A Landscape Overview of Integrated Data Systems Funding and Staffing Models Across the U.S.

Introduction: Funding and staffing structures for data sharing and integration vary widely across the U.S., with few recommendations for optimal resource allocation. Objectives: We sought to deepen our understanding of how Integrated Data Systems (IDS) are funded and staffed over time. This work examines how IDS have developed to provide practical guidance for practitioners, policymakers, and academic audiences interested in establishing and expanding integrated data efforts. Methods: Since 2008, Actionable Intelligence for Social Policy (AISP) has supported jurisdictions through the process of developing integrated data systems, offering guidance, technical assistance, and training opportunities. We regularly interact with and convene a network of 40+ sites across the U.S. and conduct a biennial network survey. This paper provides insights from survey results (2023, 2025) and qualitative interviews conducted with 24 sites (2024). Results: Interviews revealed three broad categories of IDS. Small efforts, with budgets between $250,000 - $500,000, tend to serve smaller populations, focus 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 level, often based at universities, with 3-6 full-time staff. Large efforts, which have budgets over a million dollars, tend to be county or state level, operate within government, and have up to 20-40 full-time staff members. Funding comes from a range of sources-federal, local, state, philanthropic, and fee-for-service. Across all categories, sites spend the majority of their budgets on staffing and little, comparatively, on technology and infrastructure. Conclusion: Key themes persist across the unique landscape of IDS efforts. Regardless of budget and funding mechanisms, sites spend more money on personnel than any other expense. Successful efforts tend to focus initial investments on partnership development and data governance, rather than technical infrastructure, and diversify funding sources over time.