MassMutual, a $30 billion per year life insurance company, had a problem. It was 2013 and, along with the rest of the insurance industry, it was bedeviled by fraud. According to FBI estimates, fraud sets the U.S. insurance industry (and policyholders) back by $40 billion a year. “We had to get much better at detecting fraud in real time,” says Sears Merritt, MassMutual’s chief of technology strategy and data science.
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.