Ideas & Insights / All Perspectives / Ideas & Insights Our Renewed Commitment to Building the Field of Data Science for Social Impact

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For over a hundred years, data has helped The Rockefeller Foundation diagnose root causes to problems and surface solutions that improve lives. Data drove the development of Public Health, Social Security, the Green Revolution, and Impact Investing.

Today, advances in data gathering, management, and analysis have led to a data science revolution. The private sector is doing well in capturing value from this revolution, with Alphabet recently joining Apple, Amazon, and Microsoft as companies valued at $1 trillion. However, many technology companies are coming under much greater scrutiny for how they use data and share (or not) the benefits back to society. At the same time, organizations dedicated to serving the public and creating social impact lag in their awareness, capacity, and resources to fully capture the value of technology.

Fortunately, the MasterCard Center for Inclusive Growth shares our vision to mobilize data for good. Last year we launched the Data Science for Social Impact Collaborative. This $50-million investment over five years was intended to build the field of data science through a transformational model of collaborative philanthropy – one designed to ensure data science generates wealth and well-being for all rather than only the few. We invested in organizations like DataKind, Benefits Data Trust, and Community Solutions to help them scale their impact through data science.

Over the past year, The Rockefeller Foundation has worked to apply data science to electricity demand prediction, supported the development of an impact practice model that will use machine learning and AI in the service of Community Health Workers, and improved the health and economic mobility for at least 5 million low-income people in the United States by streamlining access to public benefits. We are very encouraged by the results, but we need to move faster and go further. To do so requires partners.

Today, we announced with Mastercard that we will continue to move the future of data science for social impact forward under the banner of The original effort—launched in 2002 in partnership with millions of activists around the world—contributed to the cancellation of $100-billion of debt owed by poor countries and promoted to issuance of $50-billion in aid and trade deals that helped create pathways to greater economic prosperity, strengthening transparency and accountability in public and private financial flows. In what ultimately became the ONE Campaign, they focused on driving evidence-based approaches to development. will focus on building capacity, providing training, and sharing publicly available data to maximize the potential for data-driven insights.

With the support of the original founders, we are announcing the relaunch of as a platform for partners and partnerships to join us in this movement to build the field of data science for social impact. This new entity will focus on building capacity, providing training, and sharing publicly available data to maximize the potential for data-driven insights. As part of this effort, we will launch a new $10 million impact challenge to crowdsource scalable and sustainable data science solutions for non-profit, civic and government organizations.

The founders of the original realized the need for numbers and evidence to drive social change. Now we are in a similar moment—where data is ubiquitous and the data revolution touches all of our lives—but a bold effort is needed to bridge the gap and ensure the most vulnerable are not left behind. Which is why from our perspective, the need for partnership could not be clearer.  Now more than ever, scaling the highest, hardest walls in global development requires philanthropists to stand on each other’s shoulders.  Only by working together will we solve the world’s toughest challenges.

Together we can create a world where data transforms the lives of those in need around the world—join us at

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