12 Data Governance Best Practices and How to Implement them in 2025

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What are the 12 recommended best practices for the success of your data governance program? #

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How you implement your data governance program will depend on your organization and its specific needs. However, the following principles represent best practices of data governance that apply to almost all companies:

Data governance best practices to follow for success - Image by Atlan.

1. Secure executive buy-in #

No governance program succeeds without visible leadership. Executives set the tone, allocate resources, and embed governance into strategy.

• Strategic sponsorship: Link governance to measurable business outcomes like revenue growth, risk reduction, or AI adoption.
• Resource allocation: Secure budget, tools, and talent to prevent governance from stalling.
• Visibility: Executives must champion governance in communications, signaling its enterprise-wide importance.

Start by identifying which executive priorities (e.g., compliance, efficiency, AI-readiness) governance directly supports, and frame your business case around them.

2. Identify your vision by leading with your “why” #

Every governance framework starts with a clear “why” — a business goal, corporate driver, or strategic priority. This purpose defines how governance delivers value and aligns with organizational objectives.

Modern governance is no longer a top-down directive but a decentralized, community-led initiative. For it to succeed, employees must understand the purpose behind your data governance program, policies, and standards.

Start by asking teams how they envision your organization’s data culture evolving over the next 12–18 months.

3. Embrace a product mindset #

A data product is any asset that delivers value from data — datasets, warehouses, dashboards, algorithms.

DJ Patil, who was formerly the Chief Data Scientist at the US Office of Science and Technology Policy, notes even internal dashboards qualify, broadening horizons for scalable insight and action.

Prukalpa Sankar, co-founder at Atlan, adds that like any product, success is measured by user needs, not features. So, data teams should prioritize user experience, adoption, and reusability over ad-hoc requests.

Read more → How to apply product thinking to data

Start by treating each data domain as a product, and its consumers (analysts, scientists, and business managers) as customers.

Domain data owners (i.e., data product owners) are responsible for ensuring their datra products are :

• Reusable
• Reproducible
• Well-documented
• Scalable
• Accessible
• Easy to understand and use, enabling self-service

4. Develop frameworks and policies #

Frameworks and policies transform abstract goals into operational reality. They provide structure and enforce consistency.

• Governance framework: Define decision rights, escalation paths, and accountability mechanisms.
• Policies and standards: Cover data quality, security, lifecycle management, and regulatory compliance.
• Scalability: Use modular frameworks so governance grows with business needs.

Start by choosing a reference framework and tailoring it to your organization’s culture and regulatory environment.

Read more → Everything you need to know about data governance frameworks

5. Map roles and responsibilities #

Clarity in ownership prevents duplication and gaps. Every role in governance needs definition.

• CDO or lead: Accountable for strategic alignment and program oversight.
• Data owners and stewards: Define, monitor, and enforce data standards.
• Privacy, legal, and security: Ensure compliance with regulations and risk safeguards.
• Cross-functional accountability: Prevent silos by distributing ownership across domains.

Start by mapping your data domains and assigning clear ownership for each, ensuring accountability spans business and technical teams.

Read more → Data governance roles and responsibilities

6. Ensure seamless data management and operations #

Governance must simplify how data flows across its lifecycle while prioritizing quality with:

• Lifecycle management: Standardize how data is created, stored, archived, or retired.
• Data quality: Build profiling, validation, anomaly detection and other data quality metrics into pipelines.
• Automation: Use metadata-driven lineage and monitoring to keep governance invisible yet always active.

Start by cataloging your critical datasets and establishing baseline quality metrics before layering in automation.

Read more → Data governance lifecycle 101

7. Embed collaboration in daily workflows #

A core outcome of governance is making data easy to access, understand, and use. Metadata plays a central role by adding the context that makes data discoverable and trustworthy.

But context loses value if it’s locked in yet another tool. As Slack engineer Josh Wills put it, no one wants a “third website to just browse metadata.”

That’s why embedded collaboration is instrumental.

Work should happen where people already are, with the least friction. By embedding metadata into daily workflows, teams can search definitions, trace lineage, or view discussions directly within their preferred tools.

For example, users could query data definitions in Slack or trace lineage in Looker without leaving their workflow. This reduces context-switching and makes governance seamless.

Start by identifying the right platform, which integrates with your entire tech stack and seamlessly interoperates, driving embedded collaboration.

8. Automate wherever possible #

Data growth has far outpaced the ability of humans to govern it manually. Automation must therefore be a cornerstone of any governance strategy.

Automation in data governance eliminates error-prone, unscalable manual work and ensures policies, quality checks, and security controls keep pace with demand. Key applications include:

• Granular access control: Automatically enforce column-level permissions by user, group, or team.
• Auto-constructed lineage: Visualize data flows instantly, without writing SQL.
• Policy propagation: Extend tags and protections through lineage—for instance, automatically masking PII in every downstream report.
• Audit logs: Generate real-time usage and compliance records.

The business impact is dramatic. Tide, a UK-based digital bank, cut PII discovery from 50 days of manual work to five hours by using Atlan Playbooks to automate classification and tagging.

Start by automating one repetitive governance task, like sensitive data classification, to demonstrate time savings and build momentum for broader automation.

9. Invest in the right technology #

With cloud adoption and consumerized tech raising expectations, employees now demand enterprise tools that are intuitive, fast, and collaborative.

The right governance platform should combine ease of use with automation and scale. Look for:

• Searchable data catalog: A single, easy-to-use inventory that cuts wasted hours spent searching across systems.
• Customized workspaces: Role- or persona-based views that surface only the most relevant data.
• Business glossary: The second brain of your business, where dynamic definitions and common terminology align teams.
• Granular, role-based access: Automated, column-level controls to enforce privacy and security at scale.
• Automation: Features like automated lineage, classification, and policy propagation that remove manual overhead.
• Column-level lineage: End-to-end traceability and transparency with column-level data lineage to build trust in data.
• Data quality profiling: Real-time checks and anomaly detection with data quality profiling tools to maintain standards across the lifecycle.

Start by auditing your current stack for duplication and friction, then pilot a unified control plane that centralizes metadata, quality, and access policies.

Read more → What is a unified control plane for data, metadata, and AI?

10. Build habit loops for adoption #

Sustainable adoption comes from behavior change, not mandates. Habit loops embed governance into daily work.

• Cue, routine, reward: Identify existing behaviors and introduce governance practices with clear rewards.
• Persona-driven rollout: Show how governance tools solve pain points for analysts, engineers, and business users.
• Momentum: Launch pilots, celebrate quick wins, and scale through positive reinforcement.

Start by piloting governance with a single team or persona, solving one of their daily frustrations to create early adoption loops.

11. Foster a sustainable governance community #

Governance thrives when it’s shared, not centralized. Building community drives scale and resilience.

• Celebrate and socialize wins: Share success stories to reinforce progress.
• Data literacy initiatives: Train teams on governance tools and principles.
• Distributed responsibility: Add governance to OKRs, making it part of how teams measure success.

Start by creating a cross-functional forum where wins and lessons are shared regularly, reinforcing that governance is collective.

12. Build a culture of continuous monitoring and adaptation #

Governance isn’t static; it must adapt as business and technology evolve.

• Metrics and KPIs: Track quality, security, and adoption continuously.
• Regular audits: Identify gaps and prevent risks before they escalate.
• Iteration: Refine frameworks, policies, and tools to stay aligned with strategy and regulation.

Start by defining 3–5 measurable KPIs (e.g., % of governed assets, policy compliance rates) and reviewing them quarterly with stakeholders.

Read more → Top data governance metrics to track

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Data governance best practices checklist #

• [ ] Lead with your “why” → Aligns governance with business goals and builds shared purpose.
• [ ] Adopt a data product mindset → Treats data as reusable, valuable assets for the business.
• [ ] Secure executive buy-in → Ensures governance has funding, authority, and visibility.
• [ ] Develop frameworks and policies → Provides structure, rules, and guardrails for daily data use.
• [ ] Map roles and responsibilities → Creates clear ownership and accountability for data.
• [ ] Embed collaboration in workflows → Makes governance seamless inside existing tools and processes.
• [ ] Automate wherever possible → Reduces manual effort, scaling governance to match data growth.
• [ ] Invest in the right technology → Centralizes cataloging, lineage, and policy enforcement.
• [ ] Ensure data lifecycle management → Covers data from creation to archival or deletion.
• [ ] Prioritize data quality and security → Maintains trust and protects sensitive information.
• [ ] Build habit loops for adoption → Encourages sustainable use of governance practices.
• [ ] Foster a governance community → Celebrates wins, builds literacy, and distributes responsibility.
• [ ] Monitor, measure, and adapt → Tracks KPIs, runs audits, and evolves governance continuously.

Get My Governance Roadmap →

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Why should you follow these data governance best practices? #

The data governance best practices we’ve identified here address why some data governance programs fail.

Many governance programs exist, but few succeed.

According to Gartner’s D&A governance survey in 2021, 61% of organizations aimed to optimize data for business processes, yet only 42% felt on track.

In the same survey, Gartner predicted that 80% of companies scaling digital business will fail without a modern, decentralized, and collaborative approach to data governance.

That’s why it’s crucial to embrace these data governance best practices. You can think of them as guard rails and policies that help you answer questions, such as:

• What data do we have and where does it live?
• How does it flow through the organization?
• Who owns, modifies, and uses it?
• How is it accessed, shared, and reported?

Get My Governance Roadmap →

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Real stories from real customers: Generating business value from data governance in practice #

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Ready to scale your data governance program effectively with our 12 recommended best practices? #

By treating data as a product, embedding governance into daily workflows, and investing in modern platforms, organizations can build a culture where data is trusted, accessible, and strategic.

You can start by identifying a high ROI use case for data governance and following the best practices above. Once you’ve seen proof-of-concept, you can scale data governance for the remaining data and analytics use cases.

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FAQs about data governance best practices #

1. What are data governance best practices? #

Data governance best practices are guidelines and frameworks that organizations use to manage data quality, accessibility, security, and usability. Examples include securing executive buy-in, defining clear goals, creating policies, mapping responsibilities, ensuring quality and security, leveraging automation, and fostering a culture of continuous improvement.

2. Why is data governance best practices important for my organization? #

Without best practices, governance often fails—becoming bureaucratic, fragmented, or poorly adopted. Best practices provide guardrails that align governance with business strategy, improve adoption, and deliver measurable value.

3. What role does executive buy-in play? #

Executive support ensures governance is prioritized, funded, and aligned with business outcomes. Without it, governance programs often stall or fail to scale across the enterprise.

4. How can I implement effective data governance practices? #

To implement data governance successfully, start by defining a clear framework, assign roles, establish data quality standards, and use supporting technology. Regularly review and adapt practices to meet evolving organizational needs and compliance requirements.

5. How do data governance best practices promote a data-driven culture? #

They embed governance into workflows, improve data literacy, and foster collaboration across teams—normalizing responsible data use and building trust in the organization’s data.

6. What tools help implement data governance best practices? #

Modern metadata-driven platforms like Atlan unify catalogs, lineage, quality, and access controls in one control plane. This makes governance seamless and user-friendly, ensuring adoption while cutting governance overhead.

7. How do data governance best practices adapt to AI? #

They emphasize metadata management, transparency, and explainability—ensuring AI models are trained on trustworthy data, comply with regulations, and remain auditable.

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