Medicaid Data Strategy

The Challenge

Medicaid and CHIP together cover more than 90 million Americans. The Center for Medicaid & CHIP Services (CMCS) at CMS oversees these programs, relying on data from every state and territory to make policy decisions, track program performance, and respond to federal inquiries.

That data landscape is enormous. CMCS manages data across dozens of systems and contractor teams spanning enrollment, eligibility, quality measurement, claims, and more. Over the years, these systems grew independently. Different groups adopted different tools and workflows. Data was collected and stored without shared standards, and there was no unified strategy tying it all together.

The consequences showed up in day-to-day operations. When leadership needed answers to cross-cutting questions about program performance, pulling the data together was slow and labor-intensive. Analytic tools and reporting capabilities were spread across the organization with significant overlap, and institutional knowledge sometimes left with the contractors who built it. For an agency responsible for programs at this scale, the gap between the data CMCS had and the data CMCS could readily use was significant.

CMCS recognized the need for a comprehensive data strategy to bring coherence to this landscape. For People partnered directly with CMS on the MACBIS Data Strategy contract to build one.

Our Approach

For People led a three-phase engagement.

We started with research. Our team interviewed stakeholders across CMCS and the broader MACBIS ecosystem to understand how data actually moved through the organization. We mapped who produced data, who consumed it, where it was stored, and where the gaps were. We documented the pain points that staff and leadership dealt with daily.

What we heard confirmed the scale of the challenge. Teams across CMCS operated in silos, often unaware that other groups had already solved similar problems. There was no central data governance body, no shared definitions for key metrics, and no standard process for onboarding new data sources or retiring old ones.

From this research, we developed a comprehensive data strategy organized around five priority areas, each with guiding principles and a concrete implementation roadmap.

We grounded every recommendation in the specific problems we'd heard from staff and tied each one back to measurable improvements in how CMCS could manage and use its data. The strategy wasn't abstract. It was built to be actionable.

We also developed a path analysis recommending the creation of a Center of Excellence in Data within CMCS. This team would own the governance framework, manage shared analytic infrastructure, and serve as the connective tissue between the agency's data producers and consumers.

Data Strategy & Governance • User Research • AWS QuickSight • R • Databricks • Python