression, but they often do know, from studying historical data, how to spot patterns and infer where the data might lead.”

As if a mathematical bent and a knack for communications together weren’t scarce enough, the most effective translators bring with them one more thing: a thorough knowledge of the business they’re working in. Without that level of information, they won’t be able to understand what line managers need to glean from the data and why. People with this trifecta of talents are so scarce—so not just unicorns but pink unicorns with purple polka dots—that many companies have given up trying to hire translators from outside and are training them in-house instead. McKinsey, for instance, launched its own internal academy a few years ago, which now turns out about 1,000 data translators annually.

MassMutual has taken this route, too. The insurer launched its Data Science Development Program (DSDP) in 2014, in partnership with five colleges near its western Massachusetts headquarters. After a data-intensive three-year curriculum at the schools, including Smith, Mount Holyoke, and UMass Amherst, graduates join MassMutual as junior data scientists, while attending grad school in data science at the same time. The new hires work alongside senior colleagues on applying data to the everyday, real-life business challenges that MassMutual line managers face.

The DSDP program, which has about 20 people and a handful of grad students at any given time, offers training in both data science and translation. “Algorithms can only tell you what business problems you can solve,” says Sears Merritt, who runs the program. “But human judgment and intuition can go way beyond that, and tell you what problems you should solve.”

The story behind data

In the years ahead, we may all need to learn some data translation skills. “We need a new generation of executives who understand how to manage and lead through data,” says Salesforce CEO Marc Benioff in Nancy Duarte’s new book, DataStory: Explain Data and Inspire Action Through Story. “And we also need a new generation of employees who are able to help us organize and structure our businesses around that data.” Just about every job, in other words, will call for data-translation skills that most employees haven’t used, or needed, yet.

Of course, some people have a natural ability to render dry facts colorful and interesting, especially the born raconteurs among us. Nancy Duarte, head of Silicon Valley communications firm Duarte, Inc., believes that storytelling—turning plain, eye-glazing heaps of data into a vivid tale its audience can easily grasp and remember—works better than most other translation techniques, especially when it comes to persuading people to take a specific course of action.

That’s because the human brain is apparently hardwired to crave narratives with plots that include a beginning, a middle, and an end. “MRI images show that telling a story based on data lights up the human brain in a way that data alone just can’t match,” she says, adding that data is “useless without human experience and judgment. If we relied solely on machines to make decisions, they’d all be the wrong ones.”

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