As we all know, the world is inundated with data about practically everything we do, from where we are to who we know to what we eat, and it’s an extremely exciting time to be working in a field trying to make sense of all of it. However, as I and others have pointed out, there’s a lot of effort in our discipline put toward what I feel are sort of “bourgeois” applications of data science, such as using complex machine learning algorithms and rich datasets not to enhance communication or improve the government, but instead to let people know that there’s a 5% deal on an iPad within a 1 mile radius of where they are. In my opinion, these applications bring vanishingly small incremental improvements to lives that are arguably already pretty awesome.
On the other hand there are lots of NGOs and non-profits out there doing wonderful things for the world, from rehabilitating criminals, to battling hunger, to providing clean drinking water. However, they’re increasingly finding themselves with more and more data about their practices, their clients, and their missions that they don’t have the resources or budgets to analyze. At the same time, the data /dev communities love hacking together weekend projects where we play with new datasets or build helpful scripts, but they usually just culminate in a blog post or some Twitter buzz. Wouldn’t it be rad if we could get these two sides together?