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Lending a hand to would-be storytellers

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Advocating data storytelling is easy. But actually making a data story out of data is something else. As much as storytelling could help to deliver meaning, says one consultant who works up close to the action, many data analysts just don’t even know where to start.

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“If no one teaches you how to tell a story, how do you do it?”says DecisionViz president Lee Feinberg. How do you expect them to tell a story in words or visualizations? “Face it, most of them aren’t even good at writing or English.” What might be hard even for natural storytellers is making a set of reports into something meaningful.

In one project, an advertising agency with mounds of data had to convince a client that spending money with them was worthwhile, and to make recommendations on expenditures. The agency wanted to show media buy did well or did badly. What went up or went down? What’s it mean? Why did it happen? They know they need to do it, but they just don’t know how. He helps them through it.

“In their gut, they knew that what they should be doing was not reporting,” he says. But if they issue reports without having thought through to the final message, they actually spend more time in the end after reports are sent back and have to be done over.

He offers analysts a structure and a methodology. He shows them how to break findings into pieces.

Do organizations need roving storytellers? I imagined a thin old guy with long, graying hair and a wizened face wandering among the cubicles and speaking up at crucial meetings. Would such a specialist help? Feinberg, perhaps humoring me, said it might.

Data analysts’ three misconceptions of storytelling

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I asked Fern Halper, director at TDWI Research, what misconceptions data analysts have about data storytelling. She and I wrote a report last fall for TDWI Research on data storytelling, and she was the data analyst on that team.

1. That data storytelling is easier that it really is. That sounds right to me. After all, everyone knows how to tell a story, don’t they? Sure, just like everyone knows how to analyze data, at least if you have a tool that’s “easy to use.”

2. That people care about the details. That rings true, too. As we said in the report, some do and some don’t. Fern recalls a vice president at AT&T early in her career. He said, “Just tell me something that is 80 percent correct.” For him, good enough was good enough. She says now, “What I too down in the weeds? Probably.”

3. That a data story is a presentation. A data story shares some characteristics of a presentation but it’s much more than that — far too much to summarize here. Go read the report.

Quants vs “accidental analysts,” up close

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In just two meetups of data analysts, Dave Wells has seen the quant-versus-accidental analyst rivalry surface.

He is the former TDWI education director and now a consultant and based in Seattle. He’s taken an interest in the last few years in data analysts.

The quants are by far the smaller group, but they’re loud. At the meetups of his group Business Analysts Collaborative, they amount to less than 10 percent of the 150 who’ve attended either event. These are the ones with training, the ones who squeeze all the meaning anyone could possibly find out of data. Their findings are solid.

But they seem to be defending territory. “They’re adamant,” says Dave, about the need for training as an analytic professional. They insist that anyone analyzing data needs to understand the algorithms, the steps to prepare data, and so on. If you don’t know what you’re doing, they seem to say, stand away from the data.

That the Business Analyst Collaborative attracts so few quants is fine with him. “The quants are already somewhat of a community, with the velvet rope and bouncer and all that crap,” he says. “I want the neighborhood bar.” [Read more…] about Quants vs “accidental analysts,” up close