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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.

Data analysts’ three misconceptions of storytelling

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.