We’re thrilled to welcome Lauren Woodman as the new CEO of DataKind. She brings to the role over 25 years of experience working at the intersection of technology, development, policy, and NGOs, and we know we’ll do great things together! We sat down with the tech-for-good veteran to discuss DataKind’s role in advancing data science and AI in the social sector, furthering data ethics, and more.
Before joining DataKind, you served as CEO of NetHope, a global consortium of more than 60 nonprofits specializing in helping development, humanitarian, and conservation organizations use technology more effectively. Why was it important to channel over $100 million in technology advancements to nonprofits?
At NetHope, our focus was on catalyzing nonprofits to work collaboratively with software companies to amplify their reach and impact. To do this, we had to convince both the nonprofits and the donors that technology was no longer a “nice to have” but a fundamental resource needed for organizations to deliver optimal results. For nonprofits, it’s crucial they aspire to become digital organizations. The private sector understands the transformative impact that tech has, and many are eager to see similar progress in the social sector. In partnership, we can identify the tools and resources that nonprofits need.
Funding technology has the potential to generate exponential impact. While some donors prefer to designate their dollars to be used for something tangible – such as first-aid supplies – funding technology improvements helps mission-driven organizations accelerate the work they do. Data and AI are the next frontier. While many organizations have solid infrastructure, there is tremendous opportunity for donors to increase impact with the greater insight and efficacy that investments in data science can offer.
What do you think are the biggest challenges for organizations like DataKind to demonstrate the value of data science, AI, and other advanced technologies in the social sector?
Interestingly, most leaders in the social sector already recognize the value of data science. The biggest hurdle to adoption is the question of capacity. 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 ours 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.
What excites you most about your new role as CEO of DataKind?
I’m super excited about bridging the gap between hope and reality. It’s become almost a cliché to talk about the importance of data in the social sector. Yet, most mission-driven organizations lack the expertise and resources to take full advantage of data science, machine learning, and AI. DataKind is the technical partner to the social sector and helps to accelerate in areas that need critical attention. The barriers to implementing data science in nonprofits are not insignificant, but we have an opportunity to define the landscape and ensure AI is 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.
DataKind is uniquely positioned to support mission-driven organizations on this journey. For a decade, we’ve been working with nonprofit partners, and understand the unique challenges and pressures they face when adopting new approaches or technologies. We’ve worked with tens of thousands of volunteers and learned how best to channel their expertise and knowledge into action. Furthermore, we source the necessary funding from the right donors to ensure sufficient support is available for quality work and sustainable impact. Our challenge now is to expand and extend our learnings so that the entire sector can evolve and transform.
Data ethics has been top-of-mind lately for us and many others in the data science for good community. What role do you believe DataKind plays in this conversation?
Defining what’s ethical in the space is key to effectively leveraging advances in data science and AI within the social sector. Nonprofits are inherently conservative—working on difficult issues, human issues, with donated money is not a recipe for risk taking. While data is a critical piece to administering benefits, if it can’t be done in an ethical way, nonprofits are limited in what they can achieve. That’s not fair to those organizations or the sections of society that rely on them to provide services. It’s a broad issue and one we must address in a way that includes and considers the people who are impacted.
The public narrative around data tends to be negative because of missteps in the private sector when it comes to consumer data. We cannot let those missteps dissuade us from doing the right thing in the social sector. DataKind is a leader in the space, offering a voice for data science for good and shining a light on the opportunity of data science to improve lives.
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