Database Reference
In-Depth Information
We're sharing that framework through a deliberately placed global chapter
network. We launched our first chapter in the UK in 2012. And this sum-
mer we welcomed chapters in five additional cities—Bangalore, Dublin, San
Francisco, Singapore, and Washington DC. Volunteers lead each chapter and
will be serving as our ambassadors in their communities. The chapter loca-
tions were each chosen for their unique combination of local technical data
science expertise and the range of opportunities to work with mission-driven
organizations tackling the world's biggest problems. This means that we're
building on our track record of project work, where we've been functioning
like a Data for Good consultancy, and taking the first steps to build a global
Data for Good movement, where people are doing the same thing around
the world in their own communities, on their own, with our help and our
playbook. And of course it's an iterative process. They'll learn from our experi-
ence and framework, but they'll also find ways that work better and help us
improve our process. This year is going to be a really big year for us in terms
of understanding this process, its impact, and helping scale it out to others.
Gutierrez:
Why is it important to scale up DataKind?
Porway:
We really feel that if we're going to tackle the world's biggest prob-
lems with data science, then we want as much of a movement and community
as possible. To step back a second, I really feel like the world will be more
effective if everyone can at least converse about data science. The more we
demystify what this new “big data” resource is and take it down from being
this black-box, jargon-y field, the better. I think that maybe it should be more
like law. I don't have to be a lawyer and I don't even need to get a law degree
to do anything with law, but I know when I need a lawyer and I know what I'm
going to ask them.
So at the very least, we want to scale DataKind so we can create many simi-
lar environments where the power of data science for good can flourish and
be usable by the general population. That way we can get a nonprofit doing
important work to say, “Hey, we'd better get a data scientist in here.” That's
what's going to help us elevate the power of data science in the whole social
sector and the whole world.
This goes back to some of the principles of the way we run projects. Some
people come to DataKind and say, “Oh, I get it. DataKind does data science
projects.”Yes, we do.We do around twenty projects a year, and in the end, some
social group gets a little better. For example, Amnesty International digitized
thirty years' worth of their human rights urgent action alert data. This means
that they can better predict the urgency and prioritize incoming action alerts
based on data collected over three decades. A DataKind volunteer, Victor Hu
[Chapter 13], worked on that project and helped Amnesty International build
a model to enable them to understand, based on the information they had
digitized, how to rank these urgent alerts as they come in, and say, “Hey, really
act on this one now or something bad is going to happen.” Which is great.
