By Jake Porway, Founder & Executive Director
Like many of you, I am thinking a lot about all of the changes facing our world. As I look outside to the changing nature of this pandemic, of our political systems, and of our climate, I also feel a pull to look at changes inside at DataKind. In particular, I have been thinking a lot about DataKind’s role in this new future as well as my own role in it, and would like to share my thinking with you all.
It feels appropriate that I am writing this post almost exactly nine years since the first DataKind DataDive, back in October of 2011. In response to a blog post I’d written imploring data scientists to come together to use our skills for more than just helping Netflix make a better movie recommendation algorithm, data scientists from all over New York City had come together to volunteer for a weekend of working with nonprofit organizations. At first we had no idea if any data scientists would show up, or if nonprofits would be ready for data science, or if having both in the room would amount to much. But that night—watching would-be data science celebrities like Cathy O’Neil analyze stop and frisk data for the New York Civil Liberties Union, seeing data engineers from Facebook and Spotify show visualizations of survey responses with the UN Global Pulse, and having teams of thoughtful statisticians from OECD to OKCupid showcase their scrapers that would expand access to data from 30+ small banks in Africa with MIX market microfinance—we knew we were on to something big. My co-founder Craig Barowsky and I quit our jobs, incorporated DataKind, and set to work carrying the excitement from that first event through into a full-fledged organization.
So much has changed in the near-decade since that first event. Our first DataDive of 50 people grew into a global network of over 20,000 data scientists and AI engineers committed to using data science for the greater good. A mere three weekend projects grew into over 300 projects, ranging from saving $25M for local water districts by better predicting water demand in drought-stricken regions, to helping community health workers digitize health records almost 2000x faster than before with computer vision. Our one DataDive spread to many DataDives worldwide, which then deepened into six–nine month long DataCorps projects, which became deeper still as multiyear, issue-area specific DK Labs and Impact Practices. Our volunteer efforts spread from a room in NYC to other US cities, to the UK, Bangalore, and Singapore, each creating communities of volunteers who work to use data science and AI for social impact in their own regions. What started as a few visualizations eventually became software solutions like route optimization software for SOIL Haiti, which have been replicated by other NGOs around the world. On top of all the project wins, we’ve seen the social sector become more adept at understanding and using data science, making steps toward a world where DataKind wouldn’t be needed, which is exactly the point of this mission.
Yes, a lot has changed since that first event, but a lot has also stayed the same, unfortunately. Data science and AI are still largely wielded by big companies and governments who use it to accelerate their own goals, often to the detriment of society. The data that we thought we could use to better shape our understanding of our world is instead weaponized to distort reality to the point we can no longer have healthy discourse. And for all of the great gains made in using data science for social impact, the social sector still struggles with a lack of tech capacity, fractured and scarce funding resources, inability to match problems to solutions, and a slew of other points of friction that make balancing the scales feel too heavy to deal with.
Looking back on the almost decade of our work, I find myself at a crossroads. On the one hand, DataKind has demonstrated that the social sector can benefit from data science and is now poised to tackle the deeper challenges that could lead to even larger scale use of data science for social impact. On the other hand, there is still a need for a vision of constructive AI that unites a fractured space, and a lot of work to be done in strengthening the data science and AI skills of the social sector writ large. I see an opportunity to use the experience I’ve gained in building DataKind to help the broader data for good field, and that is why I will step down as Executive Director of DataKind in Spring 2021. I have been absolutely honored and humbled to serve as DataKind’s Founder and Executive Director through its formative years and am proud that we now have a strong executive team, a full staff, and funding to take the organization even further in providing data science innovation to the social sector. As I look out at the broader frontier of data science and AI for good, I see an opportunity to bring more institutions and people together to tackle large scale problems with this technology, I see chances to advance data science strategy across the social sector, and I see ways I can help support the future leaders of the data for good movement.
So what’s next? We’ll be starting a search for a new Executive Director in the next few weeks, so if you or anyone you know has a passion for leading a global network of data scientists for the greater of good, do apply when the posting is up (until then, we welcome any suggestions or inquiries to our Chief of Staff, Sasha Ahuja). We’ll also be continuing our work here at DataKind on our latest strategy, Impact Practices, where we are working to build cross-sector solutions above and beyond our projects of the past. As for me, I will continue to support the transition in the most helpful ways possible and am in conversation about a number of fellowships with our close friends and supporters that would allow me to continue to advance the field of data science for social impact in 2021.
I will save my goodbyes for the day they’re needed, but I will close by saying how incredible the DataKind community is and what an incredible experience it has given me. I could brag on DataKind’s stats all day, but more than any individual project achievement, I have been truly inspired by the stories of the DataKind community coming together to use data science for human prosperity. No statistic can capture the tears in the eyes of leaders in the social sector I saw as they learned about their constituents through their data. No statistic can capture the countless data scientists who have written to us to say they have been changed by their work with charities and are now pursuing a career in the social sector. No statistic captures the innumerable times I and other DataKinders have been stopped in our travels because we have a DataKind sticker on our laptops, leading to a meeting with a DataKind-er from another country, oftentimes in places we didn’t even realize people had DataKind communities. No matter what happens next, these memories are the ones that will stay with me most, and the ones that I will devote my energies to creating more of in the next chapter.
I am excited for this next phase of DataKind’s journey and this next phase of my own personal journey. I am grateful every day that I get to serve this mission, and I look forward to what shape that takes in the years to come.
Until then, back to work! See you all on the frontlines.