Start governing your data with these essential components.

In our rapidly evolving digital age, with a rising number of data breaches and new rules like CSRD coming into play, being able to handle accurate, secure, and trusted data has become crucial for today’s businesses. This is why companies need to get really good at data governance – that’s how they can effectively manage data as a valuable resource. But as the volume of data grows exponentially, the way we manage and govern this data becomes increasingly critical. This is where understanding the distinction between data governance and data management comes into play.

So, you’ve determined that data governance is important for your business and you’re ready to integrate it. The question now is, where do you start? This is where the seven crucial elements come in handy. These elements should be part of your action plan to govern your data effectively.

In the following sections, we will demystify the differences between data governance and data management and deep dive into the essential components of a robust data governance framework. Whether you’re a C-level executive seeking to leverage data more effectively, or simply interested in enhancing your organization’s data practices, this blog post will provide valuable insights to steer you in the right direction.

Data governance and data management: The symphony of data success

Before we begin discussing the elements to include in your data governance framework, it’s important to clarify the difference between data governance and data management. In the vast world of business data, two terms often stand out: data governance and data management. Both are crucial in the journey to effective data utilization, but they are not interchangeable. For C-level executives navigating the complexities of modern data landscapes, understanding the separation of duties between these two concepts is vital.

Difference between data governance and data management explained

While they have distinct responsibilities and objectives, data governance and data management complement each other. Data governance sets the pace and direction, while data management plays the notes, executing on the ground. Both are essential for creating a harmonious symphony of effective and efficient data use.

Consider this: Without governance, management could become disjointed, lacking clear guidelines and oversight. Conversely, without management, governance strategies and policies would remain theoretical, never quite making it into practice.

In conclusion, data governance and data management are two sides of the same coin, each carrying out separate but complementary duties. Understanding their unique roles and how they work together is key to leveraging data effectively within your organization. Remember, it’s not about choosing between data governance and data management. Instead, it’s about ensuring they work in harmony, guiding your organization toward a more informed, data-driven future. By achieving this balance, you’ll be well on your way to conducting your own successful data symphony.

The seven key components of a data governance framework

A data governance framework is your map and compass, guiding you toward effective data usage.

Navigating the world of data can be like maneuvering through a complex labyrinth. That’s where a data governance framework comes in – it’s your map and compass, guiding you toward effective data usage. Think of a data governance framework as a recipe for a successful meal. It’s a detailed list of all the ingredients (or components) you need to create an effective data governance program. Just as you would consider different elements like vegetables, proteins, and spices when cooking a meal, in data governance, you need to consider various aspects such as strategy, organization, directives, and technology. This ‘recipe’ or framework ensures that you’re not missing any crucial steps or components when setting up data governance for your business. It’s essentially your guide to making sure you’re doing everything necessary to manage, protect, and optimize your data effectively.

At LACO, we have identified seven key components to build a solid data governance framework:

  1. strategy,
  2. organization,
  3. directives,
  4. measurement,
  5. organizational change management,
  6. technology, and
  7. data and enterprise architecture.


Together, they provide a comprehensive approach to managing, protecting, and optimizing data, turning it from raw information into a strategic asset that drives informed decision-making, promotes compliance, reduces costs, and ultimately propels business success.

The seven key elements of a data governance framework

The seven building blocks of a Data Governance Framework

1. Strategy

A strategy is crucial to make everyone understand the importance of data governance and what it means for the organization as well as for themselves.

When we talk about ‘strategy’ in a data governance framework, we’re essentially talking about the game plan for managing your company’s data. It’s like a roadmap that outlines where you want to go with your data management and how you plan to get there. This includes setting goals like improving data quality, ensuring data security, or using data to make better business decisions.

The strategy allows you to document your goals, reasons for pursuing them, and how they align with the objectives and strategies of your organization. This is crucial to help everyone in your organization understand the importance of governance and what it means for both the organization and themselves as individuals. In essence, a data governance strategy is your master plan for making sure your data works for you, helping you achieve your business objectives.

2. Organization

The ‘Organization’ part focuses on the people who make data governance happen. It’s all about who does what – from the data stewards, who make sure the data is accurate and reliable, to the top-level executives, who support and promote data governance within the company. Let’s say in a hospital, there might be a person assigned as a data steward whose job is to look after patient data and make sure it’s correct and used properly.

This component also includes how data governance works in practice, also known as the operating model. This includes things like setting up data committees, determining how decisions are made, and how processes are carried out. Think of this as the groundwork for setting up a data governance organization. It also deals with rules around who owns the data and who is responsible for it.

 3. Directives

Directives include the policies, standards, and procedures that govern how data is handled. They’re the rules of the road, ensuring everyone knows what they should and shouldn’t do. For instance, a retail company might have a policy that customer data must be anonymized before being used in market research.

Sure, creating policies, processes, and standards is a big part of data governance. But these alone don’t guarantee success. They need to be connected to a strategy that’s driven by business needs. Also, there have to be people in charge who are responsible for carrying out, checking, and keeping an eye on these policies.

Additionally, there must be systems in place to measure and monitor how well the policies are working. And it’s important to think about how these policies and standards are communicated. Simply put, just having a policy doesn’t mean your data governance efforts are successful. It’s all about how these elements work together to support your business goals.

4. Measurement

Measuring data governance is like checking the health of your business. It’s about making sure your data practices are working well and helping your business grow. This involves keeping track of things like the quality of your data – is it accurate and reliable? Are you following all the rules and regulations related to your data? You might also look at whether your team is using data effectively to make decisions. By regularly checking these aspects, you can spot any issues early on and make necessary changes. In simple terms, measuring the effectiveness and efficiency of your data governance efforts helps you ensure your data is in good shape and your business is getting the most out of it.

5. Organizational change management

Data governance always involves changing how people work, which can be challenging. Organizational change management is all about preparing your team for changes related to how you handle data. It’s like coaching a sports team to adapt their game plan when rules or strategies change. This could involve training your staff on new data policies, helping them understand why these changes are important, and supporting them through the transition. The goal is to ensure everyone is on board and ready to work with the new data practices. This way, you can ensure your data governance plan is successful and your business continues to run smoothly, even as you make changes to how you manage data.

6. Technology

Think of technology in a data governance framework as the toolbox that helps you put your data governance plan into action. This might include tools for managing metadata, like a data catalog which is essentially a ‘library’ of your data. Or it could involve a data quality reporting tool, which is like a health check-up for your data.

But remember, technology is like your supporting actor. Its job is to back up the processes you establish. For example, if you have a process for regularly cleaning up your data, a data quality tool can help automate this task and make it more efficient.

A common mistake is to get the technology first and then figure out the processes. It’s like buying a fancy car before you know how to drive. Instead, you should first establish what you want to achieve with your data governance (i.e. your processes), and then find the technology that best supports these goals.

7. Data and enterprise architecture

This component is about how your data is structured and integrated with your broader business and IT environment. Think of it as the blueprint for a building – it outlines how all the different parts fit together. In terms of data governance, this architecture maps out:

  • how data flows through your organization,
  • where it’s stored,
  • who has access to it, and
  • how it’s protected.

For instance, your sales team might collect customer data which then gets used by the marketing team for targeted campaigns. The architecture ensures this data transfer happens smoothly, accurately, and securely.

By having a clear data and enterprise architecture, you can ensure data is managed effectively across your business. It helps avoid confusion, prevents data mishaps, and makes sure everyone is using data in a way that benefits your company. It’s like having a well-planned city – everything runs more efficiently, and you’re better equipped to reach your goals.


Having a solid data governance framework is no longer a luxury but a necessity for every modern business. To manage data as an asset effectively, organizations must understand and implement the essential components of a successful data governance framework. It’s not enough just to have policies in place – you need the right people, processes, technology, and architecture to make sure your data governance efforts are successful. By understanding each of these components and how they work together, you can create an effective plan that helps you reach your goals.

The data governance framework: not a one-size-fits-all

In a nutshell, implementing a data governance framework in your organization is not a cookie-cutter process – it’s more like creating a custom-tailored suit. There are many factors to consider, and the approach will be unique to your organization, depending on your specific needs, goals, and the importance of data governance in your business strategy.

For instance, if your organization is heavily driven by compliance, you might spend more energy on establishing policies, rules, and standards. On the other hand, if your goal is to extract greater value from your data, you may emphasize improving data usage, refining definitions, and metadata, and enhancing understanding of data.

Essentially, the implementation of this framework is a customized journey. It’s not a one-size-fits-all solution but rather a tailored approach that aligns with your organization’s specific needs and goals. The direction and focus of this journey will ultimately be determined by you and your organization.

Setting up your data governance framework?

Mathias Vercauteren, Senior Data Governance Consultant at LACO

Mathias Vercauteren

Senior Data Governance Consultant at LACO

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