Governments increasingly recognize that connected intelligence is the key to unlocking more responsive, efficient, and impactful public services. By integrating data across platforms and organizations, they can 1) build a holistic view of individuals — enabling capabilities like Customer 360 to personalize services, anticipate needs, and reduce friction regardless of the digital touchpoint or organization they are interacting with; and 2) power up AI initiatives to help drive productivity and delivery on mission goals. But the use cases are countless: delivering smarter answers to keep airmen out of harm’s way, coordinating emergency response across jurisdictions, optimizing aircraft carrier movements, or even streamlining everyday services like mattress pickups and permit requests. These diverse use cases share a common foundation — mature data management and governance.

Forrester’s Connected Intelligence Framework helps organizations activate this potential by aligning partners, practices, and platforms across eight essential activities, from sourcing and preparing data to deploying and evaluating AI systems. With embedded data governance, the framework also reduces the risk of fragmentation and inefficiency, ensuring that every insight is trustworthy and actionable.

Why It’s Harder For Government Leaders

Governments face a unique set of data governance challenges that stem from strict regulatory requirements, complex mission structures, constrained budgets, and the need to balance security with innovation. Addressing these issues is essential not only for compliance but also for enabling scalable, trustworthy AI systems. Some of the most pressing data governance hurdles that organizations face include:

  • Secure data access through privacy and compliance. Federal organizations handle sensitive data across clearance levels and mission-critical platforms. Without proper classification, the risk of leaks and noncompliance rises. Role-based or attribute-based access controls, data obfuscation, and consent management enable secure, compliant sharing across stakeholders.
  • Break down silos with integration and interoperability. Legacy systems and fragmented teams slow data flow across organizations, hindering AI adoption and decision-making. Organizations should prioritize open standards and integration frameworks to enable seamless data exchange.
  • Meet regulatory requirements with compliant cloud storage. Organizations must store data within US borders on FedRAMP-authorized platforms. Scaling AI while meeting these mandates is challenging, but government-specific cloud offerings from AWS, Azure, and others help balance compliance and performance.
  • Boost data usability with metadata and context. Without clear metadata, data is hard to interpret, trust, or reuse — limiting AI effectiveness. Investing in catalogs, glossaries, and stewardship builds shared understanding and improves data literacy.
  • Modernize governance for agility and scale. Centralized or siloed governance models limit innovation and cross-domain collaboration. A federated model with a data product mindset empowers teams while maintaining oversight — enabling both flexibility and control.

A Smarter Path Forward

To address these challenges, federal organizations should start by aligning their governance efforts with top objectives. As maturity increases data governance is embedded into daily workflows and the cost-benefit pyramid inverts, shifting from high setup costs to scalable, sustained value. We recommend building four foundational governance pillars:

  • Policies and procedures – to ensure compliance and operationalize governance. Government organizations must navigate complex regulatory landscapes while managing sensitive data across missions. Clear policies and procedures — such as role-based access controls, data classification standards, and audit protocols — help operationalize governance and ensure compliance with mandates like FISMA and FedRAMP. For example, the Department of Veterans Affairs uses automated policy enforcement to manage health data access across care teams and contractors. Start by mapping regulatory requirements to data policies, defining a RACI matrix to define responsibilities, and automating policy enforcement through workflows and audits.
  • Catalogs and lineages – to enhance discovery, transparency, and trust. Government groups often struggle to locate and understand their data assets across siloed systems. Implementing robust data catalogs and lineage tracking enables teams to discover, trace, and trust data sources — critical for initiatives like predictive analytics in public health or fraud detection in benefits programs. The CDC, for instance, uses metadata catalogs to unify disease surveillance data across states and labs. Implement a catalog or augment an existing one to build a semantic layer, ensure lineage captures end-to-end data flow, and automate root cause analysis using lineage and semantics.
  • Privacy and security – to enable safe data democratization. Balancing data access with privacy and security is essential for democratizing insights across organizations. Techniques like data masking, encryption, and differential privacy allow broader use of sensitive data without compromising safety. The DoD applies attribute-based access controls to share mission-critical intelligence across branches while protecting classified information. Governments can invest in attribute-based access controls and data obfuscation mechanisms to safeguard sensitive data and conduct privacy impact assessments to evaluate the risk.
  • Collaboration and sharing – to accelerate innovation and cross-agency insights. Connected intelligence thrives on collaboration. Organizations must break down silos to share data across jurisdictions, enabling smarter decisions — from coordinating wildfire response to streamlining permit approvals. FEMA’s integrated data-sharing platform, for example, allows local, state, and federal responders to access real-time disaster data for faster, more coordinated action. Facilitate this using inter-agency data-sharing agreements, federated governance models, and shared platforms with real-time access and audit capabilities.

Forrester’s Connected Intelligence Framework offers a roadmap to help government leaders unify data and AI efforts. Forrester clients that want to explore how their organization can modernize its data governance and accelerate AI readiness can connect with our analysts or access our latest research. If you’re planning to attend our upcoming Technology & Innovation Summit North America in November, be sure to check out my session entitled “Governance By Design Fuels Trusted AI.”