Analytics Meets Mother Goose

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

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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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.

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Topics

Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
See All Articles in This Section

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Comment (1)
Thomas Speidel
I think the argument that the analyst should be able to tell stories from data fails to recognize that most stories we are supposed to glean from data are cognitively dissonant. Faced with this incoherence we tend to cherry pick what story to communicate: those that fulfill a vested interest, impress our bosses, get us publication in a scientific paper, or are elegantly simple to understand. We then have to wonder if we are doing a disservice to the organization by encouraging story telling rather than a more cold approach to evaluating evidence. 

The role of the analyst is not necessarily to be a good story teller, but rather as someone who can lay out the evidence as it relates to a problem and perhaps make recommendations. Not to discourage developing strong communication skills, but story telling creates all sorts of biases that we fought hard to remove  or control in the first place thus impairing our ability to make optimal decisions. Story-telling promote a paternalistic approach, rather than focusing on evaluating the evidence, be it in favor, against or inconclusive. 

It is a myth that the truth is hidden in the data, that somehow every dataset contains a story waiting to be unlocked by the best and brightest. Give the same data to ten different analysts, and you will get ten different stories. Who's right?