What is Data Custodianship?

What is Data Custodianship? It's a question that seems straightforward, but sometimes in Data Governance, definitions can vary depending on who you ask. And this is okay because some terms might not suit the culture or structure of your organisation. It's always best to prioritise what will work for your organisation specifically.

However, it is something to be aware of, particularly when ‘googling’ terms online or you're new to an organisation. Do not assume that when colleagues say Data Custodianship, they mean what I'm about to tell you. I would say the best thing to do if somebody uses a term like Data Custodian is to ask them what they mean by that and who that is. This clears things up from the start. You'll be on the same page as everyone else and can have useful conversations.

Who are Data Custodians?

When I talk about Data Custodians, I’m referring to the IT department. I often talk about the importance of business in taking ownership in managing and understanding data but, in this instance, IT remains a crucial player.

The role of Data Custodian differs from other roles in Data Governance, such as data Owners and Data Stewards etc. With these roles, I'd always recommend that you go and find named individuals. However, the opposite is true when it comes to Data Custodians because generally, I would say the whole of your IT department are Data Custodians. There are so many different areas of expertise and disciplines within an IT department that no one person will know absolutely everything about that system to be the Data Custodian for it. So I usually just say that IT are the Data Custodians for all the data that's held on IT supported systems at your organisation.

The responsibility of IT, as Data Custodians, lies in maintaining data on systems in line with the requirements of the business. This involves tasks such as data maintenance, migration, aggregation and transformation - all guided by business needs.

The misconception that IT own the data

FIt's important to realise that IT does NOT own the organisation’s data. Yes, it is on their systems, and they have the expertise that the business side of the organisation may not have, but IT shouldn't be expected to work out what to do with the data.

Before the introduction of formal Data Governance, businesses often rely on IT to make business data-related decisions, but this can lead to them being blamed for things. I think is unfair because sometimes they're just doing the best they can with poor requirements from the business.

Defining Data Custodianship helps both the business and IT

Data Custodianship helps to clarify roles and responsibilities within the IT department. When I work with IT departments, they are pleased with this Data Governance role as it gives them very clear business requirements and named business people to go to make decisions about data.

So this is a really good way of starting to break down some silos and get the business to understand what happens to the data when it's on systems. IT has always played the role of Data Custodian but, without Data Governance, they've perhaps done it without the input they needed from the business.

So having a Data Governance framework in place and identifying IT as Data Custodians is a really good way to start improving communications and making consistent, holistic decisions about data.

Understanding Data Custodianship is essential for establishing effective Data Governance practices. By recognising the roles of both the business and IT, organisations can foster collaboration, enhance data quality and make informed decisions that align with business objectives.

If you would like further support with anything Data Governance related in your organisation, you can book a call with me using the button below.

The Relationship Between Data Governance and Data Quality

We often talk about Data Governance and Data Quality in the same breath. This can lead to confusion, with some people assuming they are the same thing when actually, they’re not. However, they’re closely related, and in my experience, they work best when managed by the same team.

Understanding Data Quality and Data Governance

Data Quality is about making sure that data is good enough to use. It’s a straightforward concept, if data is incorrect, incomplete or inconsistent, it can’t support business decisions effectively.

Data Governance, on the other hand, is about creating a structured framework of roles, responsibilities, and processes to manage data.

Although they’re separate disciplines within data management, they are very much intertwined. When you try to improve data quality without governance, you usually end up applying short-term fixes rather than solving the root cause of data issues.

Why You Can’t Have Good Data Quality Without Governance

From my experience, many organisations focus on Data Quality long before they consider Data Governance. After all, it’s easy to understand the need for clean, reliable data. The problem is that without Data Governance, Data Quality efforts are often tactical rather than strategic.

For example, businesses might:

• Regularly fix errors in reports but do not address the source of the errors.

• Use automated data cleansing when loading data into analytics systems.

• Have teams manually correct data every month, quarter, or year.

These approaches may make data usable in the short term, but they do not prevent problems from recurring. The same errors will keep happening, and this is where Data Governance comes in.

Data Governance establishes:

• Roles and responsibilities so that specific people (Data Owners and Data Stewards) are accountable for data quality.

• Processes to resolve data issues at the source, rather than just fixing them repeatedly at the point the data is used (one of the most valuable Data Governance processes, in my opinion, is data quality issue resolution. This identifies and fixes the root causes of poor data quality rather than applying endless fixes).

Why Data Quality and Data Governance Should Be Managed by the Same Team

Because of their close relationship, Data Quality and Data Governance should be managed together. When separate teams handle them, challenges arise. I have seen organisations where the Data Quality team is focused on fixing errors while a Data Governance team tries to implement a structured framework of definitions and roles, and responsibilities. In such cases, business users tend to bypass Data Governance efforts entirely and go directly to the Data Quality team when they need a quick fix. Their immediate concern is solving their problem in the moment rather than considering long-term improvements.

When the same team is responsible for both Data Quality and Data Governance, they are able to provide short-term fixes while simultaneously working on long-term solutions, ensuring that immediate needs are met without neglecting the bigger picture. It’s also great because they can demonstrate the true value of Data Governance by proactively solving recurring data issues rather than simply reacting to them.

Moving From Reactive to Proactive Data Management

Without Data Governance, organisations are stuck in a cycle of fixing the same problems repeatedly. With Data Governance in place, they can shift to a proactive approach:

• Data issues are resolved at the source, reducing ongoing fixes.

• Business users understand their role in maintaining data quality.

• Data Owners and Stewards take responsibility for preventing and fixing errors.

Many organisations still manually cleanse data before they can use it. However, this is a waste of time and resources and is something which Data Governance eliminates.

Final Thoughts

If your organisation is focusing on Data Quality without Data Governance, you are likely applying temporary fixes rather than permanent solutions. While Data Governance and Data Quality are distinct disciplines, they should work together, ideally within the same team, to ensure sustainable data improvements.

If you found this helpful, please consider sharing this article to help others improve their approach to Data Governance and data quality. And if you’d like more support, you can book a call with me using the button below.

If you would like further support with anything Data Governance related in your organisation, you can book a call with me using the button below.

Who is Responsible for Drafting Data Definitions?

Good data definitions are essential. I can’t emphasise enough that without them, you’ll lack clarity and accuracy, and your data will suffer for it. But who should actually be writing them? I’ve written extensively over the years about how to create strong data definitions (read more about that here), but I don’t think I’ve ever really tackled the question of who’s responsible for writing them.

Spoiler: it’s not the Data Governance team!

Who Should Really Be Writing Data Definitions?

I recently heard from a Data Governance Analyst whose team was under pressure to write data definitions for their entire organisation. The business wanted the Data Governance team to take ownership of this task, but from my perspective, this is not the Data Governance team’s job.

The reason is simple: the Data Governance team cannot possibly be experts in every single piece of data that your organisation owns. It’s the business users who know the data best, so they are the ones who should write the definitions.

By all means, the Data Governance team can support the business by providing guidance, advice and training on how to write good definitions. You might run workshops, offer templates, or give feedback, but the actual drafting needs to come from the business. After all, if the business uses the data and relies on it for decision-making, it only makes sense that only they are in the position to define what it means.

The Role of Data Owners and Data Stewards

This is where the roles of Data Owners and Data Stewards become essential.

Data Owners - accountable but not for drafting

Data Owners are senior people in your organisation. They are accountable for a specific set of data, which includes being responsible for reviewing and approving definitions. However, they’re typically too senior and busy to draft definitions themselves.

Data Stewards -drafting the definitions

This is where Data Stewards step in. Drafting data definitions is a key part of their role. They are usually the subject matter experts on that data, and therefore, have the knowledge and expertise to create definitions for the data.

Sometimes, a Data Steward might not have all the answers and will need to work with other business users in their area. That’s perfectly fine, as long as the definitions are drafted by those who truly understand the data.

Good data definitions are the foundation of reliable, usable data but creating them isn’t the job of the Data Governance team alone. While data governance provides the framework, tools, and support, it’s the business, especially Data Stewards and knowledgeable users who must do the heavy lifting.

If your organisation wants meaningful, accurate, and widely accepted data definitions, involve the people who use and understand the data every day. Empower your Data Stewards, support your Data Owners, and let the Data Governance team guide the process - not own it.

That’s how you build definitions that actually work.

If you would like further support with anything Data Governance related in your organisation, you can book a call with me using the button below.

Why Every Organisation Needs a Data Governance Council

Data is evolving all the time, and many organisations find it hard to keep up with how to manage it properly. As data piles up, so do the problems. Ownership confusion, unclear responsibilities, data quality issues, and regulatory compliance risks. That’s where a Data Governance Council or Committee (DGC) can become really helpful. In fact, I would go as far as saying that they are essential for successful Data Governance.

In today’s blog, I want to talk about how a DGC can tackle your organisation’s challenges and support you.

1. Accountability

Imagine this… You’ve been named a data owner. It sounded important at the time… but months later, no one’s reminded you what that role actually means.

That’s a common scenario. And that’s exactly what a DGC prevents.

Why it matters:

• It clarifies roles for data owners and data stewards so no one’s left guessing.

• It keeps governance alive, not just as a one-off project but a consistent business practice.

• A functioning DGC doesn’t let responsibilities slide. It builds accountability into the structure.

2. A place to go when problems arise

There are many problems which can arise with data and they happen more often than anyone likes to admit. What matters is how your organisation responds.

The DGC advantage:

• The council acts as an authoritative escalation point run by peers who understand the business.

• It creates a safe space for collaborative problem-solving, not blame.

• Instead of letting issues fester, the council ensures they’re dealt with constructively and with the right people in the room.

3. Making the invisible visible

Ever spotted dodgy data in a report and thought you’d better not say anything? Maybe it felt too risky, or perhaps you weren’t sure it was your place to speak up. You’re not alone. In many organisations, poor-quality data flies under the radar simply because people stay quiet and do not escalate the issues to senior stakeholders.

Why?

Sometimes it’s fear of blame. Other times, it's just unclear where to take the problem. So the issue stays hidden and becomes part of the decision-making process anyway. That’s when risks really start to build up. And this is where a Data Governance Council is useful.

• It creates a safe and structured channel for raising concerns so people aren’t left wondering if they should speak up or stay quiet.

• It gives staff a clear process to follow, which helps them act early rather than waiting for a crisis.

• And most importantly, it brings these issues to senior leadership, turning vague worries into visible problems that can be prioritised and fixed.

Over time, this shifts the culture. Raising a concern about data quality stops feeling like "making a fuss" and starts being seen as good data governance. That makes it easier to catch issues early, before they impact customer experience, compliance, or strategy.

4. From rules to culture

Policies alone will not change behaviour, and that is why, as well as being operational, a well-composed DGC is also supportive of Data Governance culture.

Here’s how it shifts mindsets:

• Data Owners from across departments make decisions, promote best practices and lead by example.

• It sends a clear message that senior management has signed up to and is supportive of the Data Governance initiative.

5. Governance that guards against risk

Compliance and risk are constant concerns, but a static framework might not be the most beneficial. You need active oversight.

That’s exactly where the DGC proves its value:

• It ensures that your Data Governance Framework evolves to meet the changing needs of your organisation.

• It keeps Data Governance practices aligned with changing regulations, helping your organisation stay compliant without constantly scrambling to catch up.

• It enables ongoing monitoring, so gaps are spotted before they become Issues going forward.

Final thoughts

A Data Governance Council might sound formal and it plays a serious role. But at its heart, it’s about creating the right conditions for people to do the right things with data. It gives structure to conversations that otherwise get lost, and it brings clarity where confusion often lives.

If you're building, or rebuilding, your data governance approach, having a council in place won’t solve every challenge overnight, but it will give your organisation the foundations, the forum, and the follow-through it needs to make real progress.

If you’d like to discuss getting support for your Data Governance initiative, why not book a call with me below?

What Problems Do Data Governance Councils and Domain Forums Actually Solve?

Data Governance councils and domain forums are often seen as just ‘talking shops’; all discussion, no action. But when done right, they can be much more. I asked data professionals from my network what problems these groups actually solve, and their answers offered lots of food for thought! Here’s what I learned from the discussions.

What Do Data Governance Councils Solve?

Think of a Data Governance council as the command centre for an organisation’s data strategy. Their job is to set the rules, define roles and make sure everyone understands how to manage and use data properly. When I asked my network of data professionals, 'What problems do Data Governance Councils solve?' here's what they had to say:

Councils can create a clear framework - councils establish processes, assign responsibilities, and even decide how data projects get funded. This makes sure there’s no confusion about who owns what.

Councils can solve data disagreements - when different teams have conflicting views on how data should be defined or used, the council acts as a referee. They can resolve disputes and provide clarity on tricky issues.

Councils can spotlight hidden problems - councils bring important data issues to senior leaders’ attention. Problems that might be ignored in day-to-day work can be raised and discussed.

Councils can provide executive backing - they help secure leadership support for new ways of working, ensuring changes are properly approved and supported.

Councils can manage risk and compliance - councils reduce business risk by making sure data policies comply with legal and regulatory requirements. They also set protocols for handling data breaches or other serious issues.

And What About Domain Forums?

While councils focus on the big picture, domain forums dive into the details. They take the council's decisions and make them work within specific business areas. Here's the problems they solve, as found by my network:

Putting plans into action - forums turn high-level strategies into everyday practices. They adapt enterprise-wide policies to fit the specific needs of different teams or departments.

Fixing data problems locally - if a team notices data quality issues, they can raise them in their domain forum. This helps catch and fix problems early before they become company-wide headaches!

Promoting collaboration - forums encourage teams to work together; sharing insights and learning from each other’s experiences. This can be invaluable for improving data processes across the business.

Clarifying accountability - forums make sure everyone understands their role in managing data. This helps prevent tasks from falling through the cracks.

Making It Work

So it’s not just me believing that they add value. There is a lot of value to be gained Data Governance councils and domain forums. These are answers by real people who find value in these groups. But remember, for councils and forums to be effective, they need clear goals and active participation. If they become one-sided updates or just another meeting on the calendar, they lose their impact. However, when run properly, they create space for collaboration and highly effective problem-solving.

As always, if you’d like any support with your Data Governance initiative, book a call with me below and we can discuss your needs further.

How Often Should You Revisit Your Data Governance Maturity Assessment?

One of my clients recently asked how often they should be assessing their Data Governance maturity. It’s a great question because many people underestimate how long Data Governance takes to implement and a maturity assessment is a useful tool to track progress and figure out what to focus on next.

In today’s blog, I’ll be exploring Data Governance maturity assessments in more detail.

What is a Data Governance maturity assessment?

A Data Governance maturity assessment helps you see what’s working, where the gaps are, and what needs improving.

But, the timing of carrying out these assessments is important because, as I mentioned above, Data Governance does take longer than you think, so you don't want to overestimate the number of assessments you need. You want to find a balance between making improvements and not overwhelming the team or resources. 

How often should you assess maturity?

From my experience, once a year is usually enough. This is because real change takes time. If you assess too soon, you might not see enough progress to justify the effort. Plus, people are busy - you don’t want to keep asking for their time if nothing major has changed.

That said, the timing can depend on how quickly your organisation is evolving. The best approach is to look at what’s been happening internally and decide whether it’s the right time for a reassessment.

Communication and culture

Maturity assessments aren’t just about checking progress - they also reveal how well Data Governance is being communicated across the organisation. 

When you're looking at the results of a Data Governance maturity assessment, don't take every result to mean that you're not doing certain things - it might be your communication at fault rather than the fact that you haven't done something! 

I remember times during my early career in Data Governance when I'd got results back from a maturity assessment and had been devastated because it stated that we hadn't done something that we had actually worked really hard on doing! I remember thinking, ‘We've done that. Why are they saying there are no data owners in this area? There clearly are!’ 

And then when I actually thought about it, I realised that yes, we'd done the work as a Data Governance team but what we hadn't done was communicate it to the wider audience. And the problem with this is that Data Governance doesn't work unless everybody's on board. You need to make a culture change and for that, you need to communicate. If people don’t know what you’ve achieved it’s as though it hasn’t happened for them!

Data Governance maturity assessments are brilliant tools for guiding and measuring the progress of an organisation's Data Governance efforts. However, they are most valuable when done at a pace that aligns with the organisation's ability to make change. Whether done every six months or annually, the focus should always be on actionable steps and creating a culture that values data as a business asset.

As always, if you have any questions or need further support with optimising your Data Governance initiatives, feel free to book a call with me using the button below.

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What's The Difference Between My Live and Pre-recorded Online Training Sessions?

When it comes to training in Data Governance, I like to keep the options available to you as flexible as possible. That's why, as well as the usual in-person courses, I offer two other types of training: live online sessions and pre-recorded courses. Both options have their unique advantages, so let me explain the differences to help you decide which is best for you or your team.

Live Online Training

If you’re looking for an interaction and you like learning amongst others, live online sessions are fantastic. Not only do you get to ask me questions on the spot, but you also get to meet others in the same field. There’s something quite helpful about hearing others’ stories and challenges – it builds a real sense of community, which I think makes learning more enjoyable (and a bit less daunting!).

I keep the sessions engaging by blending live calls with pre-recorded modules. You can work through the recorded material on your own, and then we come together for live Zoom calls to chat, answer questions, and bring everything together. This blended approach lets you learn at your own pace while still having that live, personal touch.

Pre-Recorded Training

If you prefer flexibility and want to go through the content on your own schedule, the pre-recorded sessions are ideal. With these, you can dive into the material whenever and wherever it suits you, and go back to any tricky parts as often as you need. I designed these courses with busy people in mind! So you can learn at your own pace and revisit bits until they click.

Pre-recorded training also has the benefit of consistency, so if you’re part of a team spread across different time zones or you’ve got a varied schedule, this option makes sure everyone gets the same high-quality content. Plus, it’s cost-effective for organisations that need frequent access to training or regular onboarding.

Which is Right for You?

Ultimately, it’s all about what works best for you. If you need flexibility, pre-recorded courses are a great choice. If you’re after real-time interaction and a bit of a boost from group discussions, live online training is right up your street. And if you’d rather meet in person and tackle your questions face-to-face, I also offer live in-person sessions.

I hope this gives you a clear picture of the options available. Whatever you choose, I’m here to support you if needed - you can reach out and book a call with me using the button below!

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Should the Data Governance Manager be Responsible for Implementing an MDM Tool?

This week, I'll be delivering a workshop on Master Data Management (MDM) at a conference, so this topic is top of mind for me right now and I thought I would quickly jot down some thoughts.

When you become known as the Data Governance manager or lead in your company, people might start thinking you're responsible for everything related to data. But that's not really the case.

So, when a company needs to choose a master data management (MDM) tool, should the Data Governance manager be in charge? Or is there a better way?

Balancing responsibilities 

Data Governance is vital to making an MDM project successful. If data quality problems aren't fixed first, even the best MDM tool won't work well. So, the Data Governance manager definitely needs to be involved. That’s a no-brainer.

However, I don’t believe that the Data Governance manager should be responsible for project managing the implementation of the MDM tool. Selecting and deploying an MDM tool is a big job that needs proper project management.. It makes more sense for a dedicated project manager to handle it, focusing entirely on getting the tool up and running.

The Data Governance team will have its hands full with its own important tasks, such as establishing and implementing Data Governance frameworks, defining and enforcing data quality rules and potentially cleansing data ahead of its migration into the MDM tool. These tasks are essential to ensure that the data managed by the MDM tool is consistent and reliable.

Effective collaboration between Data Governance and MDM teams is crucial

While the Data Governance team will be too busy to take on the whole MDM project, there still needs to be a degree of communication and collaboration between them and the MDM project manager. The Data Governance team brings expertise in data quality standards and the data governance framework, while the MDM project manager focuses on the technical and logistical aspects of the tool deployment. Below I have listed some practical tips on how to reach a level of collaboration between these two departments and their skill sets.

Practical Tips Towards Collaboration: 

1. Communication often and clearly -ensure that there is strong communication between the Data Governance team and the MDM project team. The Data Governance team should provide guidance on data standards, data quality metrics and governance policies to be followed during the MDM implementation.

2. Define the data - ensuring that you match and merge the correct data is key for a successful MDM implementation.  The Data Governance Team will be working with the Data Stewards and Data Owners to ensure that this is done for the data which is in scope for the MDM implementation.

3. Focus on data quality - the other main contribution of the Data Governance team will be to ensure that data quality is addressed. This involves defining data quality rules, monitoring data quality reports and resolving any data quality issues that arise.

4. Define roles and responsibilities - clearly define the roles and responsibilities within the project. The Data Governance team should focus on governance-related activities, liaising with Data Owner and Data Stewards,, while the MDM project team handles the technical and project management aspects of the tool’s implementation.

5. Encourage stakeholder engagement: The success of both Data Governance and MDM initiatives depends on active engagement and buy-in from stakeholders across the organisation. This requires communicating the benefits and importance of both initiatives effectively.

Well governed data sets the project up for success 

In short, while the Data Governance manager should not be responsible for the implementation of an MDM tool, their involvement is critical to make sure that the Data Governance principles are integrated into the project. This collaboration will help in achieving the ultimate goal of having high-quality, well governed data within the MDM tool, thereby maximising the return on investment for your organisation.

I hope you found this helpful. If you did, please help me reach as many people as possible by sharing this blog post on your socials. If you have any further questions or need to chat about something Data Governance related, feel free to book a call with me below.

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