What is data governance maturity and how to measure it?

A data governance maturity model is a methodology for measuring an organization's data governance initiatives. By understanding your data governance maturity level, you can effectively communicate next steps to your team and make more informed decisions for improvement. This article explores the concept of data governance maturity and provides a list of questions to measure it. Among other things, it will present the most efficient (and functional) way to move forward on the data maturity scale.

Taking Stock: How Mature is Your Data Governance?

Data is the lifeblood of any organization these days. Just as we need to take care of our bodies to keep them functioning and performing well, we need to be able to take care of our data as a valuable asset. However, with the vast amount of information coming in, ensuring its accuracy, availability, and security becomes paramount and a very challenging task. This is where data management comes in. But how effective are your data governance efforts? This is why we should speak about the concept of data governance maturity.

Understanding Data Governance Maturity

Data governance maturity reflects the progressiveness and effectiveness of your organization's data governance practices.  It's a spectrum, ranging from ad-hoc, reactive approaches to well-defined, proactive strategies.  A mature data governance program fosters trust in data, empowers better decision-making, and mitigates risks associated with inaccurate or non-compliant information.

Data governance is an ongoing process, not a one-time fix. An illustration of the process is how to get from the caterpillar stage to the butterfly. He has to go through the stages. The butterfly (the one in the advanced stage) would be in the case of data governance the one who uses the software designed for it
"I haven't started with data governance to better understand the data we store. Have you?"

Levels of Your Data Governance Maturity

Fortunately, you don't have to guess where you stand.  Data governance maturity models provide a framework to assess your organization's current state. These models typically present a staged progression, with each level outlining specific characteristics of data governance practices.

There are indeed many definitions of the different phases of data governance maturity. In this article, we will present the model that we consider to be the most universal and probably the most understandable.

Data governance maturity stages, unaware, aware, defined, managed and optimizing
Data governance maturity stages

Here's a simplified breakdown of what you might encounter in a data governance maturity model:


Lack of awareness of the need for data management or integration. For example, companies may have a data warehouse, but they don't know about data governance. This organization is operating in a data Wild West. No clear rules or processes for managing information lead to fragmented and unreliable data. Decisions are being made in the dark, without the benefit of accurate or unified information. There's no system for ownership, accountability, or overall data management.


At this stage, the organization begins to build its data management processes. There are parts of the company that are already dedicated to data governance. The organization as a whole is beginning to realize the positive benefits of data governance and the value of its own data.  

Although data ownership and clear processes are often still lacking, leaders are realizing the value of information governance, the shortcomings of current data quality and reporting practices, and the risks associated with not addressing these issues. IBM's model at this level, which it refers to as reactive, underscores the fact that most of the processes here happen only in response to the situation at hand.  


The organization is moving towards a more controlled data environment. Data policies are established, some data stewards are appointed, and basic data management technology is in place. Employees are aware that data governance will become part of their work.  


Standardized data governance processes are in place, with data quality controls and metrics implemented. Communication and collaboration around data is improving.

Specifically, the trend in process implementation is even more advanced and multi-level, with greater involvement of all employees who are properly informed and trained in data management practices. Standardization of procedures across the organization.

Data governance is an evolution, it has to go throught it stages. Illustration shows you shoud start doing good and after some time it will get better


Data governance is fully integrated into organizational practices. There's a strong focus on continuous improvement, with data quality actively monitored and managed. Individual leaders are motivated to use data to do their jobs through KPIs tied to data and data governance.

Taking the Next Step

Once you have a general understanding of the maturity levels, how do you determine your organization's specific position? Many data governance maturity models come with self-assessment questionnaires.  These questionnaires ask targeted questions about various aspects of your data governance practices, assigning scores based on your responses.  By evaluating your score against the pre-defined levels, you gain valuable insights into your current maturity level. This will enable you to understand where your organization is, what your staff knows, and what they don't.

Measuring your data governance maturity. Ask your team these questions:

A. Data Ownership and Accountability:
  • Are data owners clearly defined for all critical data sets?
  • Are data owners accountable for the quality, accuracy, and security of their assigned data?
  • Is there a documented process for escalating data ownership or access issues?

If so, you are at least at the level “defined”.

An illustration of the power of cooperation. You should organize, not panic. For data governance, you need some allies

B. Data Policies and Standards:
  • Are there well-defined data governance policies covering areas like data access, security, retention, and quality?
  • Are data standards and guidelines documented and readily available to users?
  • How effectively are data policies and standards communicated and enforced across the organization?

If so, your data governance processes are managed.

C. Data Management Processes:
  • Are there standardized processes for data collection, cleansing, transformation, and loading?
  • Is there a documented data lifecycle management plan in place?
  • Are data management processes consistently followed and monitored?

If so, you are optimizing.

D. Data Technology and Architecture:
  • Does the organization leverage data management technologies to support data governance initiatives?
  • Is there a defined data architecture that promotes data consistency and accessibility?
  • How effectively are data management tools integrated with existing IT systems?

If so, you can be at least at the “defined” level.

E. Communication and Collaboration:
  • Does the organization have a clear communication strategy for data governance initiatives?
  • Are there regular forums for stakeholders to discuss data governance challenges and opportunities?
  • How effectively does the organization collaborate across departments to ensure consistent data management practices?

If you are successful in these points, you can be at the “managed” or “optimized” level.

F. Data Governance Maturity:
  • How satisfied are users with the current state of data quality and governance within the organization?
  • To what extent are data governance principles embedded into organizational decision-making?
  • Does the organization have a continuous improvement process for data governance practices?

By answering these types of questions, organizations can gain valuable insights into their data governance maturity level and identify areas for improvement.

How to get from unaware to managed?  

With using software like Dawiso, you can easily stage by stage get from level 1 to level 4.

The evolution of what problem you are solving at what stage of data governance maturity. There are different comments in the figure. For example, in the unaware phase, you'll ask questions like "Is there a solution?" or "How did you calculate this KPI?". Conversely, in the optimizing phase, you'll see comments like "Starting with a new report, let's start a discussion on how it will align with data governance".

You already know you want to get into data governance, you have the basics and want to start big. Where to begin?

Data governance software like Dawiso can streamline the journey from unaware/aware level to managed data governance by providing a centralized platform for managing metadata, automating tasks, and fostering collaboration.

This singular view enhances visibility and control over your data assets.  Furthermore, Dawiso automates tedious tasks like data lineage and access management.  This frees up valuable resources within your team, allowing them to dedicate their time and expertise to more strategic and high-level analysis.  Finally, Dawiso fosters collaboration by facilitating communication between data users and owners.  This collaborative environment promotes a data-sharing culture, another key ingredient in achieving a mature and successful data governance program.

Mature governance practices ensure consistent and accurate information, leading to reliable analytics that you can confidently base your decisions on.  Clean and organized data, a hallmark of high maturity, becomes the fuel for advanced analytics, uncovering deeper insights previously hidden in the chaos.  This empowers organizations to make improved decisions across all levels, fostering a data-driven culture that translates into tangible benefits. Streamlined data processes achieved through mature governance save time and resources, allowing your team to focus on higher-level analysis.  Perhaps most importantly, a high level of data governance maturity grants a competitive advantage.  With reliable data and the ability to extract meaningful insights, your organization can make strategic choices that propel you ahead of the competition.

Petr Mikeška
Dawiso CEO
Petr Mikeška
Dawiso CEO

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