Critical capabilities for unified governance

While individual tools might address specific needs, true data confidence emerges from a complete unified governance platform that brings these capabilities together.

For agencies navigating everything from Federal Data Strategy compliance to AI readiness, understanding these core capabilities is essential. Each one plays a vital role in transforming fragmented data practices into a unified governance platform that accelerates mission outcomes while maintaining security and compliance.

Let’s explore the seven critical capabilities that form the foundation of unified governance, and how they work together to deliver data confidence for your agency:

Critical capabilities

Data governance: The establishment and management of policies, processes and roles for data handling, decision-making and regulatory compliance

AI governance: The application of rules, processes and responsibilities to drive maximum value by ensuring streamlined, ethical AI practices that mitigate risk and protect privacy
Data quality & observability: A methodology (and a purpose-built software solution) for achieving high-quality, accurate and consistent data as well as insights into data health
Data catalog: An organized inventory of data assets that empowers stakeholders to find and use the right data
Data lineage: A visual representation of data flow, helping to manage the origin, transformation and consumption of data
Data privacy: A process and a goal to ensure sensitive data is protected and in compliance with data protection regulations is maintained
Policy control center: A centralized location to define, enforce and monitor data access and usage policies across the organization

By understanding the components of a unified governance platform and aligning your organization’s needs with what’s available in the market, you can make an informed decision on the right solution.

Data governance: Ensure collaboration and compliance

Data governance is the practice of managing and organizing data and processes to enable collaboration and compliant access to data. Data governance allows your data citizens — and that’s everyone in your organization — to create value from data assets. Leveraging a business glossary and a data dictionary as well as stewardship management and reference data management, enterprise data governance provides a single location to find, understand and create a shared language around data.

Why data governance is important

Unfortunately, when data governance goes badly, it can lead to consequences that could jeopardize your mission, including:

Misinformation: Incorrect decisions based on inaccurate data
Security breaches: Vulnerabilities due to improper data handling
Regulatory violations: Potential legal consequences and hefty fines
Wasted resources: Time and money spent rectifying data errors

The regulatory landscape is evolving on two fronts. Data protection regulations like CCPA in California and similar laws in 20 other states set strict requirements for handling data. Meanwhile, new AI-specific regulations are emerging with states like Colorado and Nevada establishing frameworks for algorithmic accountability and model auditing. This dual regulatory challenge makes robust data governance critical for public sector organizations.

Key features to look for

Comprehensive business glossary: A centralized location for all data-related terminologies ensuring consistency in understanding and usage
Stewardship management: Identifying owners for the data who ensure data quality and usage adheres to organizational policies
Reference data management: Managing standard data assets that are repeatedly used across the organization
Centralized policy management: A singular point to define, manage and monitor data-related policies
Workflows: Automated processes for data approval, quality checks and updates
Flexible operating model: A flexible model allows you to design your environment to meet your specific needs

Use case

Here are some key areas where data governance plays a critical role in the public sector:

Regulatory compliance/Reporting: Ensuring data accuracy and consistency for regulatory reporting (e.g., FOIA, GDPR, CCPA, AI Act) and audits, reducing risks of non-compliance and penalties
Public sector data sharing: Establishing clear governance frameworks to enable secure, standardized data exchange between agencies (e.g., health departments, law enforcement, social services) while protecting sensitive citizen information
Fraud detection/risk management: Leveraging high-quality, well-governed data to identify fraud patterns in public benefits programs (e.g., unemployment insurance, tax compliance) and improve risk mitigation strategies
Citizen services/digital transformation: Enhancing service delivery by ensuring citizen data is accurate, secure and accessible across digital government platforms, reducing redundancy and improving user experience

Start by asking these questions:

Strategic mission support

How will data governance enhance mission delivery
What specific mission objectives will be supported
Which stakeholders need to be involved

Compliance requirements mapping

Federal Data Strategy milestones
OPEN Government Data Act requirements
Privacy Act considerations
Agency-specific mandates

Think about how to map your data governance initiatives
to agency missions.

Data governance assessment worksheet

AI governance: Accelerating
data and AI use cases

To ensure mission success—and launch AI use cases and applications that leverage the capabilities of large language models (LLMs)—you should be thinking about your holistic AI strategy. However, successfully integrating AI initiatives into your roadmap will require a rigorous approach. To ensure AI is used responsibly, government agencies are leveraging AI governance.

AI governance is the application of rules, processes and responsibilities to drive maximum value from your automated data products by ensuring applicable, streamlined and ethical AI practices that mitigate risk and protect privacy.

Why AI governance is so important

The truth is data is the backbone of AI, and if the data is bad, the AI models trained on it will produce results that look good but are fundamentally flawed. The implications for the public sector agencies building AI applications are profound. And as AI’s impact grows, so does the scrutiny. While regulation around AI varies, we’re already starting to see some US states (including California, Nevada, Colorado and New York) pass laws that will require state agencies to monitor the use of generative AI.

It’s why AI governance is mission-critical and non-compliance can lead to:

Regulatory violations/Legal risks: Agencies face increasing AI regulations, such as bias audits for automated decision-making tools used in hiring, law enforcement and public benefits programs
Public trust erosion: Misuse of AI in government services—such as faulty algorithms in social services eligibility assessments—can decrease public confidence in government
Policy/Ethical concerns: AI-driven systems in areas like policing, fraud detection and social assistance must ensure fairness, accountability and transparency to avoid discrimination and systemic bias
Operational failures: Poorly governed AI can lead to inaccurate risk assessments in areas like public health response, cybersecurity and disaster preparedness, undermining mission-critical decisionsdata-driven, AI-powered world

Key features to look for

A well-defined AI governance solution should encompass:

Out-of-the-box, industry- standard frameworks, like the NIST
AI Risk Management Framework
Clear AI objectives and principles
Data quality and integrity checks
Model transparency and interpretability measures
Regular audits and updates
Ethical considerations, including fairness and bias checks
Legal and data compliance

As data becomes increasingly critical to organizational decision-making, the emphasis on data governance becomes more and more mission-critical. Whether you’re looking at traditional data governance or the emerging practice of AI governance, the presence of a clear, comprehensive framework is essential in our data-driven, AI-powered world.

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Use case

Here are some key areas where AI governance plays a critical role in the public sector:

Automated citizen services/Chatbots: Governing AI-powered virtual assistants for public inquiries (e.g. DMV services, tax filings) to ensure accuracy, transparency and compliance with privacy regulations
Fraud detection: Implementing responsible AI-driven risk assessment models in unemployment insurance, tax compliance and social benefits programs to detect fraudulent activities while maintaining fairness and due process
Cybersecurity/Threat detection: Establishing governance frameworks for AI systems used in government cybersecurity to detect threats while ensuring transparency and accountability in automated decision-making
Regulatory compliance/AI risk management: Implementing AI governance policies to meet evolving federal and state-level AI regulations, ensuring agencies maintain transparency in algorithmic decision-making

Data quality & observability:
Ensure value and transparency

Modern stacks are complicated. While amassing vast amounts of data is an achievement, it doesn’t automatically translate to value. And data without quality is like a recipe without any good ingredients — there’s potentially a meal at the end of the process, but if it’s not going to taste good, the recipe isn’t very useful.

Data quality refers to the ability of data to fit its intended purpose, ensuring it is accurate, complete and reliable for decision-making. Observability in the context of data is the ability to fully understand the health, status, and performance of data systems and pipelines through monitoring, logging and tracing. Together, data quality and observability give government data leaders and their colleagues the confidence that everyone is using trusted, high-quality data, and, as a result, you can accelerate every data and AI use case.

$15M

The average annual financial repercussions of poor data quality.
Source: Gartner Data Quality Market Survey

Why data quality & observability is so important

You rely on data to drive policy decisions, allocate resources and deliver public services effectively. But poor data quality causes costly mistakes, compliance risks and, ultimately, erodes the public’s trust in government. A data quality & observability solution ensures your agency can monitor data in real time, catching issues before they escalate.

Regulatory compliance: Accurate, auditable data is essential for FOIA requests, public records management and meeting federal reporting requirements
Operational efficiency: Reliable data improves decision-making for staffing, budgeting and service delivery, reducing waste
Fraud prevention/Risk management: Clean, well-monitored data helps detect fraudulent claims

Key features to look for

Data profiling: Understand your data’s structure, content and quality. This is the preliminary step in identifying areas that need attention
Data cleansing: Clean or remove corrupt, inaccurate or erroneous data to maintain data integrity and reliability
Data enrichment: Enhance the data’s value by appending related information from external sources to add depth and context
Data monitoring: Continuously track data streams to detect anomalies, inconsistencies or any deviation from the defined quality standards
Data pushdown: Address data quality issues at the source to improve security, reduce costs and enhance efficiency across agency systems

Use case

Here are some key areas where data quality & observability plays a critical role in the public sector:

Interagency data sharing: Maintaining data consistency across government agencies to improve collaboration on initiatives such as housing, transportation and public safety
Citizen services optimization: Improving the accuracy of public records, reducing errors in benefits processing and streamlining government digital services
Infrastructure/Resource planning: Using high-quality data to make informed decisions on public works projects, energy distribution and environmental initiatives

Six steps to data observability

Profile your data

Discover and classify all data sources
Identify sensitive data types and understand data structures

Define data policies and quality rules

Establish clear data policies and quality rules
Implement continuous testing and validation to maintain data integrity

Detect anomalies

Use machine learning to establish baselines and detect deviations
Identify potential data quality issues early

Monitor for impact

Correlate anomalies with business events to assess their impact
Prioritize remediation efforts based on severity

Notify key experts

Alert relevant stakeholders about data issues
Initiate and manage remediation processes effectively

Optimize continuously

Evolve data policies and practices based on insights from monitoring
Implement continuous improvement loops for ongoing optimization

These steps will guide you in building a reliable data infrastructure, ensuring high data qualityand supporting informed decision-making.

Data catalog: Deliver trusted data

A data catalog does more than inventory your agency’s information assets—it creates a single source of truth across your entire data ecosystem. And it streamlines the discovery, description and organization of data. By leveraging metadata intelligently, a data catalog connects stakeholders, ensures compliance and enables evidence-based decision-making. Agencies use data catalogs to meet Federal Data Strategy requirements and OPEN Government Data Act mandates while improving efficiency and transparency.

Why data catalogs are important

Government agencies can’t afford scattered data silos and disconnected systems—you need your data to work harder. A modern data catalog transforms how your agency operates by:

Delivering end-to-end visibility: Get a comprehensive view of your data assets, showing where they come from, how they transform and where they’re used
Maximizing the value of your data: Eliminate scattered data silos and establish a clear path to trusted data—for day-to-day operations to strategic initiatives
Breaking down silos: Unify scattered data assets so you can get the most out of your data and support agency initiatives

Use case

Here are some key areas where a data catalog plays a critical role in the public sector

Regulatory compliance: Simplifying data access for audits, FOIA requests and transparency mandates
Interagency collaboration: Enabling secure data sharing across departments for improved coordination
Evidence-based policymaking: Ensuring decision-makers have accurate, trusted data for public initiatives

Public services improvement: Enhancing service delivery by making high-quality data more accessible.

Choosing a data catalog | Checklist

Look for these capabilities when considering a data catalog:

Connectors: Make sure your data catalog offers a centralized repository of connectors to your most widely and actively used data sources.
This ensures your catalog is as comprehensive as possible
Curation: Make sure your data catalog offers tagging, grouping and documentation to improve data discoverability and understanding
Automated classification: Automation accelerates and drives efficiencies in classifying ingested data, eliminating error-prone manual
efforts and enhancing accuracy
Collaboration tools: An important element for operationalizing your data catalog is the capacity for users to communicate and interact
with each other within the data context by sharing comments, annotations and shared insights
Data marketplace: A distinguishing capability for enterprise data catalogs, a data marketplace is a platform where allowing users
at all levels can ‘shop’ for datasets easily, helping them discover, share and collaborate around data

Data lineage: Map the data journey

Data lineage is about understanding. As data moves through multiple systems, it transforms. Understanding its journey—where it comes from, how it changes and where it’s used—is critical for managing your complex data environment. Data lineage provides transparency, accuracy and compliance support with evolving regulations.

Why data lineage is important

Data doesn’t exist in isolation; it flows, transforms, gets consumed and influences decisions.

Trust and accuracy: Just as you’d trust a news story more if you knew its source, tracing data’s lineage boosts confidence in its accuracy
Data quality: If data seems incorrect or inconsistent, lineage helps pinpoint the stage where the discrepancy occurred
Compliance and audits: Regulations often require businesses to explain their data sources and transformations. Data lineage offers a clear map for compliance teams and auditors

Key features to look for

Automated lineage discovery: Eliminates manual tracking and ensures accuracy
Visualization capabilities: Graphical views make data flows easier to understand
Integration with diverse data sources: Connects with cloud, databases and third-party systems
Technical and business lineage: Supports both detailed data tracking and high-level impact analysis
Scalability: Handles large datasets without performance issues

Use case

Here are some key areas where data lineage plays a critical role in the public sector:

Regulatory compliance: Demonstrate compliance — for example, to CCPA — by mapping the journey of personal data through various systems and processes
Data migration: Assess the potential impact of planned data changes to business units across the organization, minimizing disruptions
Data lineage: Track and document the lineage of input, training and output data of AI models, ensuring transparency and accountability

Map your critical data flows

Use this template to document your highest-priority data flows:

Critical system inventory

List your mission-critical systems

System Name
Data Owner
Primary Purpose
Key Stakeholders

Draw your data flow

Assess impact

Data flow
Mission impact
Compliance requirements
Dependencies

Data privacy: Handle data properly

Data privacy is about protection. As sensitive information moves through government systems, it requires careful handling to safeguard citizen privacy, maintain public trust and ensure compliance with federal regulations. Privacy management provides the controls, visibility and governance needed to protect sensitive data while enabling agencies to fulfill their missions.

Why data privacy is important

Data privacy isn’t just about compliance—it’s fundamental to maintaining public trust and enabling secure government operations.

Public trust: Citizens share sensitive information with government agencies with the expectation it will be protected and used appropriately
Mission enablement: Strong privacy controls allow agencies to safely leverage data for improved public services while protecting individual rights
Regulatory compliance: Federal agencies must comply with various privacy laws including the Privacy Act of 1974, FISMA, and FERPA, among others

Key features to look for

Automated sensitive data discovery: Automatically identifies and classifies sensitive information across systems
Granular access controls: Ensures users can only access information necessary for their roles
Audit trails: Tracks and documents all data
access and usage

Use case

Here are some key areas where data privacy plays a critical role in the public sector:

Benefits administration: Protect sensitive citizen information while processing benefits applications and managing program eligibility
Cross-agency collaboration: Enable secure data sharing between agencies while maintaining privacy controls and tracking data usage
Law enforcement: Safeguard sensitive investigation data while enabling appropriate information sharing across jurisdictions

Data privacy is not just a legal necessity but also a marker of trust and reputation for organizations. As individuals, understanding and controlling our data footprint is the first step in ensuring our privacy in an increasingly interconnected world.

Think about how to map your data governance initiatives
to agency missions.

Privacy impact worksheet

Data inventory assessment

Purpose of Collection
Legal Authority
Sharing Requirements
Retention Period

Select your most common PII identifiers

Direct identifiers
Indirect identifiers
Derived data
Metadata

Policy control: Define and control data access

Policy control is about protection through permissions. As government agencies manage vast amounts of sensitive data, they need robust controls to enforce who can access what information and under what circumstances. Policy control provides the framework for implementing “need to know” principles while enabling mission-critical data sharing and use.

Why is policy control is important

Policy control isn’t just about restricting access—it’s about enabling secure and appropriate data use across government operations.

Mission enablement: Ensures personnel can access the information they need while protecting sensitive data from unauthorized access
Regulatory compliance: Helps agencies meet federal requirements including FISMA, FedRAMP and agency-specific security directives

What features to look for

Centralized policy management: Single interface to create and manage access policies across all systems
No-code policy creation: Enables subject matter experts to define policies without technical expertise
Automated enforcement: Consistently applies policies across systems without manual intervention
Data masking capabilities: Protects sensitive information while maintaining data utility

Use case

Here are some key areas where policy control plays a critical role in the public sector:

Classified information: Manage access to sensitive government information based on security clearance levels and need-to-know requirements

Interagency collaboration: Enable secure data sharing between agencies while maintaining appropriate access restrictions and tracking

Personnel records: Control access to federal employee information while enabling necessary HR functions and oversight

Create an access control strategy

Document domains
Complete this matrix for your key data domains:

Access levels

View only
Edit
Approve
Admin

Required clearances

Public trust
Secret
Top Secret
Compartmented

The power of active metadata

Active metadata is a dynamic, interconnected representation of data that can span a wide range of asset types, weaving together information about each asset. It’s a key component that brings together every component of a data governance platform across your organization.

The integrated view provides rich content and context to data, ensuring it can be both trusted and acted upon. Unlike static metadata which offers a snapshot, active metadata is constantly evolving, reflecting real-time changes and integrations.

Key benefits of active metadata

Generate greater visibility into the data landscape: With automatic data classification and autolinking of data sets, business terms, policies, processes and more, data curators and data consumers can collaborate on business semantics for trusted data
Evaluate the right data for your needs: Data profiling, data scoring and crowdsourced ratings and reviews strengthen data context and allow business analysts and data scientists to evaluate and choose the best data for their purposes
Enhance data shopping experience: With highly relevant and rich business context around data, users have a more intuitive and simplified data shopping experience, which provides the right data with the right context to the right users
Deliver faster insights: Automated discovery, understanding and collaborative data access for business analysts and data scientists reduce time to insights

Active metadata isn’t just another buzzword. It represents a shift toward a more interconnected, dynamic and actionable data environment. As data continues to grow both in volume and complexity, tools and methodologies like these will be instrumental in ensuring that data remains an asset, not a challenge.