Data Governance Models Explained With Examples

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by Emily Winks, Data governance expert at Atlan.Last Updated on: December 12th, 2025 | 19 min read

Quick answer: What are data governance models?

A data governance model is how you practically implement data governance initiatives. It turns data governance goals into real operational steps, explaining who sets data rules, who enforces them, how compliance is monitored, and what tools support the process.
There are three foundational types of data governance models, including:

  • Centralized data governance
  • Decentralized data governance
  • Federated data governance

Read on for data governance model.

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Understanding the data governance models

Summarize and analyze this article with 👉 🔮 Google AI Mode or 💬 ChatGPT or 🔍 Perplexity or 🤖 Claude or 🐦 Grok (X) .

Data has no value on its own. Its value comes from context. Understanding what the data means, where it lives, who owns it, and how it should be used makes data truly valuable.

To have this context, you need a straightforward approach for how your organization manages and uses data across teams. That’s where data governance models come in.

They work like an operating system for your business, guiding how data stays reliable and usable at every step.

There’s no single answer to “What is the best data governance model?” It depends on various factors and takes business priorities into account. You’ll need to understand different data governance models to find a suitable fit.

Is data governance model the same as a data governance framework?


No, there’s a distinction between the two:

  • Data governance models tell who decides, who executes, and who enforces. The framework describes policies and lifecycle processes. A data governance model, on its own, is just like an org chart. It doesn’t tell teams what to do.
  • A data governance framework (such as DAMA-DMBOK or DCAM) provides the policies, standards, classification methods, and lifecycle processes necessary to operationalize a data model across teams and data products.

Real-world governance integrates models and frameworks as data environments get complex and distributed.

The foundational types of data governance models

Organizations implement data governance through structured models that define authority, decision rights, and operational workflows. Understanding each model’s core characteristics helps organizations align governance with their strategic objectives.

Let’s explore these types in detail:

Centralized data governance model


Centralized data governance places a single core team in charge of all decisions. The team sets the organization-wide strategy and defines data policies. They monitor compliance and maintain data quality through unified oversight.

Key characteristics include:

  • Single governance office or Chief Data Officer controls all decisions
  • Uniform policies enforced enterprise-wide without exceptions
  • Top-down communication of standards and requirements
  • Centralized monitoring of compliance and data quality metrics

Pros and cons of centralized data governance model


Pros

Cons

Centralized rule setting keeps business units aligned.

Since every decision goes through a single team, the process slows.

Naming conventions, metadata, and data models are centrally adopted, helping teams move beyond mismatched definitions.

A central team might not fully understand the needs of every business unit.

One team is accountable for ensuring data quality, making it easier to trust data.

Other practitioners might feel less responsible, as only the central team makes a decision.

A unified audit trail lets the team manage compliance better.

Teams use unauthorized datasets or tools when governance becomes rigid.

Real-life example of centralized governance: Georgia-Pacific uses a centralized data governance model with a single squad controlling how product data is created, checked, and approved. This central team includes data stewards and platform owners who all follow the same rules.

Decentralized data governance model


In a decentralized data governance model, each team or business unit makes its own decisions about how to manage its data. Instead of relying on a central team for approvals, domains set their own policies, quality checks, access rules, and documentation standards.

This model works well in modern organizations where product teams own their data end-to-end.

Key characteristics include:

  • Business units develop independent governance programs
  • Domain-specific policies tailored to operational contexts
  • Local data stewards make decisions close to data sources
  • Faster adaptation to business changes and market opportunities

Pros and cons of a decentralized data governance model


Pros

Cons

Each domain shapes governance around its own needs.

When every team works independently, it becomes harder to discover what data exists across the organization.

Domain experts who understand the data fix issues and update rules, leading to faster decisions.

The differences in business meaning create confusion and make cross-domain projects complex.

When teams control their own standards and metrics, they take governance more seriously.

Some teams may excel at governance. Others may ignore it due to a lack of skills or resources.

Teams invest in the rules that help them deliver value, skipping those that slow them down.

Inconsistent schemas and standards make it harder to build enterprise reporting and AI models.

Many organizations that adopt a data mesh start with decentralized governance. However, eventually they add some level of central coordination to keep data interoperable, maintain shared standards, and avoid drift across domains.

Federated data governance model


Federated data governance combines central control with team-level freedom. A central team sets company-wide rules and policies that everyone must follow. At the same time, individual teams manage their own data using these shared rules, based on what works best for their specific needs.

Key characteristics include:

  • Central council defines high-level policies and standards
  • Business units implement guidelines within their domains
  • Coordination mechanisms ensure alignment across teams
  • Shared accountability between central and domain governance

Here’s an overview of how federated data governance works:

  • What the central team does: They decide what counts as sensitive data, define basic quality standards, and set security requirements. They also provide shared tools like data catalogs, glossaries, and tracking systems that all teams use.
  • What individual teams do: Each business team assigns people to own and manage their data. They document their data using the shared rules, add extra quality checks if needed, and control who can access their data within the company’s security setup.
  • How teams stay aligned: Teams meet regularly to share updates and coordinate changes to shared data. Team members also join central governance meetings so their feedback shapes company-wide rules.

Pros and cons of a federated data governance model


Pros

Cons

The central team controls security and compliance, while teams work fast without waiting for approvals.

Teams and the central group must stay in sync, or conflicts can happen.

Each team manages its own data, so governance does not become a bottleneck as the company grows.

Some teams follow good governance, others may fall behind.

The people who know the data best make the daily decisions.

Teams may build the same solutions again if they do not share properly.

Teams feel involved, so they follow governance rules more willingly.

It requires strong data platforms to manage shared rules and visibility.

Porto, a large insurance and banking company in Brazil, became 40% more efficient with a federated data governance mode and automated tools. Each team became responsible for the quality and understanding of their own data, while the central team kept overall control. The computerized tools assigned data owners, added business details, and checked compliance for over 1 million data assets.

Similarly, Brainly, an education app popular in 35 countries, brought more structure to their governance without taking away team freedom. In a federated data governance model, the central team sets standard rules for finding, documenting, and checking data quality. Each team followed these rules in its own way, eliminating silos and making cross-team collaboration easier.

Data Governance implementation approaches

Organizations layer additional approaches on top of centralized, decentralized, or federated models to make governance more effective.

Adaptive governance


Adaptive governance builds flexible systems that respond to change fast. Teams use principles and guardrails rather than rigid rules, updating practices as new risks, laws, or opportunities appear. Ideal for fast-moving industries and AI driven environments.

Agile governance


Agile data governance applies agile methods to governance. Small cross-functional teams work in short cycles, focus on high-impact tasks, and improve based on user feedback. Delivers quick wins and suits organizations launching or expanding governance programs.

Non-Invasive governance


Non-invasive data governance integrates governance into existing tools and workflows so teams don’t change how they work. Governance operates in the background through catalogs, BI integrations, and metadata. This drives high adoption and low friction.

Combining approaches with governance models


Organizations often blend models and approaches to fit business needs:

  • Agile + centralized: A central team manages compliance through fast governance sprints.
  • Adaptive + centralized: Strong strategic direction with rapid adjustments to market and regulatory change.
  • Decentralized + non-invasive: Teams govern data naturally inside their daily tools.
  • Decentralized + adaptive: Domains set and evolve their own rules in dynamic environments.
  • Federated + agile: Domains operate independently while cross-functional teams improve governance in short cycles.
  • Federated + non-invasive: Central policies, local execution, and shared tools drive broad adoption.
  • Hybrid by data sensitivity: Centralize governance for high-risk data and decentralize for low-risk data to balance control and speed.

How to choose the right governance model for your organization

1. Assess your data governance maturity


  • Review what your organization does well and where it needs to improve.
  • Match the model to your maturity level:
    • Early stage: Start with centralized governance. Build early wins, set standards, and train teams.
    • Growing maturity: Shift to a federated model as teams gain competency.
    • High maturity: Selectively decentralize once teams can independently manage data without creating inconsistency.

Quickly assess your organization’s data governance maturity

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2. Evaluate industry and regulatory requirements


  • Identify compliance rules that affect your governance structure.
  • If you operate in banking, healthcare, or government, enforce strong central oversight to meet strict audit and security norms with regulations like GDPR, HIPAA, or SOX.
  • When possible, apply a federated approach where:
    • A central team defines compliance rules.
    • Domains apply them in day to day operations.

Research shows 61% of organizations are evolving their operating models due to AI technologies, with governance structures adapting to new compliance demands and use cases.

Data governance models decision tree

Data governance models decision tree. Source: Atlan.

3. Review your technology stack


  • Check whether your tools support automation, lineage, and metadata tracking.
  • Adopt centralized or federated models when your platform can coordinate distributed ownership at scale.
  • If you use disconnected or open source tools, set at least:
    • A shared data catalog
    • Common metadata standards
  • Upgrade your infrastructure if your tools limit your governance approach.

4. Match the model to your resource capacity


The data governance market growing from $4.44B to $18.07B by 2032 reflects increasing investment in governance capabilities, tools, and organizational structures across industries.

  • Centralized: Staff a strong core team, invest in training, and maintain dedicated tools.
  • Decentralized: Distribute responsibilities but increase training and alignment efforts to avoid inconsistency.
  • Federated: Combine a small central team with domain stewards. Use automation to let the center support many domains efficiently.

5. Align with business needs and culture


  • Choose centralized or strong federated governance when compliance and auditability are priorities.
  • Use federated governance for multi domain operations where teams manage data closest to its source.
  • For AI and ML initiatives, apply central principles and local execution through a federated model.
  • Confirm cultural readiness.
    • Centralized works when teams accept top down rules.
    • Federated succeeds only when teams take ownership and follow shared standards.

Many organizations adopt hybrid approaches that combine model characteristics, such as centralized governance for sensitive data with decentralized management for operational datasets.

How data governance models adapt with AI

AI turns traditional governance from a static structure into a dynamic, automated system. The foundation stays the same, but the operating model gains new guardrails, context, and automation.

Shift in focus


Traditional governance manages critical data elements. AI governance manages the entire use case. Teams must assess data quality, ethical risk, legal exposure, and business value before deploying AI.

Stronger foundations


AI succeeds only when data quality and discoverability are solid. Data and AI governance must operate as one system, since weak data foundations create AI failures.

Guardrails with flexibility


AI’s complexity demands clear boundaries. Governance must define what is safe, legal, and ethical, then let teams innovate rapidly within those limits while maintaining explainability.

Catalog as a context layer


The data catalog shifts from documentation to a live context engine for AI. It supplies the structured meaning models need to answer natural language questions accurately.

Automation and maturity


AI pushes governance toward full automation:

  • Crawl: Understand what data and models exist.
  • Walk: Automate key steps.
  • Run: Achieve proactive, near-invisible governance built into every workflow.

Manual processes cannot scale to AI. Automated governance becomes essential.

Real stories from real customers: Governance in action

“Atlan is much more than a catalog of catalogs. It’s more of a context operating system… Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models.” — Sridher Arumugham, Chief Data and Analytics Officer, Workday

Governance is an active semantic layer not a passive documentation

Watch Workday’s story →

53 % less engineering workload and 20 % higher data-user satisfaction

“Kiwi.com has transformed its data governance by consolidating thousands of data assets into 58 discoverable data products using Atlan. ‘Atlan reduced our central engineering workload by 53 % and improved data user satisfaction by 20 %,’ Kiwi.com shared. Atlan’s intuitive interface streamlines access to essential information like ownership, contracts, and data quality issues, driving efficient governance across teams.”

Data Team

Kiwi.com

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One trusted home for every KPI and dashboard

“Contentsquare relies on Atlan to power its data governance and support Business Intelligence efforts. Otavio Leite Bastos, Global Data Governance Lead, explained, ‘Atlan is the home for every KPI and dashboard, making data simple and trustworthy.’ With Atlan’s integration with Monte Carlo, Contentsquare has improved data quality communication across stakeholders, ensuring effective governance across their entire data estate.”

Otavio Leite Bastos, Global Data Governance Lead

Contentsquare

🎧 Listen to podcast: Contentsquare’s Data Renaissance with Atlan

How modern platforms streamline governance model implementation

Organizations struggle to operationalize governance no matter which model they choose. Centralized structures stall under manual policy enforcement, forcing teams to spend time on administration instead of strategy. Decentralized models lose visibility across domains, making it hard to understand the enterprise data landscape. Federated models slow down due to coordination gaps between central and domain teams.

Modern data catalogs with active metadata solve these problems through automation and intelligent orchestration. Atlan discovers assets, maps relationships, and applies policies automatically based on metadata attributes. The platform adapts to any governance model.

Organizations using Atlan’s active governance reduce administrative work by 40 percent. Automation and AI assisted documentation remove friction, enabling all teams to adopt governance without needing deep technical expertise. Governance shifts from a blocker to an accelerator for data initiatives.

Here is what Atlan delivers for each model:

  • Centralized governance: One team governs the entire landscape with automated enforcement, unified tracking, and instant scalability.
  • Decentralized governance: Domains manage their own data with automated tagging, ownership, and quality checks that keep them compliant by default.
  • Federated governance: Central policies guide local execution. Shared glossaries create common language, automated syncing aligns catalogs, and role based access protects data without slowing teams.

Atlan’s active metadata engine applies governance inside the tools people already use, improving adoption and compliance while increasing efficiency through automated ownership and lineage based impact analysis.

See how Atlan helps organizations implement governance models that scale with business growth and align with strategic goals.

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FAQs about data governance models

1. What is the difference between centralized and federated data governance?


Centralized governance concentrates all decision-making authority in a single central team that defines and enforces policies uniformly across the organization. Federated governance combines central policy guidance with domain-level implementation autonomy, allowing business units to adapt practices to local needs. The central body in federated models provides frameworks and oversight while business units maintain operational flexibility, balancing consistency with agility.

2. Can organizations combine multiple governance models?


Yes, many organizations adopt hybrid approaches that combine model characteristics strategically. For example, highly sensitive data like personal information might follow centralized governance for compliance and risk management, while operational data uses decentralized management for flexibility. Organizations can also layer agile or adaptive practices onto centralized, decentralized, or federated foundations to increase responsiveness to business changes.

3. How long does it take to implement a new governance model?


Implementation timelines vary significantly based on organizational size, maturity level, and chosen model complexity. Simple centralized structures in small organizations might establish foundational policies within three to six months with dedicated resources. Complex federated implementations in large enterprises typically require 12 to 18 months for full deployment across multiple domains and business units. Successful implementations focus on iterative progress with quick wins rather than attempting complete transformation simultaneously.

4. What happens when people create new product data inside a tool in a centralized data governance system?


The central team would stop and remind everyone that all product data must come from the master data system. It keeps all teams aligned, preventing people from creating their own versions of data.

5. How does the central team ensure data quality?


The central team thoroughly checks every data update for accuracy before it enters the master system. If something looks wrong, it’s sent back for correction.

For Georgia Pacific, if a product weighs outside the allowed range, it’s either sent back or a new GTIN is created in accordance with GS1 standards. It helps reduce errors and maintain a trusted version of product data across the business.

6. Do regulated industries prefer a centralized model?


To some extent, financial services and healthcare might benefit from a centralized model to establish consistent and standardized policies. However, if they operate across regions and regulations, local execution with central standards would be more advantageous. In such situations, a federated data governance model would be a more appropriate choice.

7. Is a decentralized data governance model suitable for organizations with low data governance maturity?


It depends on other factors, like how departments operate and how diverse they are. If different departments have unique mission statements, it makes sense to use decentralized governance. While these organizations might have a small central team that provides basic standards.

8. Does decentralized governance scale better than centralized?


Decentralized governance usually scales better than centralized governance. At scale, centralized governance might slow teams and create backlogs. However, it becomes easier to operate on data when the team aligns on a standard business meaning.

9. Is decentralized data governance more cost-efficient than centralized governance?


Centralized models concentrate governance spending, and decentralized models spread it out. However, the latter isn’t cheaper in any way. Decentralized models still require a strong central office and local stewards, implying the use of both local and central resources.

10. In a federated data governance model, what should the central team control, and what should other data domains manage?


Data domains should be tasked to drive data value while ensuring the quality and accessibility of data. Central data organization should establish foundational principles while enforcing standards. The strategy for distributing data capabilities and resources should be aligned with an organization’s maturity level.

11. How does an organization’s data maturity level affect how governance responsibilities are distributed?


Organizations with low data maturity often have very few data experts and a weak data culture. This slows down results and leads to uneven use of data rules, especially if teams handle accountability on their own. In these cases, it works better to start with a strong central data governance Center of Excellence (CoE) and slowly pass responsibilities to business teams over time.

This setup becomes more challenging in highly regulated industries like finance, where strict laws require tight control. In such situations, companies should carefully balance central rules with team ownership and shape their approach based on their industry and market needs.

12. What are common mistakes when implementing governance models?


Organizations frequently fail by choosing models based on theoretical appeal rather than practical organizational capabilities and constraints. Other common mistakes include inadequate stakeholder engagement across business units, insufficient investment in training and enabling tools, and attempting to replicate another company’s model without adaptation to specific context. Research shows 60% of organizations fail to realize AI value without solid governance, emphasizing the importance of thoughtful implementation. Successful implementations require continuous assessment and willingness to adjust the model as organizational needs evolve.

13. How does Atlan support different governance models?


Atlan’s platform adapts to centralized, decentralized, and federated governance structures through flexible metadata management and automation capabilities. The system provides centralized teams with unified policy enforcement and enterprise-wide visibility, decentralized units with autonomous control and shared metadata layers, and federated organizations with coordinated frameworks that preserve domain autonomy while ensuring enterprise alignment. Active metadata capabilities enable automated policy enforcement, AI-assisted documentation, and intelligent workflows that reduce administrative burden regardless of organizational structure.

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