Data governance: Building AI confidence through control and context
More than three decades ago, George Labovitz and Yu Sang Chang introduced the 1-10-100 rule of data quality. It costs one dollar to prevent a data error, ten dollars to correct it, and one hundred dollars if the error is ignored.
Today, bad data can be exponentially more expensive. With AI, hundreds of users and models rely on shared datasets. Labovitz and Chang’s math evolves to 1-10-100-1,000,000: One unchecked data anomaly can ripple across systems, models, and dashboards, turning a small oversight today into multimillion-dollar consequences tomorrow.
Avoiding those consequences requires cohesive data governance, but developing those frameworks is challenging for organizations, particularly federal agencies. Gartner projects that, by 2026, organizations will abandon about 60% of their AI projects because, without strong governance, they do not have AI-ready data. Neglecting governance doesn’t just reduce efficiency; it undermines trust and limits the impact of AI.
But good governance doesn’t require static committees, checklist-style compliance, and a lot of red tape. Instead, adaptive governance can turn accountability into empowerment. It’s this kind of unified, people-first governance that’s desperately needed not only to increase efficiencies, but also to build trust, and unleash the impact AI can have across federal agencies.
An adaptable and accountable approach to governance
Because data models consume and generate data, governance must address both actions as part of a single, connected ecosystem. Unified governance ensures that policies, lineage, and access controls apply consistently across datasets, models, prompts, and outputs. It gives organizations a clear view into how data is transformed, algorithms are trained, and insights are produced.
Governance must also be integrated into daily workflows for agency staff. People-first governance allows policymakers, data owners, and analysts to collaborate in real time so that quality and compliance are built into the data lifecycle, not added later.
Above all, AI governance cannot be static. It must be frequently reviewed, revised, and updated as agency requirements or public needs evolve over time.
To shift to a unified, people-first governance approach, agencies should:
- Start with structure. There must be a clear, risk-adjusted framework in place that defines how data and AI systems are created, managed, and monitored. This framework should facilitate transparency across data flows and ownership.
- Empower with integration. Embed governance controls directly into agency platforms and workflows to drive efficiency, not bureaucracy. Agencies should integrate governance into data catalogs, MLOps pipelines and collaboration tools, so reviews and audits happen seamlessly as part of normal operations.
- Design for adaptation. AI governance should evolve as needs change. Agencies should continuously engage their stakeholders and use their feedback to inform updates to governance frameworks, allowing the agency to adjust to new technologies, policies, and mission needs.
- Lead through trust. Leaders must demonstrate how AI governance can be a catalyst for innovation. One way to do this is to publish clear KPIs and audit trails that convert oversight into confidence and confidence into impact.
Accelerating AI governance with ICF Fathom
Data governance protects mission-critical data, makes AI explainable, and ensures insights can withstand regulatory and congressional scrutiny.
ICF Fathom operationalizes these four governance principles in a single secure environment for federal agencies, replacing security-only frameworks with adaptable, context-aware policies. By automating tasks like metadata tagging, content management, and audit tracking, Fathom allows agencies to maintain control and compliance while accelerating innovation.
With its policy-as-code foundation, Fathom enables AI systems to operate within defined boundaries of trust, turning governance from a static requirement into a living framework for accountability and performance. This allows innovation and compliance to coexist, forming the foundation for:
- Reliable data quality: AI models depend on accurate, complete, and contextual data. Governance validates and standardizes inputs so that models perform with confidence.
- Ethical and explainable AI: Governance provides transparency and accountability across the AI lifecycle, ensuring that algorithms remain fair, traceable, and aligned with mission objectives.
- Scalable compliance: Governance rules can be embedded directly into pipelines and APIs so that compliance is enforced at every transaction within DevSecOps and MLOps environments. This can simplify regulatory reporting, audit readiness, and risk management.
From governance to AI action
Building a cohesive, flexible governance framework is an essential step for federal agencies as they move away from siloed data management and toward integrated intelligence. It’s essential not only to support real-time decision-making and operational efficiency but also to facilitate accountability. Governance protects critical mission data, makes algorithms explainable, and ensures that insights derived from AI can be defended under regulatory or congressional scrutiny.
Far from slowing down innovation, good governance helps agencies avoid the 1-10-100-1,000,000 fate, ensuring they can explore confidently and realize the full value of their AI initiatives.
