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.

---

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.

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

If you found this helpful and would like to know more about Data Governance, feel free to book a call with me.

How long does it take to implement a Data Governance framework?

A question I get asked frequently is about the duration of time and the complexities involved in implementing Data Governance - especially if it’s someone’s first time implementing Data Governance.

First things first - Data Governance is not just an agenda that can be crossed off once implemented. 

Data Governance is a long-term ongoing practice that constructs a healthier data environment in an organisation to support its goals. Data Governance extends beyond a single activity or project. 

With that in mind, let's address the question of duration.

Does Size Matter? 

Undoubtedly, the size of an organisation, the number of systems in place, and the complexity of the organisation and its systems will impact the timeline for implementing Data Governance. 

I always recommend a phased approach, this allows prioritisation and the gradual application of Data Governance principles across your organisation.

The Art of the Rollout

Many people think that the implementation of Data Governance can be approached in one of two ways.  That it can be introduced all at once - like a seismic shift.   Or , alternatively, you might decide to tackle it by business function, data domain, or a combination of both, depending on where the greatest need for Data Governance lies within your organisation at the time.  I’m a fan of the latter approach - having next seen it be successful when the first approach is attempted!

What is the Time Commitment?

Designing your Data Governance initiative and beginning to realise any benefits can typically take between a year to 18 months. However, this should be considered a ballpark estimate - as your organisation's complexity and size are variables that could extend this timeframe. For large multifacted organisations achieving comprehensive Data Governance could span several years.

Reflections

Embarking on the Data Governance journey requires thorough planning and a measured, phased execution. The size and complexity of your organisation will influence the timeframe. That being said, don't let this discourage you! Data Governance is an ongoing practice, and keeping the wheels in motion is critical in ensuring your organisation's approach to data remains adaptable and robust.

Don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com 

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

Data Governance Interview with Danielle Titheridge

I met Danielle when she attended my training course and loved her enthusiasm, so I thought she would be a wonderful person to interview for this blog. Danielle describes herself as a ‘people’ person, sociable and likes to find solutions to problems.

She’s from Dorset and enjoys long coastal walks with her Hungarian Vizsla and likes spending time with family and friends too.

Like many of us, Danielle has had a number of different careers from a primary school teacher to working in HR and now Data Governance.

How long have you been working in Data Governance?

I have been working in Data Governance for approximately 6 months as a Data Governance Analyst. It is quite different to my HR Advisor role but there are similarities with working with people and being another support function for the organisation.  

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

I was working at the RNLI in the People department and then I saw an advert for a Data Governance Analyst and wondered what it was. It sounded really technical but after speaking with the Data Governance Team I learnt that a lot of it was about managing change and stakeholders and not having to be too technical at all. I applied for the job and was fortunate to be offered the role.  

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

People skills! I was put off originally thinking it was a really technical role (because I am not technical), however you don’t need to be really technical to be good at data governance. Don’t get me wrong, you need to understand some areas so you can understand how it can benefit the organisation but a lot of what we are focusing on is change management and increasing people’s confidence. We want them to have an understanding of what data governance is and how it can benefit them, their teams and the organisation as a whole. Once people understand the ‘why’ then they are more likely to engage and want to make positive change and efficiencies.

You do also need to have a lot of resilience at times and be able to adapt.   

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

Your website and blogs I found really helpful as well as your training course. It really cemented ideas in my head and really started to eliminate the imposter syndrome so I honestly can’t recommend it enough.

Also my wonderful colleagues in my immediate team but also our wider team to take the time to explain to me what we do and how it fits into the bigger picture and organisational strategy.

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

People not wanting to take accountability, specifically relating to data roles and the importance around this.

The ‘Why’- people still don’t really understand it. I think because it links with data protection and information security, people struggle to know the difference.

People see it as us asking them to do something else in their already busy day but the ironic thing is once they have it will provide them with more time as it creates efficiencies – work smarter not harder!

A recent project we have been working on is around classifications (sensitivity labels) and from a technical, system perspective there are a few difficulties but the main thing is the engagement and culture of people. So we have definitely had to redirect our focus and next steps and timing is definitely a crucial factor we have had to consider too.  

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

As I am working for a charity now I would be interested to see what it would be like to work for a private organisation one day and the difference in tools, culture and behaviour towards data governance.  

Finally, what single piece of advice would you give someone just starting out in Data Governance?

You don’t need to be an expert at it all and as we know technology changes, has updates etc so you will never 100% be up to date so learn to be ok with that… I am still learning to be ok with it.

It’s all about story telling and change management - so show people in a way they can relate to, pull on heart strings and then they will see the benefit.