The Rockefeller Foundation deeply believes in the potential for data science to improve people’s well-being and we’re excited to incorporate it into how we work as a foundation and how we realize impact in the world. It’s why we recently launched the Data Science for Social Impact Collaborative with the MasterCard Center for Inclusive Growth.
Personally, I’ve always been fascinated by integration. When I was in grad school, we integrated experiments with numerical simulations to better understand fluid dynamics. When I worked as a strategy consultant, we integrated the formal and informal aspects of organizations to improve our clients’ performance. I’m now fortunate to work at The Rockefeller Foundation, which has long used integration to build new fields, ranging from molecular biology’s integration of analytical techniques and life sciences to impact investing’s integration of approaches that generate financial and social returns.
Many years ago, Mary Parker Follett described integration as overcoming the tensions between different interests to realize a better solution, but without resorting to compromise. It takes persistent and often frustrating work to overcome these tensions and realize the benefits. Shortcuts are rare, which can surprise people who believe that repeating the word “integration” enough times will make it happen. This is particularly true when it comes to data science and social impact. We can’t just add a dash of AI to global health, workforce development, or environmental disasters in a hackathon and expect meaningful, long-term change. It’ll take true integration between data science and different fields to fully realize data science’s potential.
We need to apply enough energy to join data science with fields like global health and workforce development until we overcome the differences that keep them apart.
Lately I’ve been describing what I mean by integration with a different word: fusion. Consider this physics analogy: Achieving nuclear fusion requires enough energy to be applied in a confined space until atomic nuclei overcome the forces keeping them apart and those nuclei fuse together, releasing extraordinary amounts of energy. Similarly, we need to apply enough energy to join data science with fields like global health and workforce development until we overcome the differences that keep them apart—goals, techniques, and culture—so a common vision of breakthrough impact can emerge, releasing extraordinary collaboration and innovation.
We’re inspired by our partners achieving this kind of integration:
- Atlas AI – a startup fusing AI with economics and agricultural science to produce new insights for decision-makers;
- DataKind – a nonprofit fusing impact-oriented data scientists with social change organizations to achieve more impact; and
- WeRobotics – a network fusing drone and AI-related technologies with local communities to build new capacities to address a range of problems
Fortunately, leaders of these kinds of organizations often reflect on their approaches and best practices, allowing us to learn from them and improve our own methods. David Lobell of Atlas AI, Jake Porway of DataKind, and Sonja Betschart of WeRobotics have recently written about how they achieve social impact using data science. There are common themes familiar to the tech world, but with nuances relevant to social challenges. Assemble great teams, but approach problems with humility. Use the world’s best technology, but draw on local knowledge and community leaders to maximize impact. Be open to multiple possibilities for the future, but design upfront for ethics and values. These and many other lessons will help us achieve the right kind of integration.
In a few weeks, we’ll pilot an exciting new style of workshop to accelerate the fusion of data science and different fields. If you’re at SXSW next week, stop by The Rockefeller Foundation’s Global Innovation Zone to learn more. And stay tuned!
Check out the other pieces below on our #data4good series leading up to SXSW 2019.
How Much Longer Can We Continue to Overlook the “Power of Local”?
How Do We Ensure “Data for Good” Means Data for All? Consider These Three Principles
The Gender Gap in Innovation and How to Break the Digital Ceiling