Reflections

Posted 16 May, 2014 by Alix Dunn

DataKind Tests Out New Project Accelerator

DataKind Project Accelerator

   Photo courtesy of DataKind.

Social change organizations face a lot of challenges in their work, and support organizations are working hard to design creative ways to tackle them. As we launch our partnerships pilot in Southern Africa and Latin America (more on that here) we’ll be testing out different types of support, and will be taking a close look at how our partners are experimenting too.

As part of that exploration, we took part in a DataKind event in New York last week, and it was an extra special pleasure because it was the first time DataKind attempted a new approach they are calling a Project Accelerator. DataKind pairs social good projects with pro bono data scientists through deep-dive events – appropriately called Data Dives, and they also manage longer term support by managing pairings between and volunteer data scientists over 6 month periods of time. DataKind’s Project Accelerator is designed as a strategy support process, meant to jumpstart thinking about how data can be used in projects.

How did the Project Accelerator work?

Three charities were selected through an application process to present their projects. The charities included: Kinvolved, an app for supporting better attendance in public schools; RentSpecs, an app for rating the quality of landlords (and slumlords); and Cool Culture a program to support lower income families access to city museums. Private sector data scientists and a few others (like the engine room) grouped up with each of the charities. We asked a lot of questions, made suggestions, and when possible provided concrete help in thinking through data problems. Charities took notes, asked their own questions, clarified their problems, and had a few aha moments.

How did it go?

The room was brimming with accomplished data scientists who were eager and willing to help charities address problems that these scientists tackle in their everyday work. And while sometimes the expertise was in need of a clearer target – smart science minds were tackling sometimes fuzzy project goals – the data scientists managed to frame questions well, and if our group was any indication, conversation was smooth and stayed focused on the problems that the charity was facing.

We’re excited to see how DataKind evolves its support process and excited to matchmake partners when projects call for support from an amazing group of data scientist volunteers!

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