McKinsey: Learning from New York City's open-data effort

InsightaaS: McKinsey & Co. is a world-leading management consulting firm; its flagship publication, McKinsey Quarterly, has been providing insight into key management issues for 50 years. This post helps illustrate the importance of McKinsey’s depth of perspective: in it, a video interview with former New York City chief analytics officer Mike Flowers covers four key topics: the cascading effect of open data, methods of breaking down barriers by using open data, the importance of coupling open data with actionable analytics-based process inputs, and the importance of helping the entire community of citizens to understand the data, avoiding “informational asymmetry.”  A transcript of the video is also provided.

Opening government data demands much more than technology, says Mike Flowers, the former chief analytics officer of New York City. In this interview with McKinsey Global Institute partner Michael Chui, Flowers details the bridges that needed to be built, internally and externally, to make New York’s open-data effort succeed. An edited transcript of Flowers’ remarks follows.

Interview transcript

Cascading effect

The Mayor’s Management Report is something that goes out every year. And it’s been around for years, predating Bloomberg.1 What Bloomberg did was make it extremely more robust. And what it is, really, is a series of KPIs2 that the city reports on things it does. You could sit there and say, “Oh, we did X this or Y that,” in terms of volume of inspections or widgets we delivered or whatever it is. But you have to be able to back up what you just said.

In order to do that, you need a back-end system that tells you how much you did. And that means you have to be tracking the system on a regular basis. It has this sort of cascading impact–the very imposition of these more robust KPIs. It doesn’t tell you how to solve the problem, but it tells you where the problems are, or at least where you should start asking questions. Then we took the next step with analytics: figuring out how to solve those problems that we were now able to understand through KPIs. Now that might seem far afield from open data, but it’s not…

Watch the video and/or read the transcript:


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