Cost Versus Value of Data Governance Coaching

I often get asked for free advice and I truly wish had enough time to help everyone who asks but unfortunately, Data Governance Coaching is not free.  It can, however, add massive value to your organisation.

In fact, coaching, in general, is becoming a well-known asset to the overall success of organisations. The Institute of Coaching research tells us that over 70% of individuals who receive coaching benefit from improved work performance, relationships, and more effective communication skills. All massive pluses when it comes to implementing a successful Data Governance initiative.

So, I want to look explore the ways Data Governance Coaching can bring value to you and your organisation.

Let's get started.

Advice from someone who has been there and done that

A huge benefit of working with me or one of my coaches is that we have almost definitely experienced the same kind of challenges and hurdles of setting up a Data Governance initiative as you are facing.

For example, if you are struggling to articulate what Data Governance means to your key stakeholders and your organisation, then you may fail to convince them to support your Data Governance initiative. If you fail to gain stakeholder buy-in, it is unlikely the initiative will deliver much value, the Data Governance initiative may fail and you could be blamed.

I get it. I have been there. Having support means we can work through it together and you can benefit from the mistakes I've made in the past. I always say – I've already made the mistakes so you don’t have to!

Coaching goes beyond theory

Working with a coach gives you more than just theoretical knowledge - a Data Governance coach can support you in developing actionable steps to help you with YOUR Data Governance initiatives specifically. Factorial uses the well known saying to summarise just this; give a man a fish and you feed him for a day. Teach a man to fish, and you feed him for a lifetime. Good coaching won't necessarily give you the answers straight up, but a good coach will support you as you work through your Data Governance challenges.

You’ll gain confidence in your own Data Governance Initiative

It’s one thing thinking or hoping you’re doing the right thing - it’s another to know it! And to have the confidence to sell it to senior stakeholders.

Good coaching will give you the boost of confidence you need to help your Data Governance initiative succeed. You’ll develop the skills and knowledge needed to implement effective strategies that align with your organisation's objectives.

Zavvy calls this 'employee empowerment' and states that it brings more confidence in an individual's abilities, allowing them to think outside the box when it comes to problem solving and taking the initiative to improve processes. This can be vital when dealing with unconvinced stakeholders.

Many of my clients state this to be one of the biggest benefits from doing coaching and have called me “their critical friend”.

Going it alone can feel lonely!

Data Governance can feel isolating, especially if you’re brand new to it or the only person in your organisation whose remit it is. Participating in coaching sessions connects you with like-minded professionals, giving you the support you need for ongoing collaboration and idea exchange.

Is coaching worth it?

Ultimately, the answer to this depends on what you want to accomplish and the value you place on accomplishing it.

As someone who has worked from the ground up in Data Governance, I can say that there is no such thing as a standard approach to Data Governance. It's different for every organisation. However, there are some clear steps that everyone needs to follow to gain senior stakeholder buy-in and to design a framework that is right for them.

If you are struggling to gain the interest and/or participation of stakeholders or to design an effective framework then chances are you would benefit from a Data Governance coach. The cost of coaching will outweigh the cost of having to start again due to uncertainty or lack of confidence.

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.

How the Grinch (Didn’t!) Steal Data Governance This Christmas

Once upon a time, in a bustling company preparing for the festive season, there was a looming threat – the Data Governance Grinch.  

The Grinch wasn’t your typical villain. In fact, most companies have one – a challenging stakeholder who actively resists change and works to derail Data Governance initiatives. Instead of stealing gifts, this Grinch thrived on creating obstacles, sowing doubt, and attempting to prove that the organisation had never needed Data Governance before and certainly didn’t need it now.  

The Grinch had plenty of theories and misconceptions about Data Governance, which he was all too eager to spread. Fortunately, the company had Amelia, their dedicated Data Governance Manager. Calm, confident, and endlessly patient, Amelia had encountered Grinches before and knew exactly how to handle them. As the Grinch attempted to sow confusion, Amelia stepped in to explain to the senior stakeholders why his ideas wouldn’t work and what needed to be done instead.  

"There's no need to get involved – just let IT run the programme!"

The Grinch’s first misconception was that IT should run the whole show. "Leave it to the IT team!" he proclaimed. In many organisations, IT teams do take the lead on Data Governance, often focusing heavily on tools and technology. While these tools are useful, Amelia knew they only addressed the symptoms of bad data, not the root cause.  

Amelia explained to the senior leaders that the real culprit was messy, inconsistent data entry – a business issue, not just an IT one. "No matter how many tools IT uses," she said, "the data won’t improve unless we fix how it’s captured in the first place." She made it clear that Data Governance was a business-wide responsibility.  

With Amelia’s guidance, the company realised that every department needed to take ownership of their data. It wasn’t an easy shift, but with Amelia’s facilitation amd encouragement, the departments began working together, fixing data issues at the source, and keeping the Grinch’s negativity at bay.  

"Getting everyone involved is a waste of time – it’s just a quick project!"

Not one to give up easily, the Grinch returned with another sweeping claim: "Data Governance is just a one-time project. We’ll be done with it in no time!"  

Amelia firmly countered this misconception. "Data Governance isn’t a project with an end date," she told the stakeholders. "It’s an ongoing journey." She explained how many initiatives fail because they treat Data Governance as a checklist. While such projects may look successful on paper – with tools implemented and processes documented – they often fall apart because people don’t change their mindset about data.  

Amelia emphasised that for Data Governance to succeed, it had to become part of the company’s culture. Departments needed to embed it into their daily routines and collaborate continuously. "This isn’t a quick fix," she said. "It’s a commitment to long-term improvement." With Amelia’s leadership, the company began to see Data Governance as an evolving practice, one that would grow stronger over time – much to the Grinch’s dismay.  

"Oh fine, let’s get on with it then. We’ve got LOADS to do, so let’s do it all at once."

Even when the Grinch begrudgingly agreed to support the Data Governance initiative, Amelia wasn’t off the hook. His next tactic was to push for the ‘big bang’ approach – trying to solve all the company’s data problems in one go. "We can just tackle everything at once!" he declared.  

Amelia quickly stepped in to stop this plan in its tracks. "Trying to do it all at once is a recipe for failure," she warned. "It’s like trying to prepare everything for Christmas in a single evening – impossible and exhausting." She explained that a big bang approach often leads to burnout, delays, and incomplete results.  

Instead, Amelia advocated for starting small. She proposed running pilot programmes to test ideas and build momentum with quick wins. These smaller efforts gave the team confidence and a clear direction for expanding the initiative. By moving step by step, the company avoided the Grinch’s trap and steadily strengthened its Data Governance efforts.  

Perhaps the most important lesson of all…

And so, the company flourished, their Data Governance work thriving now that Amelia had kept the Grinch’s sabotage at bay. But as the festive season approached, Amelia began to wonder: "What if we brought the Grinch onside? What if we made him feel included?"  

Amelia decided to try, meeting the Grinch where he was. She listened to his concerns, acknowledged his frustrations, and gave him a clear role in the initiative. Slowly but surely, the Grinch began to see the value of Data Governance. His heart seemed to grow three sizes as he started contributing insights no one else had considered.  

Amelia’s efforts reminded the company of an important truth: success in Data Governance isn’t just about tools or processes – it’s about people. By addressing concerns with empathy, building trust, and taking a measured approach, the company created a sustainable programme that supported its success for many holidays to come.  

And they all lived data-happily ever after!

The End. 

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I hope these tips help you keep any workplace Grinches far away from your Data Governance initiatives now and in the new year! For more on avoiding common Data Governance pitfalls, follow the link below to access my full report, The Top Data Governance Mistakes and How to Avoid Them. I wish you all a very Merry Christmas, a joyful holiday season and a wonderful start to the new year. Thank you for reading, and see you in 2025!

Knowledge Graphs and Data Governance

When I first heard about knowledge graphs within Data Governance, I found it a really hard concept to grasp and it felt like stepping into uncharted territory. I think what was difficult was trying to understand how the abstract idea of knowledge graphs could translate into real-world benefits in the work we do with data in Data Governance specifically. Now, after some great discussions with Ed Mathia (one of my expert Guest Coaches, who is an expert on this topic) I can safely say I am in a much better place to talk about the importance of knowledge graphs in Data Governance - and I think it’s a really important topic for others working in Data Governance to grasp too. 

So, having been inspired by this topic cropping up in one of my regular monthly sessions with my associates and expert guest coaches, let’s now have a closer look at knowledge graphs and Data Governance in this blog. 

What is a knowledge graph?

Generally, a knowledge graph is a knowledge-base (facts about the world) that is stored in a graph structure (not a table), that ensures computers can manipulate data based on its meaning.  It is a powerful tool for organising and representing data, focusing on how different data points are connected. It allows users to easily visualise relationships and hierarchies within data, offering a more interconnected and insightful view of information. 

However, there are two more specific meanings graphs:

1. The first meaning of a graph refers to the underlying data structure, where the emphasis is on how data points are related. This version is often used in business contexts, where people rely on graphs to make sense of interconnected data. For example, a metadata graph (like a Data Catalog) can show how different data tables are connected or how one system feeds into another.

2. The second meaning, called a knowledge graph, was introduced by Tim Berners-Lee in 2009.  Knowledge graph refers to a more advanced idea of the semantic web - where the meaning of the data is documented in a way that computers can “understand” and use it.  Tim Berners-Lee did this using something called RDF triples. RDF triples organise data in a way that computers can understand better. Instead of regular text, information is set up as subject-predicate-object statements. For example, "Airplane X (subject) uses (predicate) Engine Y (object)." This format helps machines understand and work with the relationships between different pieces of data, and is very efficient.  Let’s take a look at how this works.

In a knowledge graph, things like people, products, or places are called "nodes" or "classes," and the connections between them (like relationships between people or links between products and locations) are called "edges." These edges show how different things are connected, making the graph a useful tool for representing real-world relationships. Knowledge graphs are popular because they make it easier to understand and manage large amounts of data.   Look at the image below as an example.  

The top part is a table that shows 2 people with occupation, school and spouse.  But when we get to Einstein’s spouse we have a problem.  He had two spouses and there was not enough room.  We would have to change the table to add a 2nd spouse column or extract the spouse column to a new table.  With the knowledge graph below, we don’t have to make big changes to the database, we just add another node and users will get both spouses when they search.  This is a (very simplified) version of the Google Knowledge base.  When I searched for Albert Einstein, I saw a page with information about his birth, death and spouses, and it suggested Marie Curie as someone I might be interested in because they are connected on the graph through the ‘scientist’ node (your results may vary).  The Google Knowledge base enhances regular search because it allows them to provide useful data based on the meaning of the data, just not special search terms.

(Image kindly provided by Ed Mathia

Knowledge graph use cases and Data Governance

Graphs are being used across many industries to improve data management. Some general examples include:

• Retail: Graphs are used for product recommendations and upselling, tailoring suggestions based on customer preferences and purchase history.

• Finance: In the financial industry, they help with anti-money laundering (AML) efforts and Know Your Customer (KYC) procedures by uncovering relationships between accounts and transactions.

• Healthcare: Knowledge graphs aid medical research and improve diagnoses by connecting disparate medical data points, offering a big picture view of patient information or drug interactions.

• Entertainment: Streaming platforms and media services use knowledge graphs to power recommendation engines, suggesting content based on user behaviour, preferences and connections to other media. For example, in a film knowledge graph, you could explore connections between actors and the movies they worked on together. 

Knowledge graphs offer a more flexible way to visualise data compared to static lists or tables. They help identify patterns, especially in fields like graph data science and machine learning. For example, in drug discovery, pharmaceutical companies use knowledge graphs to show connections between different molecules. By studying patterns from current antibiotics, graph machine learning models can find or predict new drugs with similar or better properties.

In Data Governance, knowledge graphs help organisations manage their data by showing how different datasets are related. It is an excellent choice for Data Catalogues since it makes it easier to organise data, follow rules and ensure compliance. They give a clear view of how data sources interact, making it simpler to track where data comes from and automate compliance tasks. We’ll explore this more later in the blog.

Benefits of using knowledge graphs in Data Governance

While they started out in specific industries, they are now being used widely across many different fields. So, as is hopefully becoming clear, knowledge graphs offer a powerful way to manage, integrate and understand data, transforming how businesses approach Data Governance. By providing a structured yet flexible framework, knowledge graphs not only make data more accessible but also improve the ability to query and navigate complex relationships between different data entities. 

Here are some of the more specific benefits of using knowledge graphs in Data Governance:

• Understanding and Managing Data: Knowledge graphs give a complete picture of a company's data. They make it easier to see what data the company has, where it's stored, how it's shared and who is using it.

• Integration of Multiple Sources: One of the main benefits of knowledge graphs is that they can combine data from different places. By linking data from various sources, companies can get a complete view of their information. This is really helpful for businesses with complex data, like aircraft manufacturers, where it’s important to understand how aircraft models, engines, and airlines are connected for the business to run smoothly each day.

• Flexibility and Scalability: Unlike traditional databases that use fixed formats, knowledge graphs are flexible and can show connections between different types of data without needing a set structure. This flexibility makes it easier for organisations to understand large amounts of data easily.

Why you need Data Governance for knowledge graphs

While there are many benefits of knowledge graphs for Data Governance, it actually works both ways in that knowledge graphs also need the support of a strong Data Governance initiative to work well. 

Without proper governance, there’s a risk of connecting wrong or misleading data, which can ruin the value of the whole knowledge graph. If the connections between data points are incorrect, the insights you get from the data can be wrong. Simply put, Data Governance and knowledge graphs work together: good governance keeps the knowledge graph accurate, and the knowledge graph helps you see how data is connected, making it easier to keep data clean, understood and well managed.

How knowledge graphs work in Data Governance

So, knowledge graphs play a crucial role in Data Governance by structuring data in a way that enhances efficiency.  As we touched on at the start of the blog, at the core of a knowledge graph are RDF triples, which represent data in a machine-readable format. This structure is very supportive of Data Governance functions because it helps computers understand and process relationships between data points. 

What's even better is that knowledge graphs are getting smarter with the help of artificial intelligence (AI). AI helps machines understand text better, find new connections and adjust to new information. This makes knowledge graphs perfect for situations where data from different sources needs to be analysed and shown based on what users are looking for. By clearly showing how data is related, knowledge graphs make it easier to check and improve data processes, supporting better Data Governance across the organisation.

It’s all about chatting and finding out

I, for one, am very glad that I now understand the basics of knowledge graphs in Data Governance. I feel it's something valuable for anyone involved in managing data to know and I want to give a big thank you to Ed (connect with him on LinkedIn here) for his support with understanding this topic (and in case you are wondering he kindly agreed to review this blog to make sure that I’m not getting the message wrong!)

And don’t forget - if you are a member of my DG Launch Pad or coaching programmes, you can schedule a coaching call with an expert guest coach. These personalised sessions offer a great opportunity to dig deeper, share ideas and learn from industry experts. This blog post is a perfect example of how our understanding of a topic improves through these discussions. So, if you're a client, reach out to schedule your next session. I'd love to see you in one soon!

Guest Blog from Niels Lademark Heegaard - Data as an asset?

I'm thrilled to introduce this guest blog by Niels Lademark Heegaard, a friend and colleague I've had the pleasure of knowing since our time working together at Platon, the first consultancy I worked at.. Over the years, I've always admired his talent for simplifying complex ideas, and this piece is yet another excellent example of his expertise in action.

Now, let's dive into Niels's insights on data management and governance—ideas that will resonate with anyone navigating the increasingly data-centric landscape of modern organisations.

Disclaimer: No LLM was hurt writing this text, the cover image caused some pain to Chat GPT.

Having described the four main data types —Master Data, Reference Data, Transaction Data, and Aggregated Data, I would like to talk about some of the properties of data.

First off my chest: Data is an asset

I mean asset in the traditional sense. Exactly like employees, buildings, materials, products, intellectual property, etc.

What I have often encountered is that organizations only pay lip service to the concept of “Data as an Asset.” It is the topic of empty toasts and balcony speeches. It should not be so.

There are some special characteristics that data assets have:

• Data can be copied

• Data is cheap to store

• Data can be used multiple times without wearing out

• Data does not take up much room

• Data can be used in multiple locations

• ...at the same point in time

Try this with tangible assets... you’ll either face strikes (employees), inability to deliver as promised (materials, products, infrastructure), jail (money), or a host of other consequences.

There are other intangible assets that possess the same characteristics — “brand value” and “goodwill” come to mind. However, there is one characteristic that is unique to data:

• Data describes all other assets

This makes data the single most valuable asset. An organisation’s ability to manage all other assets is directly dependent on the quality and availability of data assets.

Next off my chest: What asset management requires

There is one asset that every organisation manages with a high degree of zeal: Financials. Which is why budgets are always met, no expense is assigned to the wrong account, and no payment is ever late... cough...

So, imagine for a moment what Finance would look like if there were no CFO, financial controllers, bookkeepers, treasurers, auditors, etc. This is how data is often “managed.”

The responsibility often defaults to a business line (employees are the purview of H.R.). This can work if there are not too many stakeholders with different agendas pertaining to the data asset. The problem is that vital data assets are the responsibility of multiple, often unaligned, stakeholders (e.g. the Customer and Product entities).

You can read why Master Data is especially important here.

Last off my chest: Why it is so hard to assign the responsibility

The reason why organisations distribute Master Data responsibilities is because the typical organisation is set-up to manage transactions (and transaction data).

Departments executes distinct steps in cross-organizational processes. Each step is a transaction. Master Data is used in every transaction, along and across processes, but in different ways and for different purposes along the way.

• Procure material

• Produce product part

• Assemble parts

• Test product quality

• Sell product

• Invoice product

Every step uses parts of "Customer" and "Product". The way enterprises are organized in siloes is the major hindrance of getting in control. There is no single person responsible for managing the most central data asset, Master Data, end-to-end. Responsibility is distributed.

My experience is that if four people have the responsibility for data, each of them will take about 2 per cent of said responsibility.

This does not spontaneously improve. It take an active effort. Since re-organising according to processes is not happening, the answer is data governance.

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Niels started his career as a master of agriculture, but soon realized his mistake and changed to the IT industry. Niels started working with data governance in 1997, before the term was coined. In the summer 1997 he became master data manager, responsible for collecting and reporting the total research and conveyance of science done at the University of Copenhagen, from papers to museum exhibitions in one unambiguous format.

After a tenure at the Danish State Railways as information and enterprise architect, he joined a dedicated information management consultancy and later Deloitte by merger. The project tally as information management consultant ended at 28. Currently, he is working as the enterprise architect in a small company that calculates the electric grid capacity across Scandinavia.

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. Now, this is a good question because I think so many people underestimate the speed at which they're able to implement Data Governance and, as such, a Data Governance maturity assessment is a great tool for seeing what progress has actually been made and what needs to be focused on next. With that in mind, let's explore Data Governance maturity assessments in more detail.

Understanding Data Governance Maturity Assessments

Data Governance maturity assessments provide a structured framework for understanding current strengths, identifying gaps and highlighting areas for improvement in an organisation's Data Governance. 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 amount of assessments you need. You want to find a balance between making improvements and not overwhelming the team or resources.

Recommended Frequency of Assessments

Based on my experience, assessing your Data Governance maturity should be done no more often than once a year. The rationale behind this is that, realistically, organisations can only make so many changes within a short space of time. If you carry out a reassessment too soon not enough will have changed within the time frame to justify the effort of a full reassessment. And you don't want to bug people, who are already very busy, by asking for their time too often.

A yearly assessment would be the best option for most organisations, depending on the pace of change and the maturity of their Data Governance program. The key is to match how often maturity is assessed with the organisation's ability to make the changes between each reassessment. I think the best way to do this is to have a look and understand what's been moving on in your organisation and whether it's worth reassessing at this point.

Beyond Maturity Assessments: Communication and Culture

These assessments are also really good at telling an organisation's progress in creating a data culture and effective communication. 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 can recall times during my early career in Data Governance when I'd got results back from a maturity assessment and 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!

Conclusion

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.

How to Do Proactive Data Quality

Maintaining high-quality data is becoming more and more important for any organisation. According to Gartner, poor data quality can cost organisations around $12.9 million a year! However, many organisations also find themselves stuck in a cycle of reactive data quality measures, which often lead to short-term fixes rather than long-term solutions. 

In today's blog, I will explore how to shift from reactive to proactive data quality management by leveraging a Data Governance framework.

Shifting from Reactive to Proactive Data Quality

Most organisations nowadays recognise the importance of data quality. They most likely have data cleansing routines as data is loaded into data warehouses. However, these efforts are typically tactical fixes addressing issues only when they are detected. For example, missing fields might be defaulted to a placeholder value, which may be better than an empty field, but does not ensure that the data is correct.

Proactive data quality involves preventing data issues from occurring in the first place. This shift requires more than just addressing problems as they arise. It means having a strong approach to managing data quality, which can be achieved through Data Governance.

Why Data Governance?

Implementing a Data Governance framework is crucial for proactive data quality. Data Governance establishes the roles, responsibilities and processes needed to manage data quality consistently across the organisation. It ensures that data quality is maintained at the source, reducing the need for repeated data cleansing and enabling more reliable data usage.

Data Governance is a massive support towards achieving proactive data quality rather than reactive. See below for some key steps in using Data Governance to make this happen. 

Steps to Proactive Data Quality Through Data Governance

1. Get Buy-In from Stakeholders - You will need to encourage senior stakeholders to understand and support the need for Data Governance. To do this, align your Data Governance goals with the organisation's strategic objectives to demonstrate its value.

2. Identify Data Owners and Stewards - These individuals are accountable and responsible for the data quality for their data.  

3. Define Data Quality Standards - Next, work with the Data Owners and Data Stewards to establish clear data quality criteria.. This involves defining what constitutes acceptable data quality and setting rules for data entry and processing.

4. Implement Data Quality Processes - Use the data quality rules to develop and implement processes for data quality reporting and issue resolution. Regularly monitor data quality and report any issues to the Data Owners and Data Stewards for resolution. 

5. Create a Data Glossary/Catalogue - Develop a Data Glossary that includes definitions and business rules for all critical data elements. This helps ensure consistency and clarity across the organisation.

6. Establish a Data Governance Committee - Form a committee that oversees the implementation of Data Governance policies and procedures. This committee should regularly review data quality reports and address any escalated issues. Read my previous blog on Data Governance Committee’s here. 

It's no overnight task 

It's true, that transitioning to proactive data quality is not an overnight task, but it is essential for long-term success. By implementing a Data Governance framework, organisations can ensure that data quality is managed proactively, leading to more reliable data and better business outcomes. Remember, Data Governance is not just an add-on; it is the foundation that supports all your data quality initiatives.

Feel free to book a call with me if you would like to find out how I can help you implement Data Governance and improve data quality. 

Why is Data Governance Training so Expensive?

Data Governance is a critical function for organisations looking to manage their data effectively and ensure compliance with a growing array of regulations. However, many organisations and individuals are often taken aback by the high costs associated with Data Governance training. 

So, I thought I’d take a deeper look at why Data Governance training is so expensive.

Expertise and specialised knowledge

Data Governance is a highly specialised field that requires a deep understanding of industry best practices. Additionally, there’s a LOT of information out there - all you need to do is type ‘Data Governance’ into Google and you will be overwhelmed by thousands of results it yields, many of which are ambiguous and in some cases even incorrect.

Because of the above, organisations need to rely on trainers who are seasoned professionals, who bring years of experience and extensive expertise to the table. Organisations are typically paying for the depth of knowledge and practical insights that these experts provide, which are essential for building a robust Data Governance framework.

Comprehensive and updated curriculum

The curriculum for Data Governance training is extensive, covering a wide array of topics. Creating and maintaining such a comprehensive curriculum requires significant investment. 

Moreover, the field of Data Governance is continually evolving, with new regulations, technologies, and best practices emerging regularly. Training providers must constantly update their materials to reflect these changes, ensuring that participants receive the most current and relevant information. 

Customised training solutions

Data Governance training might need to be tailored to the specific needs of an organisation. Customisation ensures that the training addresses the unique data challenges and regulatory requirements that the organisation faces. Developing customised training solutions involves additional effort and resources from the trainers, including time spent on research and preparation, which can contribute to the overall cost.

Expert networking opportunities

Data Governance training isn’t just about learning from instructors - it’s also about connecting with fellow data enthusiasts. Networking with industry experts and peers can provide invaluable insights, support, and collaboration opportunities. However, facilitating these networking opportunities requires additional time and resources, contributing to the overall cost of the training.

Return on investment

Despite the high initial cost, Data Governance training offers a substantial return on investment. Properly trained personnel can help an organisation, reduce inefficiencies, comply with regulatory requirements, improve data quality, improve decision-making and provide a solid foundation from which to embrace emerging technologies such as Gen AI, which requires good data. It will also help create a data culture at the organisation (click here to read my ‘What is the impact of a poor data culture’ blog).

These benefits far outweigh the costs, making the investment in training a strategic decision that pays off in the long run.

Value of training

Training extends far beyond merely mastering Data Governance. Quality training holds the potential to generate a broad and positive impact on your organisation.

Infopro Learning suggests that the success of an organisation is significantly influenced by its capacity to provide successful training. With the right training, organisations can ensure their employees are well-equipped to perform their jobs effectively (Infopro Learning, 2023). 

Additionally, recent research from Continu in 2024 shows that training doesn't just help employees perform better; it also strengthens the workforce, aligns everyone with company goals, encourages knowledge sharing and innovation, boosts brand reputation and keeps top talent around. (Continu, 2024)

In short, investing in good training isn't just about teaching skills - it's about building a stronger, more successful organisation overall.

If you’d like to know more about how I can help you and your organisation be successful with Data Governance then please book a call using the button below.

What is Data Lineage?

In this post I want to talk about something that sounds a bit daunting but is actually super helpful when it comes to Data Governance, and that is Data Lineage. 

What is Data Lineage? 

In its simplest form, Data Lineage can be thought of as a diagram that shows you how data flows through an organisation from the first point that it comes in at.

For example, imagine a customer placing an order on a website. That's where the data journey begins. Then it might travel through various systems like order processing and inventory management, before landing in an organisation’s data warehouse for reporting. 

Now, that is a very straightforward example and of course things can get more complex than that, but the purpose of Data Lineage remains the same - to show what systems and processes your data goes through no matter how simple or complex. 

The benefits and challenges of Data Lineage

Sometimes data takes unexpected routes when it is being moved from system to system, which can lead to hiccups. That's where Data Lineage comes in handy. It can help you spot potential issues and understand how your data is flowing.

Nevertheless, creating Data Lineage diagrams can be challenging at times. There are tools made specifically to help with these challenges. Automated tools can scan your databases and do Data Lineage for you. The problem with this is that they often churn out tons of detailed diagrams that can be overwhelming if this level of detail is not needed. 

My advice? Keep it simple.

Start by focusing on the most important data for your organisation and work backwards. Ask those who use that data where they get it from, then follow the breadcrumbs all the way back. I say this because it's really hard to work forwards when you're trying to create a Data Lineage if it's never been documented before. 

Another thing I'd recommend if you're perhaps not sure where your data starts is to talk to some experienced long standing business analysts in your organisation. They probably have some good ideas about where data is flowing through. 

So, there you have it. Data Lineage isn't scary - it's actually fairly simple to create high level Data Lineage diagrams when you break it all down first.

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