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Tuesday, March 12, 2013

Data hackathon challenges and why questions are important

Data hackathon challenges and why questions are important

Mar 12, 2013 04:38 am

Jake Porway, executive director of DataKind on data hackathons and why they require careful planning to actually work:

Any data scientist worth their salary will tell you that you should start with a question, NOT the data. Unfortunately, data hackathons often lack clear problem definitions. Most companies think that if you can just get hackers, pizza, and data together in a room, magic will happen. This is the same as if Habitat for Humanity gathered its volunteers around a pile of wood and said, "Have at it!" By the end of the day you'd be left with a half of a sunroom with 14 outlets in it.

Without subject matter experts available to articulate problems in advance, you get results like those from the Reinvent Green Hackathon. Reinvent Green was a city initiative in NYC aimed at having technologists improve sustainability in New York. Winners of this hackathon included an app to help cyclists "bikepool" together and a farmer's market inventory app. These apps are great on their own, but they don't solve the city's sustainability problems. They solve the participants' problems because as a young affluent hacker, my problem isn't improving the city's recycling programs, it's finding kale on Saturdays.

Without clear direction on what to do with the data or questions worth answering, hackathons can end up being a bust from all angles. From the organizer side, you end up with a hodgepodge of projects that vary a lot in quality and purpose. From the participant side, you're left up to your own devices and have to approach the data blind, without a clear starting point. From the judging side, you almost always end up having to pick a winner when there isn't a clear one, because the criteria of the contest was fuzzy to begin with.

This also applies to hiring freelancers for visualization work. You should have a clear goal or story to tell with your data. If you expect the hire to analyze your data and produce a graphic, you better get someone with a statistics background. Otherwise, you end up with a design-heavy piece with little substance.

Basically, the more specific you can be about what you're looking for, the better.

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