in short:
Data governance is integrated into daily tasks. As federal agencies expand artificial intelligence and data-driven decision-making, the ability to trust and act on data is now critical for speed. Institutions that view governance as an operational discipline are better able to reduce uncertainty, align teams, and act faster when results matter most.
You’re in a room where time matters.
The screen is filled with numbers and maps. People are speaking out. There are some systems that get extra attention because they are systems where you can’t make mistakes.
Every decision depends on an assumption: that the information in front of you is trustworthy.
That’s the purpose of the test for Artemis 2 — a crewed mission launching on April 1, 2026, to confirm that the spacecraft’s systems are operating as expected in deep space before NASA moves on to the next phase.
Federal agencies face the same moment, just without the rockets.
Across government, data now determines how benefits are delivered, how funds are allocated, how risks are assessed and how artificial intelligence tools are used. Leaders know the data exists—the question is whether they can act on it. Which numbers are official? Who is responsible for them? How long does it take for others to notice when something changes?
These are practical questions. Answering these questions integrates data governance into daily tasks.
What makes governance urgent now
The use of artificial intelligence in government is growing rapidly, raising risks to data quality, traceability and access. In a July 2025 report, GAO found that AI use cases reported by selected agencies nearly doubled, from 571 in 2023 to 1,110 in 2024, with generative AI use cases increasing approximately ninefold (32 to 282).
At the same time, expectations for how institutions manage and publish data assets continue to mature. OMB’s Phase II Evidence Act Guidance (M-25-05) strengthens requirements for data inventories, metadata, and management practices to support access and evidence construction while maintaining appropriate safeguards.
In short: more usage, more visibility and less tolerance for uncertainty.
Flight Plan: Four governance initiatives to help organizations stay on track
If governance is to accelerate the execution of tasks, it must show where the work happens—access decisions, shared definitions, accountability, and safeguards that emerge only after the fact. These four moves are designed for just that.
Initiative 1: Establish a decision-making data committee
Many institutions already have data committees in place. The difference between a helpful council and a slow council often comes down to two things: purpose and authority.
When committees exist primarily to review documents and share updates, the same pattern emerges: meetings are held but no decisions are made; each office has its own priorities; no one knows what real changes will come next.
Councils that drive work forward are grounded in clear agency-level goals and led in a way that benefits the entire agency. It brings together mission leaders, IT, security and data leaders and treats data and AI as connected efforts.
More importantly, it makes decisions that remove confusion: which datasets are considered official; who owns them (and what ownership actually means); what access looks like; and what to prioritize first.
When these decisions are made early, teams stop wasting time arguing about which number is real and start working from the same map.
Step 2: Use inventory as a starting point — then make the data available
Inventory is necessary. They are also where a lot of motivation goes away.
Agencies often complete a checklist to address a need and then work to turn it into something that mission teams can use. Common failures are predictable: unclear priorities, limited availability, and no clear ownership associated with keeping information current.
The catalog becomes valuable when it is built for non-experts and not just data teams. This means:
- Explain in plain language what the data is and why it exists
- Clear ownership so people know who to contact and by whom to update
- Access flags prevent sensitive data from being distributed haphazardly while still being found by those who truly need it
Availability also depends on traceability. When teams understand where data comes from, confidence in dashboards and AI outputs increases because accountability is no longer just implicit.
Step 3: Focus data quality where it matters most
If the goal is to solve all problems, then data quality is where good intentions end.
A more realistic approach is to focus first on high-impact data—data sets that are relevant to outcomes on which the institution cannot make mistakes, such as grant, benefit, and eligibility decisions. This focus keeps governance pragmatic and ties quality work directly to mission outcomes.
This is not a theoretical question. A 2025 survey from the IBM Institute for Business Value found that data quality remains a top operational priority, with organizations expecting bad data to cause significant financial losses.
When teams trust the data they work with, the impact is immediate. Decisions move faster because inputs are not constantly being questioned. With consistent definitions and sources, duplicate reporting and manual reconciliations are reduced. Audits become easier to support because the underlying data is easier to interpret. Adopting AI is less risky because teams are not providing outdated or unclear inputs to the model.
Step 4: Build security into the process from the beginning
In a federal environment, it is no longer practical to separate governance from security. Governance determines who can access data, how it is shared, and how sensitive information is protected.
Artificial intelligence makes this inseparability even more obvious. People are often quick to trust AI outputs, and they can also be quick to spread confusion if they are based on data that people don’t know much about.
The problem is often timing. Introducing controls after data has been shared, after an AI tool has produced output, or after an audit forces a response, leading to rework and stalled progress.
Building security early looks like:
- Classify data and define access rules from day one
- Involve privacy and security teams in the development process
- Treat safeguards as part of the workflow rather than a separate approval layer
This is how agencies protect mission speed over the long term by reducing late-stage slowdowns.
SHI helps organizations turn intent into action
Many institutions are not in trouble because of a lack of policy. They get into trouble because of the difficulty of executing in a real-world environment—siled departments, unclear ownership, limited time to process years of accumulated data, and modern constraints that make large-scale change impractical.
SHI helps agencies build momentum without disrupting mission systems by building on existing foundations and layering governance in a pragmatic, incremental manner.
Usually looks like:
- Convert decisions into action. Define ownership “swim lanes,” decision rights, and a board structure to align data, AI, and mission leaders.
- Make the data available. Transform inventory into a navigable catalog with useful metadata and clear traceability.
- Prioritize what’s important. Focus on high-impact data sets first to make visible and sustainable progress.
- Build security early. Establish classification and access rules up front so AI doesn’t amplify sensitive or ambiguous data.
- Linking governance to AI readiness. Align governance efforts with what agencies are trying to do now — scale analytics and AI responsibly and get teams moving together.
Data governance doesn’t speed up tasks because it adds to the process. It speeds up tasks because it reduces uncertainty.
When teams agree on what the data means, who owns it, how it is used, and how it is protected, leaders spend less time reconciling conflicting answers and more time making decisions that stand up to scrutiny.
Next step
Want to know what data governance looks like in your institutional environment? We’re here to help you with this – please contact our team to start a conversation.
Want to learn more about how federal teams are implementing artificial intelligence and modernization? Read our latest opinions.
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