We started by trying to eliminate a disease—and ended by building a data culture. Here’s how.

“How would you like to help us eliminate malaria in Zambia?” I’ll never forget when Diana Pallais asked me that question almost seven years ago. We were at Milstead, the ground floor cafe of the Tableau office building that functions as our coolest conference room. At the time, Diana was the Vice President and Managing Director of International Development at PATH—an international health NGO. That one question launched one of Tableau Foundation’s most defining partnerships and helped establish how we think about our philanthropy today.

If you’ve followed our work at Tableau Foundation, you probably know that I’m talking about Visualize No Malaria: A multi-year, data-driven effort that has dramatically reduced malaria cases across Zambia and other countries. While COVID-19 created challenges for hitting the audacious target of eliminating malaria in Zambia by this year, we still believe an end to malaria is in sight. 

The partnership was an experiment for us and a pretty radical one: We made an unrestricted, multi-year grant (which was relatively uncommon at the time) to support nothing more than a vision that data could help and a partnership to figure out how. I am excited that it succeeded, not just for PATH and the countries they partner with, but also for us.

The approach of publicly setting a clear goal, funding the effort with long-term, flexible funding, and developing a deep, collaborative partnership worked. And it has influenced our philanthropy for more than seven years. Since the launch of that campaign in October 2014, we’ve initiated other audacious partnerships: With Community Solutions, we’ve committed to bringing about an end to homelessness in 75 US cities by 2024; with Splash, we want to help bring clean water and sanitation to 1 million children by 2023. These are the explicit goals—the ones with numbers attached to them to track measurable progress.

But what I’ve realized, reflecting on this type of funding that began with Visualize No Malaria, was that our deeper goal underneath the numbers was to create a common language using data for understanding and tackling these massive issues—from malaria to homelessness. 

We believe that the effort to eliminate malaria in countries like Zambia and Senegal has been so successful because it’s been, at its core, an effort to build a culture of data around health. During the pandemic, we realized that countries that invested in data infrastructure for tracking public health issues like malaria could use the infrastructure to track the spread of COVID-19. And in doing so, they created a compelling case for countries having a comprehensive, data-driven system for responding to any health crises in the future. And now, we see the same focus on data for shared understanding and action begin to transform everything from U.S. cities’ approach to homelessness to advocacy for sustainable recovery from COVID-19 across the Global South to economic empowerment efforts in the U.S. south.   

It’s an honor to be a part of catalyzing the data shift that’s happening in cities and countries across the world. We have learned a lot, so I want to share some of the observations I’ve collected along the way—beginning with Visualize No Malaria—that have helped these data cultures take root and grow.  

1. Start with the data you have, and build iteratively from there. No one has perfect data. Nearly every new partnership starts with a nonprofit telling us that their data is messy and incomplete. We respond by saying that they are not alone! And the only way to get to better data is to begin somewhere.
2. Create data visualizations and other resources in collaboration with communities and people who will use the tools—not just by data experts. Initially, when we launched Visualize No Malaria, we connected malaria experts at PATH with Tableau visualization experts to build the pilot dashboards for the initiative. But after workshopping the dashboards with the Zambian Ministry of Health workers, they ended up entirely different. The way the Tableau and malaria experts saw data informing the world was not how the on-the-ground practitioners saw it—and theirs was the perspective that the dashboard needed to reflect. 
3. Broaden the definition of what it means to be a data person. Visualize No Malaria was a success because everyone across the health system in Zambia had a role to play and was bought in. From the community health worker inputting weekly data on malaria cases to the central Ministry of Health analyst compiling the data and updating the dashboards, everyone together contributed to a data system that ultimately strengthened the whole country’s response to malaria. However, through this partnership, we realized that we need a much more expansive definition of “data person.” For example, not every person contributing to the success of Visualize No Malaria knew what an R-value was, nor could they build a dashboard in Tableau. As a result, our grantmaking now focuses on reaching a wide range of data people, from those who build vizzes to those who use them to advocate for change.
4. There’s immense value in creating shared language around data for progress. As I said above, our partnership with PATH has been influential because we set a bold goal and are now seeing it within reach. We’ve also learned that an effective data partnership doesn’t have to track toward a single endpoint. Partners like PolicyLink, a racial equity and justice organization in the US, and REDIM, a Mexican organization that advocates for childrens’ rights, use data powerfully in their communications and advocacy work across several goals - not just one. They teach us that data can serve as a shared language when data are in a clear, visual format. Their dashboards illuminate disparities and call for change while also building the most basic data capacity: understanding. For data to play a role in creating progress against some of the biggest challenges of our time, that broad understanding and a shared language are essential.

We’re now applying all of the lessons learned from partnerships with PATH, Community Solutions to new work, including our Racial Justice Data Initiative and Racial Equity Data Hub. These latest efforts are focused on engaging communities even more deeply in the process. We hope that anyone can use data to advocate for change—even if they don’t know what an R-value or trend line is.