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Data Governance
Maturity Matrix

Discover data governance, the key to digital transformation and increased data literacy

Data governance cannot be decreed, deployed, or bought off the shelf. There is no one-size-fits-all-solution.
Every organization must adopt and develop a personalized data governance strategy to succeed in its digital transformation.

DataGalaxy’s Data Knowledge Catalog platform equips your organization with the right toolset
to enable successful digital transformation through a state-of-the-art data governance plan
that works best for you.

Finding the right path for you

The definition of data governance can differ among organizations depending on the data maturity of employees and the amount of data in circulation in the company.
Additionally, the steps to achieve organizational data governance can range in every step of the journey, including defining your goal, implementing a strategy to achieve it, and defining the means to measure progress.
However, there are some universal steps and best practices to follow concerning what data governance means in your organization.

How to use the Data Governance maturity matrix

1.

Start by selecting the most relevant stage from the tabs above (Collective Intelligence, Daily Collaboration, Intermittent Motion, Reduced Collaboration, or Siloed Approach no approach). Each tab represents a different level of data culture maturity within your organization.

2.

Once you’ve chosen your stage, scroll down to explore its key characteristics, indicators, and action plans. These insights will help you assess your current position and identify areas for improvement.

3.

By recognizing where your organization aligns the most, you can pinpoint strengths, uncover gaps, and take actionable steps toward a more advanced and efficient data governance strategy.

Let’s discover your starting point and take your data maturity to the next level!

Collective Intelligence

The ultimate level of cultural maturity is based on a system driven by common sense and a shared understanding of key issues by all. At this stage, users who were previously just “consumers of information” also become active contributors to updating the reference framework. They engage through ratings (social scores), modification proposals, or requests for additional information (crowdsourcing).

There is a strong interdependence among team members, who support each other in handling interactions. Maintaining the Data Catalog is deeply ingrained in the teams, who have a thorough understanding of the data value chain. All your operational processes (whether IT or business-related) now incorporate a data dimension, ensuring continuous interaction within the Data Catalog.

You have successfully established a true Data Culture within your company!

No central data knowledge available

With such expertise in collaboration and acculturation, how could you miss the data documentation aspect!

You’re starting from ground level. No problem, we can work on it together!

Data is localized and identified

Collaboration works quite well in your organization. However, from a data documentation perspective, not everything is clear. Don’t worry, your position is quite encouraging and will allow you to move forward quickly.

Indicators to follow

The only indicator available is the number of objects found. This indicator will probably follow a stepwise curve for technical data, with each scan of a new source bringing a considerable number of objects. For business data, the curve will look more like a logarithmic curve with a slow but constant progression.

Action plan

Since you have identified and defined a lot of data, it’s important to classify it by domain. This will allow you to assign owners and see who could take care of certain properties. Implementing a matrix organization will speed up the overall data deployment, but be careful to coordinate actions with your colleagues.

Data is defined and classified

With your ability to collaborate and share knowledge, it’s surprising that you’re not further along in your data asset maturity. Have you taken into account the specifics of managing and sharing data knowledge?

Indicators to follow

Attribute completion: This indicator reflects the completeness of the information. The more attributes you have filled in, the richer your data repository is.
Number of objects per attribute: For mono attributes, this indicator will allow you to understand the distribution of objects by value. You can, for example, distribute the maintenance load fairly among the different stewards.

You may also be interested in the number of your domains, which allow you to organize your objects transversively and understand the company’s level of organizational assets.

Action plan

Conduct a mini audit of your data knowledge management tool. It’s important to choose a tool that is efficient for the retrieval of technical data, that allows for the enrichment of information and, above all, that is specific to data knowledge management. This can include a scalable data catalog or governance platform that allows you to reach your maturity objective without having to change it in the coming months.

Rules are established and monitored

With the support of your entire company, you are well on your way to achieving data governance 2.0. What remains to be done is to implement the data life cycles to enable a virtuous – and more fluid – circle of continuous updating.

Indicators to follow

The “percentage of rules followed,” those for which you have a figure at a minimum monthly frequency, allows you to validate the application of these rules and the good management of your data. This indicator will be completed with an alert system and action plan for unmet objectives.

The distribution of objects by role takes on another dimension – It allows you to apprehend the capacity of people to apply their role within the data community.

Action plan

Lean on your main asset: Having federated and acculturated teams.
Prepare a few presentations on lifecycle management: How to plan and implement new data, how to obsolesce data that no longer makes sense, etc.

The data lifecycle is known

You are in great shape! You are likely well informed about domain driven architecture and with fully data driven business teams, you have all the assets to accomplish your data strategy. But be careful not to fall asleep: as you know, data maturity is more a state of mind than an end point.

Indicators to follow

“Delays in Status (not validated)” gives you information about the speed of your lifecycle and “Inactivity on objects” lets you know which data is dormant. For validated data, these indicators also reflect the stability of your definitions. These are also important to identify the life cycle of your items and make it a rule for managing your data catalog.

Action plan

Stay informed about the latest developments in metadata management and share your experience and knowledge with your peers. Get involved in expert communities online to share your knowledge and increase your organization’s overall data literacy.

Daily collaboration

At this level of maturity, your teams have learned to work together on a daily basis. The people in charge of the objects are clearly identified by their colleagues who ask for their help via email interactions.

Nevertheless, these teams are often led by a manager who sets the pace and organizes tasks and responsibilities. The users of the information come to read information in the knowledge repository but do not participate much in its evolution (weak feedback loop).

No central data knowledge available

Perhaps you have real communication skills but your heritage knowledge is still completely siloed. You probably spend a lot of time exchanging dictionaries in Excel files, definition sheets in Word, or sharing links on some wiki scattered around.

Action plan

You need to capitalize on your main strength: Collaboration. Identify a project team whose mission will be to define your first use case and list all the data related to it.

This project will be the first brick in the foundation, so it must be a success. Remember to involve the business users to launch a global dynamic and choose a tool that allows you to centralize the information.

Data is localized and identified

Collaboration works quite well in your organization. However, from a data documentation perspective, not everything is clear. Don’t worry, your position is quite encouraging and will allow you to move forward quickly.

Indicators to follow

The only indicator available is the number of objects found.

This indicator will probably follow a stepwise curve for technical data, with each scan of a new source bringing a considerable number of objects. For business data, the curve will look more like a logarithmic curve with a slow but constant progression.

Action plan

Since you have identified and defined a lot of data, it’s important to classify it by domain so that you can assign owners. You can also see who could take care of certain properties in a cross-functional way (e.g. on personal data). Implementing such a matrix organization will help speed up overall project deployment!

Data is defined and classified

With your ability to collaborate and share knowledge, it’s surprising that you’re not further along in your data asset maturity. Have you taken into account the specifics of managing and sharing data knowledge?

Indicators to follow

Attribute completion: This indicator reflects the completeness of the information. The more attributes you have filled in, the richer your data repository is.

Number of objects per attribute: For mono attributes, this indicator will allow you to understand the distribution of objects by value. You can, for example, distribute the maintenance load fairly among the different stewards.

You may also be interested in the number of domains, which allow you to organize your objects transversely and understand the level of organization of your assets.

Action plan

Conduct a mini audit of your data knowledge management tool. If it is a wiki, for example, there is no doubt that it represents a real obstacle in your path towards governance. Choose a tool that is efficient for the retrieval of technical data, that allows for the enrichment of information and, above all, that is specific to data knowledge management. This tool should allow you to reach your maturity objective without having to change it in the coming months.

Rules are established and monitored

Congratulations, you’re well on your way to data governance, but now you’ll need to consider the lifecycle of your metadata: how to make it evolve in response to business or regulatory changes as well as technical evolutions. And of course, continue to train and acculturate all employees to the proper use of data.

Indicators to follow

The “Percentage of rules followed (measured),” those for which you have a figure at a minimum monthly frequency, allows you to validate the application of these rules and the good management of your data. This indicator will of course be completed with an alert system and action plan for unmet objectives.

The distribution of objects by role takes on another dimension, as it allows you to apprehend the capacity of people to apply their role within the data community.

Action plan

To reach the last step, apply the same good practices as before: Work by iterations, breakdown by business domain, implement a data mesh system for data maturity, and continue to involve all teams. Popularization and communication are essential to explain the complex concepts of data life cycle.

The data lifecycle is known

You are clearly positioned among the champions of data governance. However, you still need to put some oil in the wheels to make things really fluid. Fear not! Collaborative data governance is within reach!

Indicators to follow

The “Delays in Status (not validated)” indicator gives you information about the speed of your lifecycle and the “Inactivity on objects” indicator lets you know which data is dormant. For validated data, these indicators also reflect the stability of your definitions.

It will also be important to identify the life cycle of your items and make it a rule for managing your Data Knowledge Catalog. It is interesting to formalize the revision of your business terms once a year, for example.

Finally, a qualitative indicator would be the adequacy of the metamodel with your sector(s) of activity: What are the indispensable attributes that you are missing or the superfluous ones that you maintain? Have you implemented the rules specific to your business? Have you integrated your Data Catalog with your Quality Control tool?

Action plan

There may be a risk that your acculturation strategy will run out of steam. Have you thought about implementing conferences or training on the added value of collaboration around data? In a large organization, also think about the notion of decentralization to scale. More and more experts are available to talk about data mesh every day!

Intermittent motion

At this level, the data experts will update the knowledge repository discontinuously, to meet audit needs, to increase the scope of a project or to meet a specific documentation need.

This mode of operation is not optimal, because on the one hand, the fact of collaborating in an irregular manner causes automatisms to be lost and therefore limits productivity, and on the other hand, because the risk of not having updated knowledge is important.

No central data knowledge available

Perhaps you have real communication skills but your heritage knowledge is still completely siloed. You probably spend a lot of time exchanging dictionaries in Excel files, definition sheets in Word, or sharing links on some wiki scattered around.

Action plan

You need to capitalize on your main strength: Collaboration. Identify a project team whose mission will be to define your first use case and list all the data related to it.

This project will be the first brick in the foundation, so it must be a success. Remember to involve the business users to launch a global dynamic and choose a tool that allows you to centralize the information.

Data is localized and identified

Collaboration works quite well in your organization. However, from a data documentation perspective, not everything is clear. Don’t worry, your position is quite encouraging and will allow you to move forward quickly.

Indicators to follow

The only indicator available is the number of objects found. This indicator will probably follow a stepwise curve for technical data, with each scan of a new source bringing a considerable number of objects. For business data, the curve will look more like a logarithmic curve with a slow but constant progression.

Data is defined and classified

Your data repository is accessible to IT and business users, congratulations! Reaching this stage often requires a lot of work. Nevertheless, you are now likely to start having quality issues in your data and questions about how to move to the next step.

Indicators to follow

Attribute completion: This indicator reflects the completeness of the information. The more attributes you have filled in, the richer your data repository is.

Number of objects per attribute: for monovalent attributes, this indicator will allow you to understand the distribution of objects by value. You can, for example, distribute the maintenance load fairly among the different stewards.

Action plan

It is time for your team to take an interest in rules management, which allows you to define policies for managing data and metadata. This also concerns the notion of interface contracts at the level of the data flows in place in your system.

Rules are established and monitored

Individually, some members of your data team are undeniably experts at explaining your data assets. But as soon as they have to leave, problem resolution times skyrocket, and even when they are present, you better hope they are available to respond quickly. It is urgent to change things so that these experts are no longer a bottleneck.

Indicators to follow

The “Percentage of rules followed (measured)” indicator, those for which you have a figure at a minimum monthly frequency, allows you to validate the application of these rules and the good management of your data. This indicator will of course be completed with an alert system and action plan for unmet objectives.

The distribution of objects by role takes on another dimension, as it allows you to apprehend the capacity of people to apply their role within the data community.

Action plan

Agree to postpone unimportant operational actions so that your experts can transcribe their knowledge into a centralized repository that is accessible to as many people as possible. This simple action, in addition to making exchanges more fluid, will also allow your experts to be available to update their knowledge and you will thus gain in productivity on all levels.

The data lifecycle is known

You have succeeded in mapping most of your company’s data and have a real knowledge repository, complete and regularly updated. But what value do you get from it from a business point of view? Probably very little and the team in charge of this repository is probably perceived as being in its ivory tower.

Indicators to follow

The “Delays in Status (not validated)” indicator gives you information about the speed of your lifecycle and the “Inactivity on objects” indicator lets you know which data is dormant. For validated data, these indicators also reflect the stability of your definitions.

It will also be important to identify the life cycle of your items and make it a rule for managing your Data Knowledge Catalog. It is interesting to formalize the revision of your business terms once a year as an example.

What are the indispensable attributes that you are missing or the superfluous ones that you maintain? Have you implemented the rules specific to your business? Have you integrated your Data Knowledge Catalog with your quality control tool?

Action plan

It’s all about building your data culture. Since you have a rich and coherent repository, start by ensuring that it is accessible and can be segmented to facilitate its understanding. Do you have the right tool for this?
Make presentations that show the value of this knowledge when used by business users (improved quality of decisions, reduced misunderstandings and productivity in information retrieval.

Reduced collaboration

Employees partially identify the people who work on the same data perimeter as them. In some cases, a referent is clearly identified, but you then suffer from the “know-it-all” syndrome: this person is so solicited that he or she does not have the time to capitalize or share information in a global way; when he or she is absent, it is panic on board!

The interest for data is emerging but the associated stakes are not mastered.

No central data knowledge available

Perhaps you have real communication skills but your heritage knowledge is still completely siloed. You probably spend a lot of time exchanging dictionaries in Excel files, definition sheets in Word, or sharing links on some wiki scattered around.

Indicators to follow

You’re starting from ground level. No problem, we can work on it together.

Data is localized and identified

You have a list of your data but still need to enrich it while trying to get value from what you are creating. This is most likely the time to look at how to open up your data more widely to the business.

Indicators to follow

The only indicator available is the number of objects found. This indicator will probably follow a stepwise curve for technical data, with each scan of a new source bringing a considerable number of objects. For business data, the curve will look more like a logarithmic curve with a slow but constant progression.

Action plan

Your goal in a word: Scalability! Having identified and located your data, it is now essential to obtain the participation of everyone to obtain good definitions of your data. As is often the case, an iterative approach is preferable to capitalize on the enthusiasm of the teams following the value proposed by the repository you are building and therefore avoid getting bogged down in a multitude of perimeters.

Data is defined and classified

Individually, some members of your data team are undeniably experts at explaining your data assets. But as soon as they have to leave, problem resolution times skyrocket, and even when they are present, you better hope they are available to respond quickly. It is urgent to change things so that these experts are no longer a bottleneck.

Indicators to follow

Attribute completion: This indicator reflects the completeness of the information. The more attributes you have filled in, the richer your data repository is.

Number of objects per attribute: For mono attributes, this indicator will allow you to understand the distribution of objects by value. You can, for example, distribute the maintenance load fairly among the different stewards.

You can also be interested in the number of Domains (Domains allow you to organize your objects transversely and allow you to know the level of organization of your assets).

Action plan

Agree to postpone unimportant operational actions so that your experts can transcribe their knowledge into a centralized repository that is accessible to as many people as possible. This simple action, in addition to making exchanges more fluid, will also allow your experts to be available to update their knowledge and you will thus gain in productivity on all levels.

Rules are established and monitored

Individually, some members of your data team are undeniably experts at explaining your data assets. But as soon as they have to leave, problem resolution times skyrocket, and even when they are present, you better hope they are available to respond quickly. It is urgent to change things so that these experts are no longer a bottleneck.

Indicators to follow

The “Delays in Status (not validated)” gives you information about the speed of your lifecycle and the “Inactivity on objects” lets you know which data is dormant. For validated data, these indicators also reflect the stability of your definitions.

It will also be important to identify the life cycle of your items and make it a rule for managing your Data Catalog. It is interesting to formalize the revision of your business terms once a year as an example.

Finally, a qualitative indicator would be the adequacy of the metamodel with your sector(s) of activity: what are the indispensable attributes that you are missing or the superfluous ones that you maintain? Have you implemented the rules specific to your business? Have you integrated your Data Catalog with your Quality Control tool?

Action plan

It’s all about building your data culture. Since you have a rich and coherent repository, start by ensuring that it is accessible and can be segmented to facilitate its understanding. Do you have the right tool for this?

Make presentations that show the value of this knowledge when used by business users (improved quality of decisions, reduced misunderstandings and productivity in information retrieval.

The data lifecycle is known

You have succeeded in mapping most of your company’s data and have a real knowledge repository, complete and regularly updated. But what value do you get from it from a business point of view? Probably very little and the team in charge of this repository is probably perceived as being in its ivory tower.

Indicators to follow

The “Percentage of rules followed (measured)” – i.e. for which you have a figure at a minimum monthly frequency – allows you to validate the application of these rules and the good management of your data. This indicator will of course be completed with an alert system and action plan for unmet objectives.

The distribution of objects by role takes on another dimension, since it allows you to apprehend the capacity of people to apply their role within the data community.

Action plan

Agree to postpone unimportant operational actions so that your experts can transcribe their knowledge into a centralized repository that is accessible to as many people as possible. This simple action, in addition to making exchanges more fluid, will also allow your experts to be available to update their knowledge and you will thus gain in productivity on all levels.

Siloed approach no collaboration

There is no interaction between collaborators, each one is trying to understand the meaning of a piece of data and does not measure the impact of a wrong input or a wrong interpretation of the data.

You are typically in an information silo.

No central data knowledge available

Understandably, it’s disappointing to be at the very beginning of the data maturity path. The good news is that you are aware of your starting point! With no documentation for your data, and no real organization in this project, you have the advantage of starting with a blank canvas.

Indicators to follow

None – just worry about getting to level 2 as soon as possible.

Action plan

Identify someone with data domain knowledge and the desire to share, then give them time to list the data and use cases (name and technical location) and model it (the links between it). It doesn’t sound like much, but it will allow you to have a first iteration to show others how simple the process is, and you will progress quickly on the 2 axes of maturity.

Start sharing your strategy and early results to build traction and move into the inventory phase.

Data is localized and identified

There is no doubt about your ability to document your data or your level of knowledge about it. However, what is the value of this documentation if it is not accessible and understandable to others? You must put collaboration and sharing back at the center of your concerns.

Indicators to follow

The only indicator available is the number of objects found.

This indicator will probably follow a stepwise curve for technical data, with each scan of a new source bringing a considerable number of objects. For business data, the curve will look more like a logarithmic curve with a slow but constant progression.

Action plan

If there is one problem that can be easily solved, it knowledge sharing. All of your documentation could probably be made available to everyone with a few clicks. Ease of exploration (or search) and clarity of information are potentially more complicated to achieve. We recommend the implementation of a dedicated tool such as a Data Catalog to allow everyone to better exchange knowledge about the data.

Data is defined and classified

There is no doubt about your ability to document your data or your level of knowledge about it. However, what is the value of this documentation if it is not accessible and understandable to others? You must put collaboration and sharing back at the center of your concerns.

Indicators to follow

Attribute completion: This indicator reflects the completeness of the information. The more attributes you have filled in, the richer your data repository is.
Number of objects per attribute: for monovalent attributes, this indicator will allow you to understand the distribution of objects by value. You can, for example, distribute the maintenance load fairly among the different stewards.

You can also be interested in the number of Domains (Domains allow you to organize your objects in a transverse way and allow you to know the level of organization of your assets).
progression.

Action plan

If there is one problem that can be easily solved, it knowledge sharing. All of your documentation could probably be made available to everyone with a few clicks. Ease of exploration (or search) and clarity of information are potentially more complicated to achieve. We recommend the implementation of a dedicated tool such as a Data Catalog to allow everyone to better exchange knowledge about the data.

Rules are established and monitored

There is no doubt about your ability to document your data or your level of knowledge about it. However, what is the value of this documentation if it is not accessible and understandable to others? You must put collaboration and sharing back at the center of your concerns.

Indicators to follow

The “Percentage of rules followed (measured)” – i.e. for which you have a figure at a minimum monthly frequency – allows you to validate the application of these rules and the good management of your data. This indicator will of course be completed with an alert system and action plan for unmet objectives.

The distribution of objects by role takes on another dimension, since it allows you to apprehend the capacity of people to apply their role within the data community.

Action plan

If there is one problem that can be easily solved, it knowledge sharing. All of your documentation could probably be made available to everyone with a few clicks. Ease of exploration (or search) and clarity of information are potentially more complicated to achieve. We recommend the implementation of a dedicated tool such as a Data Catalog to allow everyone to better exchange knowledge about the data.

The data lifecycle is known

Especially if you are in a large company, this position is surprising to say the least! How can you control your data assets so well without a minimum of sharing and collaboration? In short, you’re playing a joke on us! No luck, we already know it.

Indicators to follow

The “Delays in Status (not validated)” gives you information about the speed of your lifecycle and the “Inactivity on objects” lets you know which data is dormant. For validated data, these indicators also reflect the stability of your definitions.

 

It will also be important to identify the life cycle of your items and make it a rule for managing your Data Catalog. It is interesting to formalize the revision of your business terms once a year as an example.

Finally, a qualitative indicator would be the adequacy of the metamodel with your sector(s) of activity: what are the indispensable attributes that you are missing or the superfluous ones that you maintain? Have you implemented the rules specific to your business? Have you integrated your Data Catalog with your Quality Control tool?

Action plan

Feel free to check another box to see if we can help you advance your data maturity!

Our vision

Not only does data governance improve overall data quality, it also has a significant impact on your company’s overall competitiveness and decision-making cabilities. Data governance allows users to better manage current challenges and gives access to new business opportunities that would not possible with data of low quality.

Successful data governance programs are attainable through a well-executed, acionable plan: Organizations can overcome the challenges that change inevitably presents by starting small and gradually expanding the program.

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