Interdisciplinarity Matters: Data Science for Social Impact

Photo credit: Hanson Lu’s new Executive Director, Danil Mikhailov, on his path to, and the opportunity for and complexity of data science for social impact

As I start in my new role of executive director at, I do so with a keen sense of urgency. Our world is grappling with an unprecedented combination of crises: climate change, infectious disease, and economic, social and racial inequalities. These crises demand new tools, new partnerships and new approaches to drive solutions.

Data science is one of the most powerful tools at our disposal. We witness its power in our day-to-day lives: through the media and products recommended to us, or in the fitness sensors in our phones and devices. But today, that power is largely absent or underutilized in the social sector. We do not have the tools, the resources, or the trust required to deploy data science solutions to many of the most pressing problems we face, whether it’s infectious diseases, climate or social justice.

Why are data and data science underutilized in vital fields of social impact like health, environment or social justice? Currently, the field of data science is tipped towards big players that are able to invest in compute resources and acquire the best talent, whether in corporate giants in Silicon Valley or big government actors. Smaller players like community groups, charities, cooperatives, and academics, are increasingly priced out.

Even larger social impact organizations (SIOs) or academic institutions face an uphill battle to compete for the engineers, designers, and data scientists they need deploy data science effectively and at scale. This is something I know well, having led the data science team in the Wellcome Trust, one of the largest philanthropic organizations in the world funding health research and innovation. Even with all of Wellcome’s resources, attracting and retaining tech talent required our constant attention.

My final year at Wellcome taught me valuable lessons about both the power and limitations of data science. The COVID-19 pandemic changed our team’s priorities overnight. Within weeks, I was in charge of coordinating on behalf of Wellcome how to speed up the sharing of data from Covid-related clinical trials, while retaining the trust of the very communities we were trying to help: scientists, doctors, patients, and the public at large.

We found innovative ways to invest in technology, using the urgency of the moment to invite competing data platforms to work together for the first time, sharing their resources and making their data findable, accessible, interoperable, and reusable. In doing so, we coordinated closely with other funders, like the Bill & Melinda Gates Foundation and Mastercard, with whom we jointly launched the Covid Therapeutics Accelerator. Together, we learned how much more can be achieved by pooling resources across organizations when we pursue a shared goal.

However, the biggest lesson we learnt was about the complexity of the problems we faced and still face. As the COVID-19 pandemic spread around the world, existing inequalities in our societies were both exposed and exacerbated. In the context of clinical trials data that included inequalities in ability to access, prepare and analyse it by teams from different parts of the world and with different levels of resources. That’s why, at the same time as driving innovation in data analysis and infrastructure, my team and I worked with social scientists on addressing the cultural issues preventing the sharing of data and ensuring access to it was available to all.

The complexity of the challenges in front of us, whether Covid or climate change, requires diverse partners and perspectives to come together, bridging the gaps between disciplines and sectors. Powerful though data science is, narrow “solutionism”, where we think an app or an algorithm on their own is the answer to everything, is rarely effective. When the chance to lead presented itself, I saw an opportunity to scale all I have done in a twenty-year career as a technologist, data scientist, social scientist, and entrepreneur. The job’s inherent interdisciplinarity is what attracted me the most.

With we are building a neutral platform, a digital and community space not confined to the lens of a single topic. We recognize the power of partnerships, and seek to bring together SIOs looking for help in deploying data science for social impact with those organizations who have the resources to provide such assistance, via funding, or tools, or data science and engineers. We are open to working with everyone: Big Tech, small community groups, top academic teams, and philanthropic organizations.

We believe that data science approaches need to be reforged and democratized with ethics and social impact at the center to avoid exacerbating the very inequalities we seek to reduce. Acting as a convener and a catalyst, we at aim to empower the sector with the tools, talent, resources, and increased trust we know is required. As a team, and with our founding partners, we are ready to move with expertise, energy, and humility. I personally look forward to engaging with the many committed individuals and organizations building the field of data science for social impact, and addressing the urgent and interrelated problems we face.

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