Getting the data fundamentals right at the Ministry of Justice

Over the past couple of years I have run a number of Data Governance Training sessions to help the Data Improvement Team at the Ministry of Justice as they embarked on their Data Improvement programme.

So, I’m thrilled that today’s blog is a guest post by Sarah Blake, Deputy Director of Data Improvement at the Ministry of Justice sharing their progress to date and plans going forward:

The Ministry of Justice (MoJ) launched its Data Strategy in August 2022 and since then we’ve been making progress against the goals we set out. We’re also looking to the future - planning our roadmap for the next three years to deliver critical data improvement work and understanding how we will monitor and measure our progress.

In this post we’ll look at our work in the MoJ, and in a future blog we’ll share plans for data improvement in the wider criminal justice system.

Better data and skills to enable better decision-making

Data underpins our work in delivering justice outcomes. It helps us measure the impact of policy interventions, gives us operational insight into prisons and probation, and deliver better services for our users – along with so much more. Yet too often, our data is fragmented, hard to share and not exploited to its fullest extent. 

The Data Improvement team is focused on improving the quality of data, access to data and the data skills of staff, so that MoJ and the wider criminal justice system will make better decisions based on data and improve the outcomes for the millions of people that rely on the justice system. 

We are creating the foundations for our colleagues – in data science, data linking, analysis, operations and more – to be able to deliver the data-driven insight the MoJ relies on. 

Our three-year strategy 

Our roadmap outlines our data improvement strategy for the next three years. As with any roadmap, we have most confidence about the activity that’s coming up in the near future, and our work in the next few months will inform our work over the coming years. 

Phase 1 – Prototyping

Following on from our discovery work, we’ve been developing prototypes of processes and tools to improve data quality, access to data and data skills. For the next few months, we’ll continue to test these solutions with users and iterate them. We will:

• Test our data management policies (including data quality, data ownership, data standards and data cataloguing standards) by initially selecting one dataset in MoJ and working to measure and improve the quality of that data.

• Support the Electronic Monitoring team to access and collect high quality data and feeding data requirements into new systems. Embed data management principles including ways to manage data issues through improvements to process and governance. You can read the full Electronic Monitoring data improvement plan here. 

• Improve our strategic approach to data architecture across MoJ by hiring data architects. 

• Put in place a new MoJ Data Board that will report to our Executive Committee and ensure we’re getting senior stakeholder input into our work. 

• Work with Digital teams and Data and Analysis colleagues to refine the process by which data user needs are considered for new projects and changes to existing products, and improve the quality and reliability of data that analysts have access to.

• Test our new approach to publishing statistics in a more efficient way and which better meets user needs, and putting it into production on two publications.

• Work with Learning & Development teams to improve data skills for two cohorts within MoJ and support that with cultural nudges and interventions, further testing our data proficiencies tooling and our data culture framework. 

Phase 3 – Scaling by empowering others

Once we have confidence in our approach and our skills, we can support other teams to lead within their own area.  We can enable them to use their subject matter expertise and our processes and tools, alongside our advisory and consultancy support, to solve problems for themselves. We expect this phase to start in mid-2025.

Talk to us

Getting the fundamentals right in data is an important topic and there are growing number of government teams working on these complex issues. If you’re looking into any of these issues or solutions, please get in touch so we can continue to collaborate across the criminal justice system and public sector, and share lessons learned. 

To deliver this ambitious programme, we’ll need passionate data professionals to join our growing team. If you’re interested in working with us to solve some knotty problems, keep an eye on Civil Service Jobs or email us for an informal chat.

Guest blog written by Sarah Blake – Deputy Director, Ministry of Justice (30 January 2024).

Who Should Be Involved in Your Data Governance Framework?

When designing your data governance framework, a mistake I often see is that not enough consideration is given to who should be involved in the process. This can lead to issues and less than smooth implementation of your Data Governance Framework.

So, I thought I'd take the opportunity to explain in this article just some of the important roles of the people who should be involved in the design and implementation of your organisation's data governance framework.

Data Owners 

It's perhaps unsurprising that data owners are the first role I'm including as part of the team involved in designing a data governance framework. In fact, they are one of the most important roles. Whether you're doing much work in their area initially or not, it's crucial to keep them up to speed on what you're doing and how things are progressing. 

A good way to do this is by setting up a data governance committee or forum. This allows you to involve your data owners, engage them, keep them updated and get their input into what will and won’t work.  You can find more information about Data Governance Forums here.

Project Manager

Next is the all-important project manager. Whether you have the luxury of a formal project manager or someone chosen from your team, you need someone who is responsible for ensuring that your initiative is planned, monitored and reported on appropriately.

If you don't have a dedicated project manager and are instead looking to utilise someone already in your team, consider your data governance manager. 

Now, me mentioning this role may of course cause confusion as I repeatedly state that Data Governance is not a project and I would urge you not to call it that.  However, at the start of your Data Governance initiative, it is likely that you will need a “project like” phase with tasks and target dates needing to be planned and managed and those are tasks handled with expertise by a Project Manager.

Project Data Governance Manager 

You might be wondering why I have made this a separate heading when I was just talking about it in the above role. And the reason is that while utilising your data governance manager as your overall project manager can be a great idea, it must be noted that the two roles require very different skill sets. 

A project manager's role is to design and implement a framework. Therefore, they will need skills such as stakeholder management and change management which require impeccable communication and persuasion skills. 

On the other hand, your day-to-day data governance manager has skills such as carrying out routines, monitoring and reporting on the framework and often they lead the data quality team too. 

If your data governance manager is to also be your project manager then you must ensure they also have project management skills. Sometimes it is the case that a person will thrive in both roles. However, if you don't have the right person for this then you can utilise two separate people from your team to carry out the two different jobs. Just make sure you get your business's usual data governance manager involved at the right stage of implementation for a smooth transition. 

Business Analyst 

Having business analysts involved is invaluable in helping to understand what the current state of affairs is and provide valuable insight into the current state of data at your organisation. Their input will help you design and implement the framework as well as make a start on documenting what data you have where. 

Enterprise Data Architect 

An enterprise data architect, or any type of data architect really, can offer brilliant support to your initiative as they have a bank of useful information and knowledge about your organisation, which can help with influencing stakeholders and identifying problem areas with data. This role does not need to support your initiative on a full time basis but will definitely prove useful in those initial stages where you find yourself needing to persuade others of the work you are doing. 

Final thoughts 

Overall, when it comes to the people working on your data governance framework it's important to realise that there are other people, not just the business as usual roles of Data Owners and Data Stewards, which you should consider involving. That's not to say that the people already on your team don't have these skills, and if they do then by getting them involved in a way which utilises such skills is likely to make your Data Governance initiative more successful.

So, keep an open mind, look out for support and input from anyone expressing enthusiasm about the initiative. Pay attention to who you're getting involved and when to ensure success! 

If you want to find out how I can help you design and implement a Data Governance Framework successfully please book a call with me using the button below.

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Data Governance Interview With Justin York

I thought it was time that I interviewed Justin York for my blog. I have known Justin for many years and he was my first Data Governance Coach Associate. You’ve possibly seen guest posts he has written in the past or seen him delivering Data Literacy courses for me, so I thought it was time to ask him to share how he got into Data Governance and to share some insights from his many years of Data Governance experience.

After a long military career in army logistics, with the final 15 years working in Data Management, Justin moved into consulting and eventually moved more into Data Governance. Justin has since worked in a myriad of businesses all with different challenges. Justin is also a qualified coach and said he enjoys the engagement with people whether on a contract or simply in day-to-day life.

How long have you been working in Data Governance?

I have been working in Data Governance formally (under that title) for around 16 years, however, I have been engaged with the ownership of data and the decision-makers for around 20 years or so.

Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

I have worked for a long time in the realm of information/Data Management and while employed on a contract at the UK Ministry of Defence met Nicola Askham and at that point realised that much of the work that I have been doing could loosely be called Data Governance.

The real realisation was that all the challenges that we face around data come under people and whether that’s Data Management or Data Governance you need to get them to understand what you need from them and why. So working under the Data Governance umbrella seemed the most logical step.

What characteristics do you have that make you successful at Data Governance and why?

Primarily I enjoy the engagement with people and the vast majority of the challenges involved in Data Governance spring from people after all people touch the data at all points of its journey and systems generally (notwithstanding the magic of AI) do what their human masters instructed them to do so if there’s a fault that was generated by a human.

So my key skills are engagement with and fast understanding of people and what makes them tick, coupled with excellent levels of patience and the ability to communicate across all levels and explain just what Data Governance is, its benefits and the way that people can engage.

Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

There are so many books out there and all sorts of models and to be honest, while I keep up with the latest trends I don’t tend to read a lot of books and to be fair many of them either reiterate the same material with a different name or refer to well-established materials. I use the DAMA DMBoK as a useful reference and then I typically use my own experience and adapt that to individual organisations’ needs where I work.

What is the biggest challenge you have ever faced in a Data Governance implementation?

Buy in from the management or the people on the ground, Data Governance initiatives to many are just another fad that will pass with time and so with their busy existences, they try to ignore it and get on with their busy jobs. However, they fail to understand what the benefits are such as giving them more time back to do more in their busy jobs, so I think it’s turning the supertanker of suspicion is the biggest challenge.

Is there a company or industry you would particularly like to help implement Data Governance for and why?

I have a particular interest in aviation so I would like to get some work in an airline or manufacturer or space.

What single piece of advice would you give someone just starting out in Data Governance?

Expect the journey to be difficult and challenging because people generally won’t welcome you with open arms.

Finally, I wondered if you could share a memorable data governance experience (either humorous or challenging)?

There are two:

• While working at a financial services organisation on director was quite challenging and dismissive of the Data Governance work and would not engage. Eventually, we had a couple of options, one was to go over his head which may create additional friction and the other was that we recognized that his data was not critical to the project so we simply sidelined him.  The strange thing was that when he was sidelined and the rest of the organisation kept moving forward he wanted to be involved rather than be left out.

• On a different contract I was faced with a member of the management who stated “We don´t need Data Governance as we already do it”, so I asked him to describe what it was that he did in that line and he replied, “well its what we do”, the defence rests!

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Defining Data Definitions and How to Write Them

Have you ever stopped to wonder what a "data definition" actually is? It's one of those terms that we often toss around in the world of Data Governance, but it's surprising that until now, no one has actually asked me to break it down. When I got an email with this query, I had a bit of an "Aha!" moment. I thought to myself, "Surely, I must have tackled this topic ages ago," but guess what? I hadn't!

Now, "data definitions" might sound a tad on the technical side, but they're an essential piece of the Data Governance puzzle. You might be wondering, "Why didn't this person just Google it?" Well, let me tell you, I did. I braved the labyrinth of Google search results, and honestly, I wouldn't recommend it. It spat out some super technical gibberish, like, "a data definition is the origin of a field that references a data domain and determines the data type and the format of data entry."

The reason these Google results—and others like them—are about as clear as mud is that they're designed to describe data definitions in the context of something called a "data dictionary." But data dictionaries are all about the nitty-gritty technical stuff—like where data lives in a database and its techy constraints. It's not exactly the thrilling stuff business users are itching to know.

So, when we talk about data definitions in the Data Governance realm, we're not diving headfirst into the deep end of tech talk. We're all about making data accessible to the people in your business who need to use it and gain insight from it. We're talking about the entries that populate your data glossary or data catalogue.

Ever notice that in organisations, people often throw around the same terms, but their interpretations can be like comparing apples and oranges? That's where data definitions come to the rescue. We're here to extract those varying interpretations, decide on one common definition, document them, and get everyone on the same page because when stakeholders aren't on the same wavelength, you end up with reports and decisions that are about as reliable as a chocolate teapot.

Now, some data terms are like old friends—easy to define, and everyone's on board. Think "date of birth," "first name," and "last name" in systems with personal info. You could define those in your sleep, and everyone in the organisation would nod in agreement. But then you dive into the murkier waters of terms like "customer…” that's when things can get a bit iffy. What does "customer" mean to you? And what about Bob from accounting? His definition might be worlds apart.

So, the name of the data definitions game is making sure your organisation understands its data inside and out. A big chunk of that process involves pulling those data definitions out of people's heads, getting them down on paper, and achieving a group thumbs-up on what these terms really mean.

Now, I know you might be thinking, "Crafting data definitions sounds like a colossal headache!" But trust me, it's not rocket science. When I talk about a data definition, I'm simply talking about a short, sweet phrase or a couple of sentences that lay out what an item is and what it's all about. No need to overcomplicate things.

Here's a trick I use with my clients: I ask myself, "Could someone who knows nothing about this organisation and its inner workings read this definition and get it?" If the answer is a resounding "yes," then you've nailed it.

Now, here are some practical tips for fantastic data definitions:

• Keep It Simple: Your definitions should be clear and straightforward. No need for data Shakespeare here—simplicity is your friend.

• Plain Language: Avoid techy talk and opt for plain language that even your grandma could understand.

• Stay Objective: Write definitions from a neutral standpoint. Ditch the department-specific biases.

• Team Effort: Get the relevant people in on the definition action. Consensus is the name of the game.

• Put It to the Test: Before you pop the champagne, test your definitions with non-experts. If they scratch their heads, it's back to the drawing board.

• Amp Up the Info: Consider adding extra tidbits like data lineage and usage to your definitions.

In a nutshell, data definitions are the unsung heroes of Data Governance. They bridge the gap between IT and a diverse range of stakeholders.

I hope that was helpful and don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or you’d like to know more about how I can help you and your organisation then please book a call using the button below.

Data Governance 2023 Round-Up

Welcome to 2024!

Happy New Year and welcome to 2024!

As we bid farewell to the past year and step into the new one, I want to extend my warmest wishes to all of my readers and network. May this year bring you success, joy, and countless opportunities for growth. 

The arrival of a new year symbolises a fresh start - a clean slate to set new goals and pursue new ambitions.

To kickstart your Data Governance journey in 2024, I've compiled a list of the top ten most popular blogs from 2023. These blogs cover a wide range of topics, providing insights, tips, and best practices to guide you through the intricacies of Data Governance. Whether you're a seasoned professional or just beginning to explore this field, there's something for everyone in this curated collection.

1. How to identify Data Owners, where multiple areas of the organisation use the same data?

2. How the COM-B Model for behaviour change can be used when implementing Data Governance

3. The 7 Potential Benefits of Having a Data Glossary or Data Catalogue

4. The First Six Months of Your New Data Governance Initiative

5. What is a Data Office?

6. What are the key components of a data culture?

7. Data Governance Interview with ChatGPT

8. How to Certify a Report through Data Governance: Importance and Best Practices

9. Data Management Disciplines - Separate Specialities or Better Together?

10. Who owns the Data in a Data Warehouse?

As we step into the promising realm of 2024, let's embrace the opportunities it brings for personal and professional growth. 

Here's to a year of data-driven success and innovation - Happy New Year!

If you would like support with your Data Governance initiative make sure to check out the training options I have coming up in 2024 or if you would like to discuss any of your Data Governance needs book a call. 

Data Governance Interview with Rini Choudhury

Rini Choudhury is the Data Governance Lead at Haleon, bringing with her over 8 years of experience in the field of Data Governance. I am thrilled to share her insightful interview with you.

Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

 I embarked on my career as a database developer with boundless enthusiasm, eager to delve into coding, create innovative applications, and master various programming languages. However, I soon recognized a critical gap in my approach – I was solely focused on solving immediate challenges without considering the broader context.

The recurring challenges of acquiring timely and reliable data became increasingly apparent. These data issues posed significant obstacles to our work, leading me to question the overall quality and effectiveness of our data-driven solutions. It was at this point that I realized the importance of understanding the bigger picture.

Motivated by the desire to address core data problems and their far-reaching consequences, I transitioned into a role where I could delve deeper into data-related issues. This journey led me to appreciate the critical need for data governance, a fundamental discipline for ensuring data's accuracy, accessibility, and usefulness within an organization.

What characteristics do you have that make you successful at Data Governance and why?

 Patience of a saint 😊, Persistence and ability to not get frustrated with zillion followups.

Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

Data Governance for Dummies

What is the biggest challenge you have ever faced in a Data Governance implementation?

Shifting people's perspectives and securing their commitment to a larger vision, particularly when immediate benefits aren't readily apparent, can be a formidable task. This challenge often arises in contexts like Data Governance initiatives, where the true impact may only become evident in the long run.

Is there a company or industry you would particularly like to help implement Data Governance for and why?

I have a strong desire to assist the healthcare industry in implementing effective Data Governance practices. This choice is driven by the profound impact that data can have on people's lives within this sector. Healthcare relies heavily on accurate and timely data for patient care, medical research, and public health initiatives.

By helping the healthcare industry establish robust Data Governance, we can ensure the quality, privacy, and security of patient data. This, in turn, can lead to improved patient outcomes, more efficient healthcare processes, and enhanced medical research efforts. Ultimately, it's a field where data governance can directly contribute to saving lives and improving the overall well-being of individuals, making it a particularly compelling and rewarding area to focus on.

What single piece of advice would you give someone just starting out in Data Governance?

Have patience, don’t give up and keep trying until you succeed. If you're facing challenges, remember, you're not alone. We've all encountered obstacles on our journey, so seek out supportive communities that can offer guidance, as they've likely experienced similar if not identical challenges.

Finally, I wondered if you could share a memorable data governance experience (either humorous or challenging)?

While the idea of ownership is often thrilling, becoming a data owner doesn't typically generate the same level of excitement. To illustrate, if I were to offer a car, complete with maintenance and upkeep responsibilities, many would eagerly embrace the opportunity to own it. However, when posed with the question of who would like to take ownership of a dataset, the enthusiasm tends to wane. This observation has taught me a valuable lesson: people are often uncertain about whether they should assume ownership of data.

Navigating Data Mesh and Evolving Data Governance: A Practical Guide

In my previous blog, I examined the concept of Data Mesh and its relationship with Data Governance. Now, let's take a closer look at practical insights and strategies for successfully navigating the complexities of Data Mesh while evolving our approach to Data Governance.

Strategies for Integrating Data Governance with Data Mesh

Imagine standing at the intersection of two parallel roads: Data Mesh and Data Governance. How do you ensure a smooth transition from one road to another without causing traffic chaos? The answer lies in a thoughtful strategy that integrates the principles of Data Governance with the unique demands of Data Mesh.

1. Embrace the Complexity: Data Mesh challenges the notion of a centralised data warehouse or lake. It advocates for a decentralised architecture where data products are accessible through APIs and various systems. This complexity demands an amend to your data governance approach so that it supports data democratisation while maintaining quality and consistency.

2. Define Clear Roles and Responsibilities: The emergence of data product owners and development teams introduces new roles to the Data Governance landscape. While traditional roles like data owners and data custodians remain vital, these new roles must align seamlessly to ensure effective governance across decentralised data resources.

3. Cultivate a Data-First Mindset: Data Mesh isn't just a technology trend; it's a cultural shift that requires everyone in the organisation to adopt a data-first mindset. Data Governance should promote collaboration between business units, data professionals, and IT teams to ensure that data products are valuable, understandable, and compliant with quality standards.

Balancing Complexity with Simplicity: Data Product Principles

The democratisation of data products within the context of Data Mesh poses both opportunities and challenges. How do you strike the right balance between making data accessible and ensuring its quality and usability? The answer lies in a set of core principles that define what constitutes a data product:

1. Accessibility: Data products should be available in various formats, catering to different user needs. This accessibility ensures that users across the organisation can easily access and utilise the data.

2. Understandability: Documentation and clear definitions are crucial. Users should be able to understand what each data product contains, how it can be used, and relevant examples that demonstrate its value.

3. Discoverability: Data products must be easily discoverable. Organisations need a data catalogue or glossary that enables users to locate and access relevant data products effortlessly. 

4. Interoperability: Data products should be designed to work well with other datasets, fostering a collaborative environment where various data products can be combined to generate valuable insights.

5. Trustworthiness: Data quality is paramount. Data products must adhere to defined data quality standards, ensuring that users can rely on the accuracy and integrity of the data they're accessing.

Evolving Data Governance in the Data Mesh Era

Data Governance isn't static; it's a living process that evolves to meet the demands of changing data landscapes. In the context of Data Mesh, this evolution takes on new dimensions:

1. Flexibility in Roles and Responsibilities: While the traditional Data Governance roles remain essential, the advent of data product owners and development teams introduces a layer of complexity. Organisations must be willing to iterate and adjust responsibilities to ensure effective governance and minimise conflicts.

2. Holistic Data Ownership: Data ownership gains even more significance in the Data Mesh paradigm. As data products span multiple domains and applications, having a holistic data owner is vital to ensure consistent decision-making and accountability.

3. Continuous Adaptation: Data Mesh isn't a one-size-fits-all solution. Expect the unexpected and be prepared to refine your data governance approach as you gain insights from real-world implementations. Flexibility and adaptability will be your allies.

Final Thoughts

Navigating the intersection of Data Mesh and Data Governance requires a delicate balance between complexity and simplicity. The democratisation of data through data products empowers organisations with valuable insights, but this must be paired with robust governance to ensure that data remains trustworthy and usable.

As the journey continues, keep in mind that Data Mesh isn't a static destination; it's a dynamic evolution that demands openness, collaboration, and the willingness to learn from successes and challenges alike. By embracing the principles of data product creation and adapting your data governance approach, you'll be better equipped to harness the potential of Data Mesh and drive meaningful business outcomes.

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Understanding the Basics of Data Mesh and its Impact on Data Governance

In the rapidly evolving landscape of data management, a term has emerged that is simultaneously intriguing and confusing: Data Mesh. If you find yourself puzzled by this concept and its implications for, you're not alone.

Many of us in the data realm have been grappling with the question: what exactly is a Data Mesh, and how does it impact our approach to Data Governance?

Imagine encountering a new client who casually drops the bombshell that they're embarking on a Data Mesh journey and expect you to oversee Data Governance for it. Panic might set in, as you realize that while you've heard of Data Mesh, you're not entirely certain how it impacts Data Governance.

In this blog, we'll dive into the basics of Data Mesh and explore its intersection with Data Governance.

Unravelling the Mystery of Data Mesh

Data Mesh isn't just another technological marvel, like the migration of data to the cloud that prompted a flurry of questions about Data Governance changes a few years ago. Data Mesh concept encompasses more than a fresh technology stack or a novel infrastructure. It's about a distributed architecture that breaks away from the traditional data warehouse or lake model. Instead, it envisions data as a decentralised resource, accessible through various APIs and systems.

The crux lies in the shift in mindset that Data Mesh demands. It's not just about IT delivering solutions; it's a cultural change that invites all stakeholders to think differently about data ownership and accessibility.

While previous data warehouses and lakes could operate without airtight Data Governance (albeit suboptimally), the same isn't true for Data Mesh. It hinges on a cultural revolution where data becomes the shared asset of the entire business, requiring robust governance to maintain its integrity and usability.

The Democratisation of Data: Introducing Data Products

At the heart of Data Mesh lies a fundamental shift in how we perceive data's value and accessibility. The term "democratisation of data" is more than a catchy phrase; it's a philosophy that shapes how we approach data products. Data products aren't massive data dumps; they're finely curated, bite-sized datasets that hold value on their own. These products are designed to be easily accessible and usable by a wide range of users across the organisation.

The concept of a data product may sound straightforward, but its implementation requires careful consideration. Not all data is meant to be a data product. The criteria for turning data into a data product hinge on its accessibility, understandability, discoverability, interoperability, and trustworthiness. By adhering to these principles, organisations can ensure that their data products are valuable, usable, and ultimately contribute to the democratisation of data.

Adapting Data Governance for Data Mesh

As we explore the intricacies of Data Mesh, the question of Data Governance looms larger. How does Data Governance need to evolve to accommodate this new paradigm? The first step is acknowledging that a one-size-fits-all Data Governance framework won't suffice. While a standardised framework can offer inspiration, each organisation's unique culture and challenges necessitate a tailored approach.

Roles and responsibilities play a pivotal role in Data Governance, and Data Mesh introduces some new players. The introduction of data product owners and data product development teams raises questions about the role of traditional data owners and data stewards. The evolution of Data Governance in the Data Mesh era involves reconciling these roles, ensuring that data ownership and stewardship align with the demands of democratised, decentralised data.

In conclusion, the confluence of Data Mesh and Data Governance represents a transformational shift in how we manage and utilise data. Data Mesh isn't just about technology; it's a cultural and architectural evolution that necessitates rethinking our Data Governance strategies. By embracing the democratisation of data and adapting our governance practices, we can navigate the complexities of Data Mesh and harness its potential for enhanced data usability and value.

Stay tuned for my next blog, where I'll delve deeper into the practical implications of Data Mesh on Data Governance and share valuable insights for successfully navigating this dynamic landscape.

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