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What Does the Research Say?

Helping families access high-quality early childhood options requires knowing where children are, what services they’re receiving, and whether those services are working—yet in many places, that information is difficult to access. Compared to K-12, early childhood programs are funded and regulated through a of overlapping federal, state, and local programs, making data more challenging to link.—including dashboards, integrated data systems, and longitudinal systems—can help policymakers use limited resources more strategically, give advocates local evidence, and give communities a clearer picture of strengths and gaps.

Kansas’s Early Childhood Data Trust

Put governance first. Previous efforts to link data across agencies had stalled because the structure to hold them together wasn’t in place. This time, before building anything technical, leaders created a legal agreement that established conditions for data sharing, a governance board, and a process for carrying out authorized projects. Getting five agencies to sign took a year and a half. But as one leader reflected, “It was going to be the thing that was going to hold this together, no matter what administration might come next.”¹ The agreement also had to navigate legislative concerns about government tracking of children—a political undercurrent that shaped what was possible.

Combined political savvy with technical expertise. The partnership between CPPR’s Teri Garstka, who led the Data Trust design from 2018 to 2023, and Melissa Rooker, executive director of the Children’s Cabinet, was central. Garstka brought deep technical and systems knowledge; Rooker brought legislative experience and the authority to lead state agency directors toward making data a shared priority. As Garstka put it: “It wouldn’t have happened without the both of us.”²

Invested in soft infrastructure. Garstka developed the concept of “soft infrastructure”—the human capacity that bridges the technical and relational dimensions of systems. This means having people who can understand data and technology yet also navigate agency dynamics, build trust, and translate findings across audiences. Soft infrastructure, she said, is “the grease that makes the wheels run.”³

Started with a high-value proof of concept. Rather than build everything at once, leaders began with a high-value question: Does participation in early childhood services reduce child welfare involvement? The results were striking—children who received early childhood services were 23 percent less likely to be removed from their homes. “It was Kansas data,” Garstka reflected, “so that mattered to people.”⁴

Planned for the long game. Garstka described the work in three phases: the sprint—running hard when a window opens; the marathon—sustaining the work over time; and the relay—ensuring knowledge and momentum can be passed on. “If you are planning for all of those phases—the sprint, the marathon, and the relay—that should infuse all of your design work, because you never know what disruptions are going to happen.”⁵

Chicago’s Early Childhood Integrated Data System

In Chicago, early childhood programs operated across Chicago Public Schools, the Department of Family and Support Services (DFSS), Head Start grantees, and hundreds of community-based providers with no clear, shared view of the children they collectively served. Launched in 2022 after several years of planning, the Chicago Early Childhood Integrated Data System (CECIDS) merged data across agencies to answer 52 use cases identified by an advisory group—from operational questions about enrollment and capacity to longitudinal research on children’s pathways. Northern Illinois University (NIU) served as technical trustee, holding individual data sharing agreements with each contributing agency and producing dashboards and ad hoc analyses. CECIDS is now winding down due to funding constraints; the work ahead includes documenting and transferring lessons about governance, trust-building, and financial sustainability.

How Leaders Made It Happen

Understood that early childhood data is not K-12 data. Building an ECIDS requires expertise in early childhood’s unique landscape: blended and braided funding, multiple agencies, community-based providers, transient families, and data systems never designed to work together. As one leader said: “You need to have conversations with your data providers to understand how those data are created, how they’re stored, how you’re ingesting them—and then how you can process it.”⁶

Maintained data integrity as the foundation of trust. When new leadership inherited CECIDS in 2023, the system required a technical overhaul. But the harder work was restoring agency confidence in the data. When published numbers don’t match agency records, trust erodes quickly. As one leader put it: “You put out numbers, after building all this trust with your stakeholders, that don’t reconcile with them—you will lose [trust] like that. And it’s very hard to rebuild.”⁷ That meant planning to meet individually with each agency to review their numbers before anything was published.

Invested in the translator role. The most valuable hire is someone “deeply interested and curious about learning the business”—the programs, agencies, and data systems that make up early childhood.⁸ Rather than serving primarily as a technical expert, this person asks probing questions, understands stakeholders, and translates that understanding to the technical team. Knowing your audience also matters: sending SQL code to a family child care provider doesn’t meet them where they are, and communication that misses its audience can undermine trust.

Planned for financial sustainability from the start. CECIDS is now winding down in part because agencies were willing to share data but not dollars. The lesson: “If you’re going to stand up an ECIDS and do this time and investment and do it properly, have you really thought about that financial sustainability piece?”⁹ A financial model—embedded in existing grants, diversified across funding streams, or connected to state infrastructure—deserves the same attention as governance and technical design.

What These Stories Tell Us and a Glimpse at Other Examples

A few patterns stand out across these examples:

• Relationships, governance, and trust came before technology. In Kansas, leaders spent a year and a half getting five agencies to sign a data trust agreement before building anything technical. In Chicago, the agencies contributing data spent two years working through data sharing agreements.
• The translator role is indispensable. Both needed people who could bridge the technical and human dimensions of data work—what Kansas calls “soft infrastructure” and Chicago describes as a strong analyst who is curious about learning the nuances of the early childhood landscape.
• Start with questions. Both teams started with questions that mattered to decision-makers, using high-value use cases to build trust and momentum.
• Design for sustainability, don’t just hope for it. Kansas designed the data trust to outlast any single administration or champion. Chicago’s experience offers a caution: if the financial model isn’t built in from the start, even high-quality, well-used systems are vulnerable.

Other Promising Approaches

Making data visible. Indiana’s curates data for families, providers, and communities, reflecting the principle that data should serve users, not just builders. Nevada’s , codified through legislation from the start, offers public data on population, workforce, child care, and the state’s economy.

Linking data across agencies. Georgia’s links data across six programs and five agencies, demonstrating long-term sustainability through diversified state, federal, and philanthropic funding. Philadelphia’s connects birth, health, foster care, pre-K, and early intervention data through a longstanding university partnership, formalized by executive order in 2022. Rhode Island embeds financial sustainability into its by funding it in part through federal Medicaid dollars rather than time-limited grants.

Following children over time. Minnesota’s integrates data from birth records, child care assistance, home visiting, and early education programs as a companion to the state’s broader P-20 system. New Mexico’s —the nation’s first entirely cloud-native statewide longitudinal data system—connects early childhood, K-12, postsecondary, and workforce data across five agencies, and it illustrates the relational and project management discipline large-scale data systems require.

Using workforce registries to guide policy. Ohio’s converts workforce registry data into dashboards tracking turnover and retention. It demonstrated its value in supporting evidence-based policymaking by showing that the state’s Hero Pay bonus program correlated with greater retention among participating providers.

Cross-Cutting Implementation Challenges

Data infrastructure presents both technical and human challenges. Common friction points include:

• Governance complexity — Getting multiple agencies to agree on data sharing terms and sustain commitment across leadership transitions takes time
• Trust and data integrity — Stakeholders who know their own numbers will quickly lose confidence in a system that produces results they can’t reconcile
• Legacy systems — Many agencies hold data in outdated systems that are difficult to query, export, or link
• The unique identifier problem — Deduplicating children across agencies remains one of the most persistent unsolved challenges. Kansas found that aggregate data was the practical starting point; Chicago has a shared enrollment ID in place but consistent use across agencies is still a work in progress.
• The translator gap — Finding people who can bridge technical and policy work is difficult and easy to underestimate
• Turning data into action — Producing findings is not the same as changing policy; without communication and advocacy, even striking results can go underused
• Sustainability — Many data systems are built on time-limited grants, leaving them vulnerable when funding cycles end

Measures of Progress

Consider tracking:

• Agency and program participation and active data maintenance across contributing agencies
• Use cases answered and dashboards in active use by decision-makers
• Data quality indicators, including reconciliation rates and stakeholder sign-off on published figures
• Diversity of funding streams supporting the system
• Evidence of data informing specific policy or budget decisions

Tools for Action

• Toolkit for translating this brief’s findings into concrete next steps
• Questions to guide your work
• —a user-friendly, interactive digital tool that helps policymakers, advocates, and leaders explore how cities and states across the country are tackling challenges related to early education