The abrupt cuts to foreign aid in early 2025 were a shock to the system, forcing us to reimagine development data. Key data systems were immediately impacted: the world’s leading warning system for food insecurity, the Famine Early Warning Systems Network (FEWS NET), went down and funding for the foundational Demographic and Household Survey (DHS) was cut. PEPFAR logistics systems—a partnership between the President’s Emergency Plan for AIDS Relief (PEPFAR) and leading private sector supply chain innovators that treat thousands in more than 50 countries—were barely functioning.
How do you begin to triage a situation such as this?
While some of these systems have (slowly) started coming back to life with stopgap funding from foundations and, in some cases like FEWS NET, the return of partial funding from the U.S. government, it’s hard to know where to start with so many data systems affected.
With the benefit of a chance to reflect, we decided to start with a simple first step: let’s list the data systems that seem important for development.
Keeping this in mind, our team at AidData’s Reimagining Development Data (RD²) project began by compiling short lists of data systems in eight priority sectors: governance, health, education, agriculture and food security, conservation and environment, and disaster and humanitarian assistance, as well as cross-sectoral development data and gender equity assets.
But a list is only a good first step. What we really want to know is which data systems are most important to those who rely on this data for their ongoing work in international development. Our first solution was to look through documentation of the types of data that countries and international organizations use for planning and monitoring programs. Reviewing country strategy documents gave us a general idea of the data systems countries find important, while metadata on the Sustainable Development Goals (SDGs) helped us discover the data sources that are key for monitoring country progress on SDGs at the international level.
With SDG metadata and 50 national strategy documents across 44 countries in hand, the RD² team developed a public inventory of 496 data assets. More than just datasets, these data assets also incorporate data tools and repositories.
What did we learn when creating the inventory?
While this Data Asset Inventory is meant as a starting point for further analysis of the funding for data assets and factors that influence their resilience, the process of collecting this information has already helped us to surface some initial findings.
One unexpected insight was the applications for development data assets in the private sector. Eighty-seven of these data assets had documented use cases by private companies, with many related to trade and investment. While we expected some private sector use, we did not expect it to be this clearly documented. This degree of use by the private sector suggests one possible avenue for sustainability: the sale of derivative data and analysis to private companies. For development programs, however, it is important that the core (non-derived) data remains a public good, so that data is freely available for program monitoring, evaluation, and planning, as well as public accountability.
Another finding was the role of universities and multilaterals in maintaining large data assets—a contrast to the perception that data programs are (or were) mostly run by private companies or NGOs. Examples of university-run data programs are the Global Roads Open Access Data Set (Columbia University’s CIESIN), V-Dem (University of Gothenburg), and the DHIS2 project (HISP Centre at the University of Oslo). Among multilaterals, the World Bank and other UN agencies play important roles in collecting and curating large amounts of data. UNICEF manages the Multiple Cluster Indicator Survey program (MICS) which covers many of the same indicators as DHS. These organizations, whether through university investment and cost sharing or multi-stakeholder investment in multilaterals, offer examples of pathways to data resilience that private sector data production programs clearly lack.
What did we learn from reviewers of the inventory?
Since earlier this summer, when we published the preliminary Data Asset Inventory, we have been actively seeking feedback from those in all parts of the development data ecosystem. This feedback and input has been invaluable, and we’d like to share some of that discussion here.
One commenter asked about the provenance of the data. Knowing the origins of the data and how it has been modified is central to understanding its quality. Users will have greater trust in data when the data source and methods of modifying the data are well known. When data collection methods are documented and data is modified through standard techniques, such as the case of the DHS, assessing data quality can be relatively straightforward.
But as methods evolve that rely on sources where the accuracy of the information is less known, more effort is needed to triangulate datasets with underlying sourcing and methodologies to ensure quality. AI complicates matters further, as AI-provided data sources and methods are not always transparent. One way of partially getting at the origins of data is to distinguish between primary and secondary data, which we will do in the upcoming revision of the inventory.
Another commenter focused on the feasibility of analyzing such a large number of data assets. Our count of 496 data assets is overwhelming. How can we reduce this to a reasonable level? One way is to focus on how funding flows. Often financing supports an entire program, not just individual data assets. In the case of DHS, funding generally flows to the DHS program, not the six data assets it produces. By focusing on the financing, we can greatly reduce the number of data systems to be analyzed.
The critical question for now is: how will we assess the impact of the aid cuts on data? Our next step is to refine the Data Asset Inventory based on a careful review of all the helpful comments. From there, we will analyze funding trends related to data asset families and conduct a global survey to see what data leaders really need. We will ask development data users and producers about the impact of aid cuts on the data ecosystem in their countries; their perceptions of the health of the data ecosystem; priorities for data to collect in their country; and how to strengthen the data ecosystem.
Our research will also incorporate key informant interviews to consider in-depth questions on topics such as data production capacity. Ultimately, we will synthesize the survey findings and the insights from our key informant interviews into a series of reports on the funding for development data and the resilience of data systems.
How can you help us to reimagine development data?
If you have not already done so, please browse the Data Asset Inventory and give us your feedback before September 15, 2026. We also have active conversations with individuals and organizations doing similar work. If you would like to be part of those conversations, feel free to contact us.
