Data Governance Models: Centralized, Decentralized & Federated Explained

Updated May 14th, 2024

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A data governance model helps you achieve your data governance goals by lending structure to your efforts—establishing processes, roles, responsibilities, and more. This is essential to understanding who decides the rules, who enforces them (and how), and who communicates the standards (and how).

Read on to learn about the most common data governance models, how to combine them, and how to pick the right one for your organization’s needs and management style.



Table of Contents

  1. What are data governance models?
  2. What about adaptive, agile, and non-invasive data governance models?
  3. Best practices
  4. Summing it up
  5. Related reads

What are data governance models?

A data governance model is an implementation plan for your data governance initiative. Rather than describing abstract principles, data governance models aim to show how to achieve data governance objectives through processes, team structure, roles and responsibilities, and technology.

The three most common data governance models are centralized, decentralized, and federated.

Each model has unique benefits and challenges that make it appropriate for different organizational needs. So, let’s explore them further.

1. Centralized data governance: Concept, benefits, and challenges


Centralized data governance involves a core group or committee making data governance decisions. The group is responsible for setting and implementing the overall data governance strategy, policies, and standards, as well as monitoring compliance and ensuring data quality.

The benefits of this top-down model include:

  • Greater consistency in data governance rules and policies
  • Better interoperability across your data estate
  • Improved trust in data

For instance, American manufacturing company Georgia-Pacific adopted the centralized model. As a result, the company could streamline communications across countries and industries, measure quality uniformly, and optimize supply chain costs.

However, centralized data governance can often be bureaucratic and rigid. The mandates from the governing body can’t consider every team’s unique use cases. Meanwhile, data practitioners who aren’t a part of the core team might feel less ownership and accountability for data governance.

The model’s rigidity might also cause data practitioners to seek a workaround instead of being bogged down by bureaucracy, which can go against your data privacy, security, and compliance measures.

2. Decentralized data governance: Concept, benefits, and challenges


Decentralized data governance empowers individual teams to manage their governance initiatives. This allows teams from each data domain to customize data governance to address their unique needs and requirements.

The benefits include:

  • Flexible data governance practices
  • The ability to adjust and pivot policies according to business requirements
  • A greater sense of ownership and accountability

However, this flexibility has its downsides. If each team governs data based on its select rules, achieving data interoperability becomes more challenging. In larger organizations, the decentralized data governance model can result in isolated data silos, limited data asset visibility, and inconsistent data policies and standards.

The decentralized model tends to represent the lowest level of maturity, and many organizations transition to the federated or centralized models as their programs progress

3. Federated data governance: Concept, benefits, and challenges


A federated data governance model integrates centralized and decentralized approaches, balancing central guidance with domain autonomy:

  • A central body creates a company-wide data governance policy, i.e., global data governance
  • Each data domain has autonomy in implementing and managing data governance practices according to their use cases

The federated model helps you collaborate and share resources without sacrificing local control. Let’s consider two examples — Porto and Brainly.

Brazil-based insurance and banking leader Porto wanted to modernize and improve its data governance. The goal was to empower data domains to develop data pipelines autonomously and be responsible for data literacy around these products. Realizing this goal and switching to federated data governance helped Porto increase the efficiency of its data governance team by 40%.

Another example is Brainly, the world’s most popular education app. Brainly went from decentralized to federated data governance to reduce data chaos, overcome data silos, assign ownership, and improve cross-domain collaboration.


Besides the centralized, decentralized, and federated models, organizations also explore adaptive, agile, and non-invasive approaches to data governance. Let’s examine these models.

What about adaptive, agile, and non-invasive data governance models?

Three more commonly discussed data governance models include:

  • Adaptive data governance
  • Agile data governance
  • Non-invasive data governance

Let’s quickly explore these concepts before understanding how they can be connected to centralized, decentralized, and federated data governance.

Adaptive data governance


Adaptive data governance focuses on flexibility, proactiveness, and adaptability in response to an organization’s changing needs and dynamics.

With adaptive data governance, your organization’s governance framework stays relevant and effective in the face of new technologies, regulations, market trends, and more.

Read more → Adaptive data governance: Principles, benefits & implementation factors

Agile data governance


In an agile data governance model, small cross-functional teams spearhead bottoms-up data initiatives within a framework that enables security, quality, and compliance.

This model ensures faster time-to-value for your projects, faster time-to-market, better collaboration and less friction between teams.

Read more → Agile data governance 101

Non-invasive data governance


Non-invasive data governance focuses on achieving governance goals through lightweight, collaborative, and user-friendly approaches that integrate seamlessly into existing business operations. As a result, there’s minimal disruption to daily tasks.

Read more → Non-invasive data governance: Why should you care?

Combining data governance models: How does it work?


Organizations can combine centralized, decentralized, and federated models with one (or more) of the above approaches for greater benefits. Let’s see how:

  • Agile and centralized data governance: A centralized data governance authority can oversee the overall risk management and regulatory compliance. They can use agile, iterative improvement cycles to conduct regular data governance sprints that address emerging risks and compliance updates.
  • Adaptive and centralized data governance: An adaptive governance model allows you to respond quickly to market trends and customer data insights. Moreover, a centralized governance body handles the overarching data strategy and policy alignment and liaises with the stakeholders responsible for driving the adaptive model.
  • Decentralized and non-invasive data governance: This approach requires integrating data governance practices within daily workflows with minimal disruption. It emphasizes empowering the people handling data to play a crucial role in data governance.
  • Decentralized and adaptive data governance: Each team can follow its data governance style based on its business context. Every team is autonomous and responsible for adjusting its approach to data governance to react to business opportunities and risks.
  • Federated and agile data governance: You can adopt a federated governance model for regional data autonomy. Additionally, you can implement the agile method by setting up cross-functional teams that iteratively establish and optimize your data governance initiative. This combination ensures local flexibility while fostering collaboration and knowledge-sharing across the organization.
  • Federated and non-invasive data governance: In this combination, a centralized authority would guide data governance and management. However, each team would implement data governance in a way that minimally disrupts existing workflows and processes.

You can also adopt a hybrid approach by combining centralized and decentralized models based on specific data domains or business units. For instance, critical (PII) data can follow a centralized model, while less sensitive data can use the decentralized governance model.

Since there are many ways to combine these approaches, you should carefully weigh each model’s size, stage, industry, tools, and data governance maturity before deciding which to use.


Data governance models: Best practices to choose the right model

Choosing a suitable model for your organization depends on several important factors, such as:

  • Data governance maturity
  • Industry
  • Tech stack
  • Resources
  • Use cases and requirements

Let’s dig deeper.

Data governance maturity


Conducting a data governance maturity assessment helps identify gaps, strengths, and improvement opportunities. This is crucial to understand which data governance model is ideal for you.

A mature organization with well-established governance practices may opt for a federated model. A mature data governance program implies greater autonomy for data domains and teams. Everyone in your organization would have strong data literacy and an in-depth understanding of data governance.

Meanwhile, a startup just beginning its data governance journey can use a centralized model. This model provides clear governance oversight and control as it establishes foundational policies and standards.

Industry


The regulations and laws in your organization’s industry will also help you decide on the right data governance model. For example, for verticals like financial services and healthcare, you may need a centralized model to establish consistent, standardized data governance policies and ensure compliance.

Meanwhile, an e-commerce platform could adopt a federated model due to its global presence and varying data privacy laws in different regions. This model allows regional teams to tailor governance practices while meeting local regulatory demands.

Tech stack


Your organization’s data tools and applications affect your choice of governance models. For example, centralized and federated models can work if you already use a data governance platform with active metadata management and automated policy enforcement capabilities.

However, if your tech stack is more open-source, you may lean towards a more decentralized approach, as these tools offer flexibility and extensibility.

Resources


Each data governance model requires some overhead to implement and coordinate. Some data governance models (like centralized data governance) require substantial investments in technology, training, and personnel, while others (like decentralized) may be more resource-efficient.

Use cases and requirements


Your organization’s data governance use cases will influence your choice of data governance model. For example, an agile or adaptive data governance model may be more suitable if the organization prioritizes rapid adaptation to market changes.

Meanwhile, if you belong to a heavily regulated industry, models that offer robust controls, audit trails, and compliance monitoring capabilities are a viable alternative.

Lastly, a federated model might be ideal if your data ecosystem is complex and diverse since each domain or business unit can manage its data governance initiatives independently. Governance activities (data quality management, metadata management, access, etc.) can be handled closer to the data sources, making your data governance approach pragmatic and effective.

Also, read → Active data governance: What it is and how to get started


Summing it up

Data governance models provide a blueprint for implementing data governance. Organizations can adopt centralized, decentralized, federated, or hybrid models depending on factors like data governance maturity, industry compliance, tech stack, resource availability, and specific use cases.

Combining different models, such as federated with non-invasive or centralized with agile, can lead to optimal results by leveraging the strengths of each approach while mitigating their limitations.

Choose a data governance model that closely aligns with your business objectives and data and analytics priorities. This ensures that your organization’s data governance efforts are targeted, efficient, and impactful.



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