This weekend I volunteered for my first Data Dive, DataKind UK’s initiative to connect data scientists with charities and social organisations to help them tackle some of their biggest challenges. This month’s Data Dive focused on Buttle UK, the Access Project, Shooting Star Chase and Citizens Advice Bureau, looking at a range of issues from getting a better understanding of the volunteer life cycle and what pressure points cause volunteers to drop out to improving warning systems for social issues that emerge commonly in communities across the UK (increasing difficulty finding housing, for example, or a rise in consumer debt.) More details on each of the projects are available on the DataKind blog about the event.
On this occasion I wasn’t there in my capacity as a Data Wizard but as a general supporter-helper-outer-type volunteer person. This worked brilliantly as an introduction for a newbie to the format of the Data Dive, which is a full weekend’s worth of high-intensity data science work. First the charities present their needs and ideas for projects, next the volunteer data scientists pick a project to help with during the course of the weekend, then everyone divides into groups and gets stuck in with the datasets the charities have brought with them. There are periodic check-ins to present what each group is working on and how they’re getting on with tackling the available challenges, and at the end everyone gets together to present their findings/charts/diagrams/interactive dashboards etc. There are repositories available on GitHub and Hackpads to capture all the different sub-projects, techniques, and lines of enquiry people are pursuing, which hopefully will facilitate longer-term collaboration on projects.
While the data science volunteers are there for the full weekend, or as much as they can spare, the volunteer shifts are broken into morning, afternoon and evening chunks with the ability to stay on and pitch in on any of the projects when your shift is finished (or when it’s just gotten a bit slow at the check-in desk). That’s a little less daunting than committing to the full stretch, definitely something I appreciated as a first-timer. Plus, I finally got to rekindle all the dormant skills I developed as a longtime summer camp counselor: checking enthusiastic, curious, and sometimes a little bit nervous faces in at registration, jerry-rigging broken catering equipment, distributing snacks and keeping everyone hydrated, directing people to resources (or resources to people), making colorful signs, and of course rocking a totally cool volunteer-staff t-shirt. (Bonus camp-counselor-style tip learned from a Kaitlin-with-a-K this weekend: dry erase markers can get permanent marker off your counter. Or in this case off Mozilla’s counter.)
After watching the format of the day and participating in a few analytic threads, I realized that in my day job I don’t really do that much analysis any more: my main role is helping people articulate their questions and goals then refining tools they can use to get the answers themselves, rather than me providing them with the answer directly. In many ways that is the first step to every problem with data and I definitely think there’s still a role for those kinds of skills to be useful during a Data Dive or in a longer-term relationship with DataKind’s other initiatives, so I’d still recommend going along even if you don’t think you have exactly the right skill set. I would especially love to see more lady geeks at my next Data Dive–I know that lots of research says many women tend to avoid putting themselves forward for roles where they feel their skill set doesn’t exactly match the job description. Well, this is one situation where any skill you can bring to the table will really help these organisations advance their missions, so even if you think you won’t be able to contribute enough, you most definitely WILL be able to offer something that one of the charities will find helpful. Volunteer for the next one and see for yourself.
Among other reasons why I think DataKind is a great place to volunteer: it can be hard to know how to apply data skills in a way that has a positive social impact, and this is a way to connect to analysis that fosters a sense of meaning and of social value. A Data Dive is also a way to expand your own core skills by learning about new tools and techniques that are being used by other data scientists. Seeing what other people are doing with data helps you expand your framework of the kinds of questions data can be used to help answer. And of course it’s always great to hang out with other geeks and unashamedly tell hilarious stories about attribution errors, correlation not equaling causation, and that kind of thing.
Though I wasn’t there for the whole weekend I’ve collated other people’s posts about their experiences in a Storify of the event. If you were there and you want me to add something, get in touch. Otherwise, I hope to see you at a DataKind UK event soon!