The Power of a Decade: DataKind Celebrates 10 Years of Data Science for Good

Ahead of the event, Woodman shared exclusive insights with The Rockefeller Foundation on what has been key for DataKind’s longevity, how the approach to their work has changed, and what lies ahead.

RF: How has DataKind’s work, or approach changed since its inception?

Lauren Woodman: Our very first DataDive event spread to many DataDive events worldwide, which then expanded to six to nine month-long DataCorps^® projects, which then deepened to multiyear, issue area-specific work. Our volunteer efforts spread from a room in NYC to other U.S. cities, to the UK, Bengaluru, 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 solutions like route optimization software, which has been replicated by other NGOs around the world.

We’ve moved beyond our project-to-project work, evolving our long-term projects, and building thematic portfolios. The demand for our services is more than we can tackle without partners who can offer support with critical resources. With thanks to The Rockefeller Foundation and the Mastercard Center for Inclusive Growth for their generous support, we’ve further expanded our efforts in providing pathways for replicability and scalability across various fields and topics.

RF: What has been the biggest obstacle DataKind has overcome?

LW: Most leaders in the social sector already recognize the value of data science and AI. The biggest hurdle to adoption is the question of data capacity and data maturity. I don’t mean that in terms of time and effort, but as the capacity for the social sector to understand, absorb, and effectively utilize the tools that are currently available – and to do so at scale. Without having the technical skills on staff, it’s difficult to execute a new data science strategy, which is where partnerships with organizations like DataKind come in. This is true for donors as well. They recognize the need to use data better but don’t quite know how to fund it effectively.

RF: How has DataKind adapted to the changes in the field in the last 10 years?

LW: It’s become almost a cliché to talk about the importance of data in the social sector. Yet, as I mentioned above, most mission-driven organizations lack the expertise and resources to take full advantage of data science and AI. The barriers to implementing data science in nonprofits are not insignificant, but we have an opportunity to define the landscape and ensure it’s working for the benefit of the social sector at the same rate as the private sector. It’s up to us to illustrate how we get from point A to point B. And as DataKind helps to accelerate in areas that need critical attention, we’re uniquely positioned to support mission-driven organizations on this journey.

This year, we’re celebrating 10 years of successfully leveraging Data for Good—an idea that seemed novel a decade ago when data science was barely on the radar. Now, as conversations about data science and AI have entered into the public discourse, I’m looking forward to helping us grow our impact for the next 10 years and beyond.

RF: What is something that DataKind has learned you wouldn’t have expected given the original mission and vision?

LW: The recent crises have had an uneven impact on the relationship between citizens and institutions, highlighting the need to address disparities that can’t be ignored. Specifically, the pandemic laid bare the need to close the digital divide to ensure that every community has access to healthcare, education, and trusted information. We see daily the immediate and cumulative impact these gaps have. At DataKind, we’re committed to replicating and scaling our work and strengthening our position as the go-to technical partner to the social sector.

RF: Where is DataKind heading in the next 10 years?

LW: As we emerge from the pandemic, we must come back stronger, invigorated, and better equipped for the future. We must ensure that solutions are designed with, and not just for, the intended community. We must examine how organizations adapted and share learnings for broader resiliency. To the extent that technology helped us shift delivery models, we must evaluate whether those models are scalable, equitable, and ethical. And we must recognize that effective partnerships demand expertise and engagement from all of us, working together for the communities and citizens we represent and serve.

I’m looking forward to finding the most effective ways to direct affordable technical capacity into the social sector and partnering with more companies, foundations, and individuals to build a world where all those who fight on the frontlines of social change can use data science and AI to accelerate the pace of their impact.

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