Data governance operating models

In today’s business world, data is extremely valuable. Data governance refers to the ways in which your company sets data-related policies, defines roles and responsibilities in relation to data management, and ensures data is handled appropriately—on an individual level, departmental level, and right across the organization.

To manage, harness, and safeguard data effectively, you need a robust framework. This framework is called a data governance operating model. There are several types of models, but there’s no one-size-fits-all solution. Your model needs to be tailored to your specific organization.

So, how do you choose the right model? And how do you tailor it to your company’s needs and goals? First, you need to know your options. Buckle up, we’re about to go deep into the world of data governance operating models. Whether you’re just beginning your journey or looking to optimize your current practices, this information is sure to be a valuable resource in your quest for efficient and effective data management.

What is a data governance operating model?

The purpose of a data governance operating model is to enable a business to run smoothly and efficiently. It’s a lot like the human resources department in a company, only instead of defining rules to ensure people behave appropriately, it defines rules to ensure data is handled appropriately. An effective data governance operating model establishes company-wide processes, standards, roles, and metrics for data management.

You could think of it as a guiding force. A data governance operating model sets procedures and standards that ensure data is always handled in the same ways—harmoniously and in line with the company’s policies—no matter which employees or departments are involved.

Why is conformity so important in data management? Because data isn’t just data, it’s a crucial element of informed decision-making. If it’s not managed with clarity and consistency, people get confused, details get missed, balls get dropped, and the potential for compliance breaches goes through the roof.

Crafting a robust data governance operating model is of the utmost importance. It’s not an ancillary task; it’s an integral part of your business strategy.

Different types of data governance operating models

Different organizations have different facets, different needs and different goals. Therefore, they require different data governance operating models. Some data governance operating models focus on centralized control, while others advocate for a more collaborative, decentralized approach.

How can you tell which model is best for your company? It depends on:

  • the size of the organization,
  • industry sector,
  • company culture,
  • complexity of the data, and
  • overall business strategy.
Different types of data governance operating models

Different types of data governance operating models

1. Centralized data governance

A centralized data governance model is like an orchestra. The conductor is the data governance lead. They direct all the musicians—business analysts, data stewards, data architects, and data analysts—to deliver a harmonious performance. This model is typically initiated to support a specific project. While it acknowledges the need for business experts, they generally only participate as needed.

A centralized data governance model’s strength lies in its clear lines of ownership and accountability—everything falls under the watchful eye of the data governance lead. As you can imagine, launching this model across an entire organization would require a lot of changes and have a serious impact. It’s generally more suitable for a single department rather than as an enterprise-wide approach.

Centralized data governance operating model


  • Formal data governance position at an executive level.
  • Data governance steering committee reports directly to executive.
  • Single data governance lead means more effective decision-making.
  • One place for all data governance needs.
  • Easier to manage by data type.


  • Significantly impacts the organization.
  • New roles will most likely require approval from HR.
  • Formal separation of business and technical roles.

2. Decentralized data governance

If a centralized data governance model is an orchestra, then a decentralized model is a jazz ensemble. It uses organic improvisation to address data inconsistencies and challenges as they arise. This grassroots approach often originates within a team of employees and requires executive backing to maintain momentum and implement ideas.

The downside is that the employees involved have other primary roles and no formal data governance duties. This can make it difficult to sustain data governance when other tasks pull them away. Committees in this model often struggle with decision-making, creating strategies, and implementing change. Moreover, without a single person in charge of data governance, enforcing roles and accountability is challenging.

Decentralized data governance operating model


  • Relatively flat organization.
  • Informal data governance bodies.
  • Relatively quick to establish and implement.


  • Consensus discussions tend to take longer than with a centralized model.
  • Many participants, which can compromise governance bodies.
  • May be difficult to sustain over time.
  • Provides the least value.
  • Difficult to coordinate.
  • Business as usual can interrupt data governance.
  • Issues around data co-ownership and accountability.

3. Hybrid data governance

A hybrid data governance model combines the best aspects of both centralized and decentralized models, while reducing their limitations. It features a centralized data governance office complemented by a cross-functional, decentralized working group.

The office may comprise various roles, such as a data governance lead, program manager or business analyst, and possibly, a data quality team. The decentralized working group consists of experts from different business units, as well as IT representatives. This offers flexibility and enables direct involvement of the people who are impacted by the data.

Accountability is embedded in the structure of a hybrid data governance model, with the steering committee ensuring decisions are enforced and the working group participants reporting up through corresponding steering-committee-led business lines.

Hybrid data governance operating model


  • Centralized structure for establishing appropriate direction and tone at the top.
  • Formal data governance lead serving as a single point of contact and accountability.
  • Data governance lead position is a full-time, dedicated role, so data governance gets the attention it deserves.
  • Working groups with broad membership for facilitating collaboration and consensus building.
  • Potentially an easier model to implement initially and sustain over time.
  • Pushes down decision-making.
  • Ability to focus on specific data entities.
  • Issues are resolved without requiring the whole team to participate.


  • Data governance lead position is a full-time, dedicated role.
  • Working group dynamics may require prioritization of conflicting business requirements.
  • Too many layers.

4. Federated data governance

A federated data governance model is like a franchise system. Regional or divisional data governance offices execute the program within their areas. To maintain consistency across these offices, an enterprise data governance office ensures collaboration and uniformity.

This model enables regional offices to focus on the data types that are critical to them, irrespective of their relevance to the entire organization. However, potential conflicts between divisional and enterprise priorities may arise, so an effective conflict resolution process is crucial.

Federated data governance operating model


  • Centralized enterprise strategy with decentralized execution and implementation.
  • Enterprise data governance lead serves as a single point of contact and accountability.
  • “Federated” data governance practices per line of business (LOB) to empower divisions with differing requirements.
  • Potentially an easier model to implement and sustain over time.
  • Pushes down decision-making.
  • Ability to focus on specific data entities, divisional challenges, or regional priorities.
  • Issues are resolved without requiring the whole team to participate.


  • Too many layers.
  • Autonomy at the LOB level can be challenging to coordinate.
  • Difficult to find balance between LOB priorities and enterprise priorities.

5. Agile data governance

Agile data governance is an emerging operating model that resembles a vibrant marketplace. It’s an approach in which everyone contributes to the company-wide data resource, supported by data catalogs and tools equipped with machine learning and artificial intelligence.

As this model evolves, it promises faster adoption, better alignment with business objectives, and the emergence of new capabilities. However, this empowerment also comes with shared responsibilities, including contributing to data knowledge, adhering to data protection guidelines, and ensuring approved data usage.

The agile data governance model encourages a supportive framework, pushing policies as close to the end user as possible. It fosters collaboration and positions the data governance office as a shared service that provides guidelines in support of business objectives.

Technology, especially data catalogs, plays a pivotal role in ensuring guidelines are followed and contributions are tracked. This approach resembles a federated data governance model, with decision-making involving cross-divisional and cross-functional groups, multiple working groups, and a data steward community. Accountability at senior levels and the identification of who is responsible for data are integral parts of this model.

Agile data governance operating model


  • Focuses on providing support to staff who work with data.
  • Staff are empowered, but also expected to contribute to the corpus of data knowledge.
  • Staff follow guidelines rather than rigid, prescriptive procedures.
  • Supports data end users.
  • Ensures investment in communication and training on how to deal with data needs (not just high-level statements of what needs to be done).


  • This modern approach requires tools like a data catalog.
  • For organizations used to traditional governance structures, transitioning to an agile model can be a complex and challenging process.
  • This model requires a significant cultural shift within the organization.

Important considerations for defining data governance operating models

To choose the appropriate data governance operating model, you need an in-depth understanding of your organization’s culture. If your chosen model doesn’t align with the company culture from the outset, implementation will be challenging. Make sure you consider the company’s values, leadership style, and communication culture. You also need to decide how centralized or decentralized you want the model to be. And it’s crucial to align decision-making entities with the existing organizational structure, which leverages their authority to foster accountability throughout the model.

While shaping your operating model, bear in mind the principle of thinking globally and acting locally—conceive your future state but focus on what is immediately attainable. You will adapt the model over time. At LACO, we dedicate considerable time to pinpointing the best operating model for each client. Occasionally, this means scaling back the future-state model until the organization achieves a certain level of maturity.

It’s important to note that no two companies will have identical models due to their different organizational cultures. This means you can’t simply copy a model from another company, not even one in the same industry.

To get started, check out this list of actionable steps that can help you enhance your data governance operating model.

  • Understand your business needs: Every data governance operating model should be grounded in the strategic priorities of the business. Understand the needs of your stakeholders and build your model to meet these needs.
  • Create a data governance council: This council should comprise representatives from different business units. They will be responsible for making decisions regarding data policies and standards.
  • Define roles and responsibilities: Clearly outline the roles and responsibilities of everyone involved in data governance. This will help to avoid confusion and ensure accountability.
  • Manage the change: Data governance is an evolving field, so it’s vital to keep your team up to date with the latest trends and best practices. Change management is also a crucial part of successful implementation, as well as providing ongoing education and training.


Remember: people are a critical element of every data governance operating model. Assess the people and roles within the model you choose carefully. Disregard titles and focus on assigning the right people to the right roles at the appropriate level of the organization. Ensure they’re good decision-makers and that their managers are happy for them to commit the necessary time. And if you can’t initially fill all roles with the ideal people, focus on the most important roles.

With a solid data governance operating model, your organization is better equipped to handle data challenges, make informed decisions based on accurate data, and drive growth through the effective utilization of data. It’s well worth the effort. And don’t forget, this is an ongoing process. It will require continuous monitoring and adjustments to sustain it over time. You’ll scale it as you go and watch it mature. Always aim for progress rather than perfection.

Need help setting up a data governance operating model?

Mathias Vercauteren, Senior Data Governance Consultant at LACO

Mathias Vercauteren

Senior Data Governance Consultant at LACO

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