Objectives In the U. S. , funding and staffing structures for data sharing and integration vary widely across state and local governments. We set out to deepen our understanding of how different integrated data systems (IDS) are funded, staffed, and how they developed over time to provide practical guidance for practitioner, policymaker, and academic audiences. MethodsSince 2008, Actionable Intelligence for Social Policy (AISP) has supported jurisdictions through the process of developing shared data infrastructure, offering guidance, technical assistance, and training opportunities. We regularly interact with and convene a network of 46+ sites across the U. S. , and since 2011, we have conducted a biannual network survey. The 2023 survey results revealed wide variation and inconclusive findings regarding resources required to develop and operate state and local data systems. In 2024, we conducted qualitative interviews with 30 sites to better understand their survey responses and inform guidance for the field regarding budgets, funding, staffing, and development. Results Interviews revealed three broad categories of IDS. Small efforts, with budgets between 300, 000-500, 000, tend to serve smaller populations, focused on a specific domain, with 2-3 full time staff members. Medium efforts, which have budgets between 500, 000-1, 000, 000, tend to be county or state efforts based at universities, with 3-6 full time staff, as well as student and faculty affiliates. Large efforts, which have budgets over a million dollars, tend to be county or state efforts operated within government, and have up to 30-40 full time staff members. Funding comes from a range of sources—federal, local, state, philanthropic, and fee for service. Sites spend the vast majority of their budgets on staffing and very little, comparatively, on technology and infrastructure. ConclusionRegardless of budget and funding mechanisms, sites spend more money on personnel than any other expense. It costs around 350, 000 for the data system itself, including tools for storage and linkage. The major distinction between sites lies in their capacity to afford personnel to do research and design data products internally.
Nelson et al. (Thu,) studied this question.