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

Tasnuva Orchi — Former Senior Data Analyst, Innovation, The Rockefeller Foundation

In 2012, Jake Porway had an idea.

Coming from the tech world, he was curious about taking the skills he and his fellow data technicians were using every day and putting them towards social good. So, he wrote a quick email sharing his vision and asking if anyone would like to join him in harnessing the power of data science in the service of humanity.

He posted to a long dormant personal blog, emailed it out to his network, and signed off for the weekend. By Monday, the post had gone viral. By the end of the week, over 3,000 people had responded. The post had even been reshared by The White House.

The popularity of that first DataDive® event – and the many that followed – spurred Porway to quit his full-time job and co-founders Craig Barowsky and Drew Conway, start DataKind.

Big data in the service of humanity. (Video courtesy of Jake Porway at TEDxMontreal)

Data Science for Social Good

What Porway acted upon was the gap felt between data and social good. While data scientists have the potential to contribute to incredible social impact, they’re an expensive asset.

“Not every nonprofit has the machinery that a for-profit organization might have,” explains The Rockefeller Foundation’s Senior Vice President of Innovation Zia Khan. “They’re dealing with a lot of politically complex, values-driven issues, and the ability of really talented data science experts to work in collaboration with organizations that don’t have the data maturity is really powerful.”

Since 2019, The Rockefeller Foundation and Mastercard’s Center for Inclusive Growth have contributed numerous grants towards DataKind’s growth.

Moving From Singular to Scalable

One of DataKind’s greatest successes over the past decade is advancing the idea that “something we’ve created for a specific organization’s project can be used globally,” says Cox, which has allowed DataKind’s work to grow exponentially while still remaining flexible.

DataKind UK volunteers come together to dive into data science and AI for good projects. (Photo courtesy of DataKind)

Image slideshow of projects worked on by DataKind.

When Covid-19 hit, DataKind needed to quickly change how it operated. In-person, weekend-long data events DataKind prided itself on before suddenly weren’t possible. While still exploring the long-term implications of moving away from their initial engagement models, DataKind began embracing the benefits virtual engagement brought. Suddenly, they could work with volunteers based outside of their Chapter locations, bringing in more diverse perspectives that only benefited their work.

These projects have included optimizing access to healthcare in Africa, advancing racial wealth equity in the U.S., sending more kids to college, mitigating home fire deaths, and more. Their work has had an impact throughout numerous locales, from Michigan to Florida, Haiti to Uganda, and across the globe.

“I think it’s just incredible to see what’s happened over the last 10 years,” Porway shared at a recent DataKind event. “But it’s even more exciting to think about what’s going to happen over the next ten.”

Looking Ahead with DataKind’s CEO

In November, DataKind celebrated their 10-year anniversary with a virtual event featuring DataKind leaders both past and present. Talking with funders and volunteers tuning in from around the world, DataKind’s CEO Lauren Woodman shared the organization’s forward focus will be on environmental justice, benefits access, and food security.

Their hope is to become the go-to collaborator between social impact organizations and data scientists.

DataKind Co-Founder Jake Porway rally data science volunteers for a high-energy, marathon-style DataDive event. (Photo courtesy of DataKind)

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 its 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.

Curious about DataKind’s work? Here are a few ways to get involved: