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Lending a hand to would-be storytellers

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.

they-want-stories

“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.

Learning from earthquake relief to design BI tools

Rescue dog. Humble, helpful.
Rescue dog. Humble, helpful.

You might say I’m crazy to see any connection between some big IT deployments and typical responses to big natural disasters — but that’s what I see. It fits a recurring theme across many disciplines of big interventions versus smaller, more humane and often more effective effort.

Yesterday on the PRI program The World, Associated Press reporter Jonathan Katz, author of The Big Truck That Went By: How the World Came to Save Haiti and Left Behind a Disaster, talked about what he saw in Haiti.

There was one mistake that was made over and over in Haiti: The people of Haiti were looked at as being bystanders, as obstacles or as security threats. It was thought that they needed to be put aside and that things needed to be done to them and for them — instead of with them or, even more importantly, following their lead. The decision to look at Haitians as obstacles in their own recovery led to creating these tent camps that ended up being an enormous focus of the relief and reconstruction effort for years to come.

The decision not to take seriously people’s needs during recovery. … They make the decision to pour in food instead of working with them to find out where they were getting their food before. The would-be helpers fail to ask, “How could we help you get food now? Is the problem that you have no income now to buy food? Were you growing food; could we help you grow it again?”

“How can we help?” shows a kind of humility that seems to underlie many of the tools I prefer, starting with QlikSense and Tableau.