Here is a brief excerpt from a brilliant article by Michael Fitzgerald for the MIT Sloan Management Review. He suggests that data scientists tend to work in relative isolation and focus on their data rather than the “big picture” their data sheds light on. Instead, they need to create a simple story for their work — as simple as the tale of Mother Goose. To read the complete article, check out others, and obtain subscription information, please click here.

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Want to get your point across about data? You’d better learn to tell stories.

Data scientists tend to work in relative isolation and focus on their data rather than the “big picture” their data sheds light on. Instead, they need to create a simple story for their work — as simple as the tale of Mother Goose.

Analytics is not just about data. In fact, getting too caught up in the data can obscure what the data means. And what it means is what matters in business.

But data scientists often get more involved in the data. “When I ask analysts to tell a story, they feel like they have to talk about their data and their process. That’s what they should not be talking about,” says Meta (pronounced May-tah) Brown, an analytics consultant and speaker who just published Data Mining for Dummies.

Instead, they should focus on telling the business story their data shows. One exercise she gives her clients is to develop a 60-second story from their data. She uses this to force data scientists to talk not about their data and processes, but on one thing the data says about the business’s customers. Most of them struggle.

This is the “last mile problem” of analytics, says Arvind Karunakaran, a PhD student at MIT’s Sloan School of Management who is studying how companies can get better decision making from analytics. The phrase refers to the problem of getting connectivity from telecom and ISP endpoints over the “last mile” they travel to arrive in homes — a thorny bandwidth conundrum. In analytics, this last mile is making connections between the data and executives.

The good news is, you don’t need a PhD to communicate — just practice and a shift in focus. Brown tells students keep points short, talk in dollar terms, and don’t overdo the details. Pay attention to what interests your audience, and respect it. Remember that they are the ones with the power to act on your insights.

While that sounds simple, Brown says most data scientists go to graduate school, and “people who go to graduate school are being trained to write in a way that most people cannot understand.”

Brown experienced this first-hand when she was working on her MS in nuclear engineering at MIT. She walked in to her advisor’s office one day and found two chapters of her thesis in the trash. He told her to rewrite them in a more academic style — one she felt was less readable.

Brown, who spent more than a decade in various roles at SPSS, a maker of statistical software packages, says the number of requests she gets from companies to help train data scientists to communicate jumped in the last couple of years, as the phrase Big Data became widely used. Usually, she hears from business managers that they can’t understand the point their data scientists are trying to make.

Compounding their communications woes, data scientists tend to work in relative isolation and focus on their data rather than the “big picture” their data sheds light on. The people they talk with most often are probably other data scientists. In contrast, information technology workers will probably work every day with people in marketing and sales and operations, and will learn in a hurry if people can’t understand what they’re saying.

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Here is a direct link to the complete article.

Michael Fitzgerald is a contributing editor at MIT Sloan Management Review.