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Interoperability is a trust problem, not just a technology problem

Lessons from the 2026 State Chief Data Officer Roundtable on enabling data sharing and breaking data silos

Interoperability is a Trust Problem, Not Just a Technology Problem | Deloitte US
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In brief

Government data interoperability is primarily an organizational and structural challenge, not a technological one. Drawing on insights from the January 2026 State Chief Data Officer Roundtable, this article argues thattechnical integration fails without a foundation of institutional trust. Success requires a shift away from “bespoke sharing” toward a model where governance establishes the rules, culture provides the “social license,” and technology serves as a reciprocal shared service.

The context: Introduction

A persistent challenge in state operations is the failure of basic resident data to flow cleanly across programs, leading to manual rework, human error, and operational exhaustion for the workforce. Insights from the 2026 State CDO Roundtable reveal that initiatives for data integration and interoperability rarely stall due to technology. Instead, they falter because every request to exchange data with agencies becomes a one-off negotiation over risk and approvals. The core issue is not a lack of tools but the absence of predictable permissions and institutional trust. To overcome the default “no,” states must address structural obstacles across three pillars: governance, culture, and technology.

Effective data governance is data enablement. It builds the trust required for repeatable cross-agency and cross-state data sharing by establishing quality, discoverability, common terminology, and clear ownership. Without a shared operating model, government data management defaults to slow, case-by-case negotiations that erode trust. Leading states are moving toward scalable models built on five foundational practices:

  1. Data standards: Adopting a mandatory baseline for standards (e.g., ISO, HL7 FHIR) to reduce friction.
  2. Veto rights: Formalizing veto power to provide agencies the psychological safety to participate.
  3. Standard permissions: Using Master Data Sharing Agreements (MDSAs) to replace one-off legal reviews.
  4. Decision roles: Publishing clear RACI (Responsible, Accountable, Consulted, Informed) matrices to designate specific decision-makers.
  5. Shared definitions: Embedding shared semantics into policy frameworks to ensure terms mean the same thing everywhere.

A positive culture for data sharing emerges when data interoperability is positioned as a service that returns time to programs, not as a mandate. Agency staff, often overwhelmed, will only collaborate if they see a direct benefit. This shift from a defensive to a collaborative culture is driven by five practices:

  1. Executive support: Leaders must provide visible “top cover” by tying data sharing to priorities and unblocking bottlenecks.
  2. Data literacy: Empowering staff to use shared data confidently through role-based, workflow-embedded training.
  3. Quick wins: Focusing on a steady cadence of 30-60-90-day wins on discrete, resident-facing use cases.
  4. Reciprocal benefit: Ensuring the relationship is symbiotic, where the state data office provides a functional asset (like a dashboard) back to the agency in exchange for data.
  5. Operational efficiency: Reducing the burden of participation by automating routine extractions and reconciliations.

Technology translates agreed-upon governance and culture into a reusable, low-friction capability. The government data exchange layer is more than a technical integration; it is a platform that enables human usability and builds confidence for a secure data-sharing government. This is achieved through three key practices:

  1. Product ownership: Managing the exchange layer as a product with a dedicated owner who is accountable for a user-informed roadmap.
  2. Traceability: Providing usage dashboards and audit logs so agencies can see exactly who accessed their data and why, building confidence by design.
  3. Hybrid design: Meeting agencies where they are with an API-first when possible but always a compatibility-first approach that supports modern patterns while remaining backward-compatible with legacy systems.
The path forward

The CDO roundtable made it clear that interoperability advances fastest when states create structured forums for collaboration. Two effective approaches are:

  • Interoperability labs: Immersive sessions that align agencies around a single resident outcome to resolve high-impact priorities across governance, culture, and technology.
  • Regional working groups: Steady-cadence meetings that sustain relationships and decision processes, ensuring that the data-sharing governance framework endures across new programs and administrations.

By convening stakeholders to align on shared challenges and a practical path forward, states can build a lasting capability for data movement for the public sector and act as one government when residents need it most.

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Dr. Adita Karkera

Dr. Adita Karkera

Chief data officer | Government & Public Services | Deloitte Consulting LLP
+1 501 710 0459
Uday Katira

Uday Katira

GPS Strategy & Analytics USDC Leader | Deloitte Consulting LLP
+1 407 619 4485