Centralised vs. Federated vs. Hybrid: Choosing the Right Data Governance Model

Data governance is often overlooked until something goes wrong. We regularly see firms prioritise it midway through transformation programmes, when cracks start to show and projects stall.

But done right, data governance isn’t just about compliance. It’s the foundation for scalable operations, reliable reporting, and confident decision-making.

An important consideration is how to implement data governance within your organisation. Should you centralise control, decentralise responsibility, or aim for a hybrid? In this article, we explore the pros and cons of each and what to consider before committing.

Which Model is Best?

There’s no one-size-fits-all approach to the operating model for data governance but choosing the right approach early can save a lot of headaches later on. Typically, models fall into the categories of centralised, federated or some “hybrid” of both. Each comes with trade-offs in control, agility, and scalability. The key is finding the right balance for your organisation’s size, maturity, and strategic priorities.

Federated Model

A federated model decentralises governance, giving individual business units control over their own data while maintaining a level of oversight at an organisational level. This is a good fit for firms with multiple departments or geographies, each with specific data needs and systems.

Pros:

• Flexibility: Each team can shape data governance to suit their specific needs and processes.
• Agility: Teams can innovate and adapt faster without waiting for central approval.
• Scalability: The framework can grow with new data domains or business units without major changes.

Cons:

• Increased Complexity: Multiple teams managing their own data can lead to chaos and lack of coordination.
• Risk of Data Silos: Departments may withhold data, limiting business-wide insights and visibility.
• Cultural Resistance: Teams used to independent management may resist engaging with central standards or coordinated processes making consistent governance harder to achieve.

Centralised Model

In a centralised data governance model, a central governing body, such as a Data Governance Council, oversees all data management activities. This model is more rigid, ensuring consistency and clear accountability across the organisation by creating a single framework for all data governance processes.

Pros:

• Consistent Standards: Centralised governance ensures consistent application of data standards across the organisation.
• Simplified Compliance: A centralised model makes it easier to stay compliant by applying regulatory standards consistently across the organisation, reducing the risk of gaps, duplication, or costly breaches.
• Holistic Oversight: With all data governed under one roof, it’s typically easier to get a complete view of your organisation’s data assets, leading to better reporting, risk management, and decision-making.

Cons:

• Lack of Flexibility: Centralised systems may be slow to adapt to specific business unit needs.
• Limited Context: A centralised body may lack on-the-ground insight without regular input from SMEs.
• Resource Bottlenecks: Centralising governance can lead to stress on resources, especially if the team is understaffed.

Hybrid Model

In our experience, many asset managers find that a hybrid model strikes the right balance combining the control and consistency of a centralised approach with the adaptability of a federated one.

This model typically involves a central team setting overarching standards, tools, and governance principles, while business units retain responsibility for applying them to their specific data sets. Done well, it enables both strategic oversight and local accountability.

Hybrid models can work particularly well in firms that are scaling, operating across regions, or managing multiple lines of business — where a one-size-fits-all approach doesn’t reflect operational reality.

The key challenge? Coordination. Hybrid governance requires clearly defined roles, open communication, and shared commitment to enterprise-wide standards – or it risks becoming inconsistent by default.

What to Consider When it Comes to Implementation

Choosing the right model is just the start. Implementation is where many firms fall down, especially when governance is treated as a one-off project instead of an evolving business function.

Based on what we’ve seen work well (and not so well), here are five things to get right from the outset:

Stakeholder Buy-In

Engage stakeholders across all business units early. Successful governance demands collaboration, transparency, and buy-in from leaders as well as those who’ll be responsible for governance day to day. It should begin by assessing current pain points and aligning the governance framework with strategic business goals.

Data Maturity

Assess your organisation’s data maturity. How well do you understand your data, and how mature is your data management framework? If your data landscape is fragmented with limited controls, you may need to start with a centralised governance approach and gradually move towards federated governance as the organisation matures.

Technology Fit

Without the right tools, it’s impossible to ensure that data is properly managed, tracked, and protected. Ensure that your data management, integration, and reporting tools align with your governance model and that your tech stack can automate and embed governance to support long-term scalability.

Compliance & Risk Management

Governance is about protection and it’s important not to lose sight of that. Make sure your model includes clear ways to monitor usage, manage risk, and stay audit-ready to help prevent potential violations and make audits smoother.

People & Expertise

Finally, good governance needs the right people in the right places. Whether it’s a central team or business-aligned SME’s, make sure you’ve got the capability to run and refine your model over time.

Getting It Right from the Start

Governance isn’t a side project; it’s the foundation of any successful data strategy. Whether you’re centralising, federating, or building something in between, the key is getting the structure, people, and tools aligned from the start.

At Liqueo, we help asset managers put practical, scalable governance models in place – tailored to the way they actually work. If you’re reassessing your data governance model or struggling to make yours stick, we’d love to help.