Making Data Governance Work With, Not Against, Your Teams

Shifting Data Governance Left

I recently recorded a video with Paolo Platter, CTO and Co-founder of Agile Lab, discussing how organisations can embed data governance into their development processes to make it a true enabler.  It was a fascinating conversation and I’m thrilled that Paolo agreed to write a guest blog on the topic:

If you've been following data governance discussions lately, you'll have noticed a troubling pattern: despite significant investments in governance teams, data catalogs, and policy frameworks, organisations are still struggling with the same fundamental challenges. Poor data quality, compliance violations, and that persistent lack of trust in enterprise data.

Sound familiar? You're not alone.

The uncomfortable truth is that most data governance approaches are fundamentally broken—not because the concept is wrong, but because we've been implementing it backwards.

What Traditional Data Governance Gets Wrong

Let me start by addressing the elephant in the room: traditional data governance is reactive. We've built entire frameworks around fixing problems after they occur, rather than preventing them from happening in the first place.

Here's what this typically looks like in practice:

• Data gets created and pushed to production without proper metadata

• Data stewards scramble to document and classify assets after the fact

• Quality issues are discovered downstream when reports fail or decisions go wrong

• Data governance teams spend their time in endless catch-up mode, always one step behind

This approach creates what I call the "governance gap"—the dangerous space between when data is created and when it's properly governed. During this gap, data consumers lose trust, compliance risks multiply, and the entire data governance program starts to feel like an expensive afterthought.

The Knowledge Hand-off Problem

One of the biggest issues I see is the problematic knowledge transfer between domain experts, data engineers, and data stewards. Think about it: the person who best understands the business context of the data (the domain expert) isn't the same person building the data pipeline (the data engineer), who also isn't the same person responsible for cataloguing it (the data steward).

Each hand-off is an opportunity for critical context to get lost. By the time your data reaches production, much of its business meaning has been diluted or completely misunderstood.

Absolutely not ideal, is it?

Introducing Governance Shift Left: A Better Way Forward

Here's where things get interesting. What if instead of treating governance as a separate, downstream activity, we embedded it directly into the data engineering process from the very beginning?

This is the essence of Governance Shift Left—a proactive approach that integrates governance practices into the earliest stages of the data lifecycle, particularly during the software implementation phase when data pipelines are being built.

The concept isn't entirely new (software development has been "shifting left" on testing and security for years), but its application to data governance represents a fundamental paradigm shift.

The Four Pillars Of Governance Shift Left

Governance Shift Left is built on four core principles:

1. Lifecycle Alignment
Metadata, code, and data should follow the same development lifecycle. They're all part of the business value you're creating, so why manage them separately?

2. Ownership
Your data engineering team becomes directly accountable for compliance, not just data delivery. They adopt governance policies as part of their standard development process.

3. Policy as Code
Governance policies are no longer guidelines—they're automatically enforced through code and cannot be bypassed. This transforms abstract policies into concrete, executable rules.

4. Transparent Documentation
Policies should be documented, accessible, and self-explanatory. A good policy explains not just what to do, but why it exists and what the trade-offs are.

Why This Approach Works

When you align data documentation with the software development lifecycle, you can apply the same quality gates you use for code before it goes into production. The benefits compound quickly:

Improved Time to Market: No more waiting for separate governance teams to catch up with your data initiatives. Quality and compliance are built in from day one.

Reduced Manual Effort: Your data catalog automatically stays aligned with governance policies, eliminating the need for manual data entry and reducing errors.

Enhanced Trust: When data and metadata are created together and never fall out of sync, data consumers can rely on what they find in your catalog.

Lower Costs: Fewer manual checks, less rework, and reduced maintenance costs as quality issues are prevented rather than fixed.

Making It Practical: Data Contracts and Policy Automation

Two key enablers make Governance Shift Left practical rather than just theoretical:

Data Contracts serve as software-defined agreements that include technical schemas, business metadata, SLAs, and quality expectations. These become artefacts produced by your data teams, enabling governance enforcement at deployment time.

Policy as Code provides the ability to build automated quality gates for metadata and enforce them during your CI/CD process. These can be sophisticated—checking if semantics align with your business glossary or ensuring compliance with industry regulations.

Your Next Steps

If you're ready to move beyond reactive governance, here's what you should do:

1. Start Small: Identify one critical data pipeline and implement basic data contracts

2. Align Teams: Bring your data governance and engineering teams together to define policies that can be automated

3. Implement Quality Gates: Add metadata validation to your CI/CD pipeline

4. Measure Impact: Track the reduction in downstream quality issues and governance effort

The shift won't happen overnight, and you'll need buy-in from both technical and business stakeholders. But the alternative—continuing with reactive, resource-intensive governance—simply isn't sustainable as data volumes and complexity continue to grow.

The Bottom Line

Traditional data governance assumes that good governance happens to data after it's created. Governance Shift Left recognises that good governance happens with data as it's being created.

This isn't just about improving your governance program—it's about fundamentally changing how your organisation thinks about data responsibility and quality.

The question isn't whether you can afford to make this shift. It's whether you can afford not to.

Ready to explore how Governance Shift Left could work in your organisation? The principles are universal, but the implementation needs to fit your specific context and constraints. 

This article has been condensed and updated, and originally posted here: 👉Data Governance Framework: Governance Shift Left

CTO & Co-Founder of Witboost. Paolo explores emerging technologies, evaluates new concepts, and technological solutions, leading Operations and Architectures. He has been involved in very challenging Big Data projects with top enterprise companies. He's also a software mentor at the European Innovation Academy.

Data Engineering and Data Governance

Just a short blog this week, but one that addresses the alignment between data governance and other data roles. Of course, I am biased, but I have always believed that data governance can help the other data management disciplines be more successful. The rising prominence of data engineer roles across organisations presents an interesting question: how do these technical specialists integrate within an established data governance framework? The answer lies in recognising data engineers as data custodians rather than creating a separate data governance role. 

Understanding the Data Engineer Role

A data engineer is not a data governance role; it is a fundamentally technical role that exists whether or not you have data governance in place. Data engineers function as technical intermediaries who source, transform, and prepare data for analytical consumption. Their responsibilities span several critical dimensions:

Data Pipeline Architecture: Designing and building automated systems that move data from source systems to analytical platforms. This includes creating robust ETL (Extract, Transform, Load) processes that handle varying data volumes and formats whilst maintaining reliability.

Technical Transformation: Converting raw data into standardised, analysis-ready formats (including the increasingly popular data products). Data engineers apply business rules, handle data type conversions, and implement transformation logic that aligns technical outputs with analytical requirements.

System Integration: Connecting disparate data sources across the organisation. This involves working with APIs, databases, cloud platforms, and legacy systems to create unified data flows that support comprehensive analytics capabilities.

Quality Assurance: Implementing checks throughout data pipelines. Data engineers build monitoring systems that detect anomalies, track data lineage, and ensure data quality.

Performance Optimisation: Ensuring data processing operates efficiently at scale.

Infrastructure Management: Maintaining the technical environment that supports data operations

So you can see that a data engineer is a technical role, distinct from formal data governance roles. However, within the data governance structure, data engineers align naturally with the data custodian role through their operational responsibilities. Whilst most data governance roles are predominantly fulfilled by business stakeholders who understand data context and requirements, the data custodian role is often fulfilled by IT professionals

The data custodian role works well as data engineers are not the business stakeholders accountable for the data itself. Data engineers maintain and transform data according to business requirements without assuming ownership responsibilities. They are responsible for liaising with Data Owners to obtain permission for use of their data, ensuring appropriate authorisation throughout the data lifecycle.

Successful integration requires positioning data engineers within your existing data governance framework rather than treating them as separate entities. You need to make sure that their activities are aligned with your data governance framework. Agreeing that they are data custodians provides flexibility to accommodate the technical nature of data engineering work whilst ensuring alignment with your data governance objectives.

Effective data governance succeeds through role clarity and simplicity. Data engineers contribute most effectively when their technical capabilities operate within your established Data governance framework and considering them as data custodians is a simple and effective way to achieve this.

If you want a deeper insight into the data custodian role, I've previously explored this topic in this blog post.

If you'd like support aligning data engineer roles with your data governance framework, book a call with me using the button below.

What is a Data Governance Roadmap?

If you've been following me for a while, you already know that I frequently emphasise that Data Governance is an ongoing journey, not a finite project.  However, we are implementing a Data Governance framework and juggling numerous activities. It may not be a project, but we do need to plan and track our activities. So, we need to find a solution to planning which takes into account the longevity and flexibility of Data Governance initiatives without being too rigid or detailed. 

This is where the trusty Data Governance roadmap comes in. It’s a high-level approach that outlines our activities, priorities and rough timelines without bogging us down in the details. 

In this blog, I’ll cover what a Data Governance roadmap is (and isn't), why it's beneficial and how it can keep your Data Governance efforts on track and adaptable.

Firstly, What is a Data Governance Roadmap?

In simple terms, a Data Governance roadmap is a high-level plan which outlines the steps an organisation will take to effectively implement data governance. It lays out the high-level actions, timelines, and priorities needed to implement a Data Governance framework, i.e. it provides an overview of the “big picture”.  

Its purpose is to show the overall direction of travel, a high-level idea of what you are going to do and roughly when. They use broad phrases like “identify Data Owners” and “draft data definitions”.

They are useful to you as the Data Governance Manager for planning and tracking your activities, and also to the senior stakeholders and other teams who need the strategic overview of your initiative.

What it isn't

It’s important to mention that a Data Governance roadmap is not a project plan.  As mentioned at the start of this blog, Data Governance is not a project, so why would it need a project plan?

A project plan is very detailed, specifying exact tasks by specific dates. This approach is not well-suited to Data Governance, which requires flexibility in its implementation and is deeply rooted in cultural change, meaning progress can only move at the pace your business stakeholders are ready to embrace.

A project plan can also create the false impression that Data Governance is a one-time effort with a definite end date. As you are aware, Data Governance is ongoing and evolves over time. It’s carried out by many business users across your organisation who have their own agendas and priorities. Expecting them to strictly follow a detailed project plan is unrealistic.

So, does this mean we should not have any plan at all? Absolutely not. 

Not having a plan would lead to a lack of direction and progress. To avoid this, I recommend using a roadmap, which serves as a high-level plan, but is high enough level to easily adapt and evolve as required.

The Benefits of a Data Governance Roadmap

A roadmap allows us to articulate what we are doing, why we are doing it and roughly when we aim to complete it. For example, we might set a target to complete data discovery and create conceptual data models for five key business functions by the end of June. Following that, we might aim to identify and train data owners by the end of September. 

This approach gives us a flexible framework that can be adjusted as we engage with business users and refine our plans. In doing so, we're able to get a general idea of what's to come without being overwhelmed by unnecessary details and deadlines. And this is just one of the benefits of working with a Data Governance roadmap. They can help us in several other ways too, including: 

• Direction and Structure: It provides a clear sense of direction and structure for the Data Governance initiative, outlining key milestones and goals.

• Communication: It serves as a communication tool to share with senior executives and business users, helping them understand the initiative’s objectives and timelines.

• Engagement: It can be used to engage business users in agreeing on realistic target dates that align with their other responsibilities and priorities.

• Flexibility: It allows for adjustments based on feedback and changing circumstances, making it a more practical approach than a rigid project plan.

Your Data Governance Roadmap is a Strategic Planning Tool

A Data Governance roadmap is key to Data Governance success. It is a high-level guide that helps manage the initiative effectively while remaining adaptable to the evolving needs of the organisation. This ensures that Data Governance efforts are integrated and aligned with business objectives.

I hope this has clarified the concept and importance of a Data Governance roadmap for you. If you'd like further support with your Data Governance initiative, book a call with me below. 

Does Data Governance Coaching Really Work?

Data Governance can be really tough.

After 20+ years in the industry, I can honestly say it’s one of the most challenging areas to work in. The pressure comes from all angles - expectations from leadership, confusion among stakeholders, and the constant need to prove the value of what you’re doing. (That’s a whole blog post in itself but we’ll save that for another day!)

So if you’re sitting at your desk feeling stuck, wondering how on earth you’re supposed to make this work, firstly, you’re not alone.

But let’s be honest, sympathy from me won’t solve anything. 

You need something practical. And I know how overwhelming it can feel to sift through all the options, trying to figure out what might actually help and what’s just noise.

One option people often consider is coaching. But does it really work? And can it really help?

Coaching helps when it's grounded in real experience

The benefit of working with a coach is that they’ve probably been through what you’re going through.

And it’s incredibly valuable to have someone on your side who truly understands the challenges you’re facing. That way, you’re not just getting advice - you’re getting support, ideas, and reassurance from someone who gets the real-life pressures.

That’s why it’s so important to choose a coach who has actual experience in your field. In something as complex and misunderstood as Data Governance, you need someone who doesn’t just know the theory but has lived through the reality.

In my case, I’ve worked my way up in Data Governance from the ground level, and there are very few challenges I haven’t seen before. I always say, I’ve already made the mistakes, so you don’t have to.

Coaching helps when it builds confidence

There’s no single way to “do” Data Governance. Every organisation is different, shaped by its people, its culture, and its goals.

That’s why support isn’t just about getting things done. It’s about helping you feel more sure of yourself - especially when things feel messy or unclear. The right kind of coaching gives you space to think, test ideas, and talk things through without pressure.

Often, what people need most is confidence. Not just to understand the concepts, but to talk about them clearly with others, especially senior leaders. Coaching can help you feel more ready to speak up, explain your thinking, and trust your own judgement.

One person I worked with described coaching as having a “critical friend” - someone who listens, gives honest feedback, and helps you stay grounded when things are tough.

At Volkswagen Financial Services UK, one Data Governance lead said my coaching helped them feel more confident in new situations. Their Enterprise Data Governance Lead was working through new, unfamiliar situations and needed a way to sense-check their approach – to feel confident that the decisions they were making were not only right for the business, but aligned with good industry practice. They described our coaching sessions as a space where they could test ideas, tackle challenges, and get strategic input:

“It gives you real confidence that you are heading in the right direction, and you are doing the right things by your organisation, by the people in your organisation, and by your customers. And that you are doing the right thing with industry practice.”

That confidence filtered outwards. Data Governance began to feel less like a blocker and more like a tool for enabling change:

“Before I came into this role, I saw governance as a bit of a blocker... Nicola has enabled me to [see it differently] because she’s given me the confidence in myself. People now want to work with us to achieve and make what they need to make. I have really benefited from it.”

When people feel more confident, they don’t just feel better - they start to lead. And that’s when real change starts to happen.

Coaching helps when it fits your needs

There’s no shortage of theory out there – blogs, books, frameworks. But theory alone doesn’t get you unstuck when you’re faced with real-life challenges. The real question is: how do you turn all that knowledge into something that actually works for you and your organisation?

That’s where coaching comes in – not as a one-size-fits-all solution, but as flexible support tailored to what you need, when you need it.

When we work together, you can use our time in whatever way is most useful for you. That might mean:

• Asking questions about anything you’re grappling with

• Practising stakeholder conversations (I’m happy to play the awkward one!)

• Getting feedback on your documents or plans

• Having me join virtual meetings to quietly back your case

• Or using the time for me to review materials so you can focus on other priorities

And to make things even easier between sessions, you can also get access to my AI assistant.

It’s designed to help you navigate day-to-day data governance tasks – whether you want to sense-check your thinking, explore best practices, or get help writing a stakeholder summary. It’s like having a pocket version of me that’s available 24/7. It won’t replace our sessions, but it will help you keep momentum and feel supported in the moments when you’re working things through on your own.

All sessions are recorded, so you can revisit them anytime. And you can choose between six- or ten-hour packages to use flexibly over six months – no pressure, just support at your pace.

Data Governance can feel isolating – especially if you’re the only one leading it in your organisation. Coaching creates a space where you're not doing it alone. You’ve got someone in your corner who understands the challenges and wants to see you succeed. And if it helps to know you're not the only one figuring this out, I’m always happy to connect you with others facing similar situations.

Is Data Governance coaching worth it?

It’s a fair question – and the honest answer is: it depends.

Coaching isn’t a one-off fix. It’s not about dropping in, delivering a template, and disappearing. It’s a personalised journey – and no two journeys are the same.

Some people come to me feeling completely overwhelmed, asking “What do I do now?” Others already have the basics in place but want to fine-tune their approach, build buy-in, or gain the confidence to lead more strategically.

Wherever you’re starting from, I’ll meet you there.

My focus is on helping people succeed with Data Governance in a way that feels right for them – not just ticking boxes or copying what others are doing. Because that’s where real, lasting impact comes from.

When we work together, you’ll come away with:

• A clear vision of what you’re trying to achieve

• A practical action plan you can explain and stand behind

• Confidence that you're focusing on the right things

• And support to keep going – even when it gets tough

So, is it worth it? That really depends on how important success is to you.

But if you’re feeling unsure of your next step, isolated in your role, or struggling to get others on board, then yes, coaching could be the most valuable investment you make in your data work.

If you’d like any further help or information about coaching, take a look at my website coaching page, or, book a call with me below.

The Difference Between a Data Governance Policy and an Operating Model

When it comes to implementing Data Governance, a common question I get is…What’s the difference between a Data Governance policy and an operating model? It’s a great question because understanding the distinction between the two can make or break your Data Governance initiative. So, let’s take a closer look. 

What is a Data Governance Policy?

A Data Governance Policy is the “what” of your Data Governance approach. It’s a high-level document that outlines:  

- The principles your organisation will follow regarding Data Governance.  

- The responsibilities of key roles (without going into too much detail).  

- The objectives and expected outcomes of your Data Governance programme.  

Think of the policy as a guidebook, a clear and concise document that describes what you are going to do. It sets the tone and direction for your organisation’s Data Governance efforts.  

What is an Operating Model?

So, if we consider the policy as the “what”, then the Operating Model is the “how” of Data Governance. It includes:  

- Detailed processes and workflows for managing data.  

- Role descriptions and specific responsibilities.  

- Practical guidance on how Data Governance activities are executed.  

The Operating Model gets into the nitty-gritty of how your organisation will implement the principles outlined in the policy. It’s where the rubber meets the road. 

Why Keep Them Separate?

When I started working in Data Governance, I tried putting everything (both the “what” and the “how”) into one policy document. But here’s what I learned:  

1. Policies that are too long won’t be read - if your policy is overly detailed, it becomes a daunting document that nobody has the time (or inclination) to read.  

2. Approval processes can stall progress - policies usually require a lot of oversight and approvals, which can take weeks or even months. If your policy is jam-packed with details, every little update will need to go through the same exhaustive approval process.  

3. Flexibility is critical - Data Governance is iterative. As your initiative evolves, processes and responsibilities might change. If all those details are locked into your policy, making updates becomes a bureaucratic nightmare.  

By separating the high-level policy from the detailed Operating Model, you make it easier to get the policy approved and keep your program flexible and adaptable. While we are on the topic of operating models, some organisations love to use this term, but others can find it confusing or resist it entirely. If that’s the case, that’s fine. Instead of an Operating Model, you can use a different term, like “framework” or break the details into smaller, individual documents (e.g., process guides or role descriptions). The key is to make it easy for your organisation to accept and use these resources.  

A Final Word

Splitting your Data Governance Policy and Operating Model is not only about simplifying documents but also creating a governance framework that’s practical, flexible, and easy to implement and evolve as needed.. Remember, the policy sets the vision, while the operating model provides the tools to bring that vision to life.  

If this has helped you, leave a comment below and let me know your thoughts! Alternatively, if you have further questions, you can book a call with me using the button below. 

Prefer this content in video form? Click here to watch the video

Why do we Need a Simple Data Governance Framework?

Data Governance can be a complex field to work in and on a regular basis, it seems like there is always some sort of newbarrier to overcome, whether you’re a newcomer to the field or a veteran. Yet one thing I bet most Data Governance practitioners will have in common is the understanding that when it comes to being successful with your framework, simplicity is the way forward. And I can vouch for this too. 

In this blog, I want to break down why exactly simple is simply the best (Tina Turner pun intended!).

The Purpose of Data Governance

At its core, Data Governance is about enabling people in your organisation to use data more effectively. It helps them understand the data they have, assess its quality, and take the necessary actions to improve that quality if needed. The aim is to make sure data is working for the business - not the other way around.

But if we design a framework that is overly complicated, we run the risk of turning people off from using it altogether. Instead of engaging with Data Governance, they may see it as a burden that gets in the way of their daily work. And that’s the exact opposite of what we’re trying to achieve.

Complexity Creates Barriers

Let’s face it,no one wants to deal with unnecessary complexity, especially when they’re already juggling a hundred other tasks. If your Data Governance framework feels like a set of obstacles that makes accessing or using data more difficult, people will naturally resist it. Even if you’re telling them that it will make their work easier in the future, it’s hard to convince them if the framework feels like just another layer of complication.

We’ve all been there, haven’t we? We’ve encountered processes that are so complicated they just seem to make life harder rather than easier. When that happens, people tend to disengage, and they’ll stick to what they know instead of adopting new practices - even if those new practices would benefit them in the long run.

Think About it in Terms of Watching TV

To explain this further, I like to use an analogy that most people can relate to - watching television.

Years ago, when I was a child, things were simple. You had terrestrial TV with a few channels and after dinner, my family would gather around, check the Radio Times, and decide what to watch from a limited selection. We all watched the same show together and it was easy.

Fast forward to today, and watching TV is anything but simple! You’ve got cable, satellite, streaming services like Netflix, Amazon Prime, and Disney+, and you can watch on multiple devices. The choices are endless, and sometimes it feels overwhelming.

Earlier this year, I had a rare opportunity to watch something on my own, without my husband. I thought I’d pick out a nice costume drama, but it didn’t go as planned. I spent 45 minutes scrolling through multiple platforms and still couldn’t decide what to watch. In the end, it felt so complicated that I gave up, made a cup of tea, and read a book instead.

Now, imagine that same level of frustration amplified in a work environment. If you present someone with an overly complex Data Governance framework, they’ll feel just like I did when trying to pick something to watch. They’ll give up and move on to more urgent tasks because, even if the framework could help them in the future, they can’t see past the immediate complexity.

Simplicity Is Key

So what’s the solution? Keep your Data Governance framework simple. Start with a framework that’s easy to understand and easy to adopt. The goal is to make it clear, concise, and not intimidating. If people can grasp it quickly, they’re much more likely to engage with it and see its value.

Simplicity doesn’t mean you won’t be able to build on the framework later on. You absolutely can add more detail as your organisation becomes more comfortable with it. But the starting point should always be straightforward. By doing this, you make Data Governance feel like something that helps people do their jobs better, not something that gets in the way.

At the end of the day, the reason we implement Data Governance is to deliver real value to the business. It’s about making data a more valuable asset, making it easier to access, understand, and improve the quality of that data where needed. If the framework is too complicated, people won’t see it as a tool to help them - they’ll see it as a burden.

Final Thoughts

Simplicity is more powerful than complexity in a lot of ways, and this includes Data Governance. When frameworks are simple, people are more willing to adopt them, which leads to better engagement, better data management, and ultimately better business outcomes. Remember, the goal is to help the business, not to create more work.

So, if you’re designing a Data Governance framework, always keep simplicity in mind. Start small, keep it clear, and watch as your business begins to truly see the value of good Data Governance.

If you'd like support implementing an effective Data Governance framework in your organisation, book a call with me below.

Data Governance on a Shoestring Budget

There’s no denying it. The demand for Data Governance is growing fast. Whether it’s because of changing regulations, technology, or economic pressures, more organisations are finally realising they need to get serious about their data. But there’s a problem…

Budgets are tight. People want Data Governance, but they don’t always have the time, team, or money to implement it properly. So how do you move forward when the pressure is high but the resources are low? You take a minimal approach.

What is Minimal Data Governance?

So, first of all, let’s clear something up. “Minimal” doesn’t mean careless or incomplete. It’s not about doing the bare minimum to keep the regulator off your back or to tick a box in a report. Minimal Data Governance means being focused and deliberate about what you're doing and why you're doing it, so that even a small initiative delivers visible value.

You start with a narrow scope. You choose just one area or one goal to tackle. Maybe it’s improving the quality of customer data, or making financial reports more reliable, or helping the business meet compliance rules. Whatever it is, it should matter to your organisation right now. That one focused goal becomes your anchor. It helps you design a framework that works, even on a budget.

Does “Minimal” mean less time and effort?

Unfortunately not! Whether your scope is large or small, setting up Data Governance still takes time and attention. If you try to rush it or cut corners, you won’t get the results and that can hurt your reputation and your chances of getting support for future work.

But here’s the good news: if you do minimal Data Governance well, it becomes your foundation. Something you can build on over time and that proves the value of Data Governance to your stakeholders. You don’t need to do everything at once. You just need to do the first thing right.

You don’t have to do it alone

If you're wondering how to get started with a limited budget or team, remember: you don’t have to go it alone. That’s why I offer flexible coaching options that help you do Data Governance in a practical, structured way without wasting time or money.

The first option is Coaching, which gives you one call per month for six months. Each session lasts one hour and is recorded so you can refer back to it. You're welcome to bring along team members, and you can also use a session for stakeholder discussions, reviewing documents, or getting help implementing your framework. You’ll also receive my detailed Data Governance Checklist - a practical tool to help you keep track of what you’ve covered and what still needs attention.

The second, most popular, option is Coaching Plus. This gives you ten one-hour sessions to use at any time over a six-month period, offering greater flexibility if your needs vary from month to month. Like the standard coaching option, the sessions are recorded and team members can join in. You can use the sessions however you like, from strategic planning to stakeholder engagement. In addition to the Data Governance Checklist, this option includes a licence for my online Data Governance training course. There is also the option to add on access to my Data Governance-specific AI assistant, trained on my own 20+ years of experience and knowledge.

Both packages are designed to help you focus your efforts and adapt as your initiative evolves without overcommitting your time or your budget. Take Volkswagen Financial Services UK, for example. They chose the Coaching Plus package to guide their Data Governance work over six months. Their lead explained how valuable it was to have someone to check in with, saying it gave them confidence they were doing the right thing—not just for the business, but for the people and customers too. With a modest amount of support, they were able to avoid common pitfalls, sense-check their approach, and move forward with clarity. That’s exactly what minimal, focused support should offer. 

Start small, think long-term

The trick to Data Governance on a shoestring is focus. Choose one benefit that matters, design a simple, strong approach to deliver it, and make sure what you build today can be reused and scaled tomorrow.

And, you don’t need a big budget to get started. You just need a clear goal, a smart plan - and maybe a little help from someone who’s done it before!

If you’d like any further help or information about coaching, take a look at my website coaching page. 
Or, check out this case study on coaching to see how it’s benefited my clients.

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.

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.