Data Governance Interview with Gary Maw

Gary has over 40 years in IT with experience across a variety of industry sectors including developing and leading teams across IT Services, Project and Programme Management, Application Development, and over the last 10 years or so more focus on data - MI, BI, Data Warehousing, Governance, Quality etc.

How long have you been working in Data Governance?

Maybe around 6-7 years in practice, longer unknowingly as a necessity and bi-product of other roles.

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

It came about out of genuine interest and frustration. I think frustration when delivering BI and DW services where governance was immature and therefore had an impact on results. The interest came about with a desire to put this right and contribute to enabling data as an asset and to remove similar frustrations for other people.

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

Tenacity, endurance, perseverance, knowledge of data and associated issues, and sense of humour.

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

There are many but anything produced by DAMA and also Precisely is good.

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

Making it business-led and gaining acceptance and support across the business.

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

It doesn’t really matter to me which industry it is as the principles are the same, it’s the reasons for doing it and obviously the business itself that differs. However, it would be good to implement this somewhere that has a positive impact on the planet, humanity, nature or people.

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

First of all, believe in what you are doing and don’t give in. If there are valid business reasons for doing it then seeing the benefits come to fruition is worth the pain. A basic understanding of people in terms of psychology will help to explain the behaviours and reactions that you will experience.

Secondly, gain an understanding and/or experience across several disciplines such as Data Management, Data Quality, Data Modelling, BI and Business Strategies in order to understand the need for data as an asset and the issues and difficulties involved in getting it there.

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

They are all challenging but since my whole nature and approach is based on humour (not always directed well) I can recall many things but one that springs to mind is…

In the early days of people being introduced to computers on their desks, I found one older employee who didn’t need all the information presented to him. Subsequently, I found that he had tip-exed out on the screen all the information he didn’t need, not realizing that other selections would present different information.

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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 2022 Round-Up

Happy New Year! I hope you all had a lovely festive season and are looking forward to 2023 as much as I am! 

I have had a well-earned rest and I’m now raring to go - I have BIG hopes for 2023 and plan to help as many people as possible understand and manage their data. 

Which is why I thought I’d get off on the right foot and share with you my 10 most popular blogs from 2022. 

These are the blogs that have resonated and supported my readers and are perfect to have a flick through before you get underway for the upcoming year. 

1. Data Literacy: So What?

2. The Six Principles for Successful Data Governance

3. Do you know what is in a Data Governance Framework?

4. The Rocky Horror Data Show: Disastrous data definitions…

5. What is a Data Domain?

6. Data Governance Interview with Rob Saundby

7. Five Common Data Governance Misconceptions

8. What Makes a good Data Governance Consultant

9. Do I really need Data Governance when I’m doing a master Data Governance?

10. What the data and tech industries are doing to support Ukraine?

If you want to get a head start in 2023 with your organisation's data governance why not book a free call with me? 

You can also find out more about all the different courses and training options I offer. 

A Data Christmas Carol

It was a cold, dark Christmas Eve, as the grumpy Chief Data Officer at the Counting House known as Ebenezer threw his hands in the air and declared himself done with Data Governance. He had been trying for several months to implement a new initiative within his organisation but to no avail. He was done, finished… and ready to hand in his resignation.

He had been trying for several months to get departments across the organisation on board with his plans but he had no stakeholder buy-in, and it was impossible to get heads round a table to get things done. 

“To hell with Data Governance!” he shouted across the empty office.

Ebenezer buried is head in his hands and closed his eyes and started to drift off into a deep, exhausted sleep. Suddenly, he jumped awake, and there in front of him was a tall, shadowy figure.

“Who are you?” he croaked out.

“The ghost of Data Past” came the reply, “And you need to come with me… I have something to show you…”

Dumfounded, Ebenezer stood up and followed the figure into the next room. Suddenly the empty bullpen was bustling with people; keyboards were clanking, telephones were ringing and there was some sort of commotion.

An argument had broken out between members of the marketing team and members of the customer care team about the quality of data in the systems; both teams were making changes to the same set of data and felt they had ownership of it but were working with different sets of rules and definitions and the quality of the data set had become so corrupt it was no longer useable. The business had ground to a halt and there were serious concerns that a regulatory fine could be on the horizon as the data had been badly mishandled.

Jobs – and the future of the Counting House – was at stake.

“You see, Ebenezer” said the shadowy figure, “this is why you began this Data Governance journey in the first place… without you, all of these people would have lost their jobs just before Christmas.”

Ebenezer was frustrated, “Yes, but why are you showing ME this? I understand why we need Data Governance!”

“To remind you of your why…” replied the shadowy figure… and with that, they vanished, and the office returned to its dark, cold, quiet state.

Ebenezer let out a heavy sigh and began to mutter about not eating any more of his Christmas cheeseboard before taking a nap when the room around him started to transform once again, and a new, slightly glowing figure appeared by his side.

“Oh, what fresh hell is this?” he asked the new presence.

“I am the ghost of Data Present, and I am here to shed some light on what is going wrong with your data governance initiative Ebenezer.”

Ebenezer was really beginning to get frustrated now, trying desperately to wake himself up from what he thought was a cheese-induced nightmare.

“I know what’s going on with the data governance initiative – no one else cares!” he snapped back.

“Do you really think that’s true Ebenezer? Maybe you should take some time to listen to your colleagues…”

They stood quietly, clearly invisible to the cluster of colleagues that stood near them, chatting away. As he listened in, he could hear his colleagues complaining that they were in the dark about why The Counting House was implementing Data Governance at all:

“Ebenezer keeps saying it’s ‘best practice’… what does that even mean?”

“He’s so unapproachable – he talks in riddles about efficiencies and opportunities but why should I care?”

“Why should I take on extra work to keep the regulator happy – that’s above my pay grade!”

Ebenezer was shocked into silence.

The Ghost of Data Present began to speak in a calm tone, “Ebenezer, you’ve not communicated with these people. They see you as unapproachable.

“If you want them to buy into your Data Governance initiative, you need to explain to them what is in it for them and if you can't in a way that they will be interested in and benefits them, they're just not going to be interested.

“You are struggling to get stakeholders to buy into your data governance initiative and that is why you're not getting the support you need for it or the funding and everything you've done to date feels like wasted effort. Communicate Ebenezer…”

And with that, the glowing figure was gone again.

Ebenezer rubbed his eyes… he was really done with the blue cheese this time. But before he could go any further, a third, glistening ghost was at his side.

This time, he surrendered himself to the inevitable, “And whom might you be then…?”

“I am the Ghost of Data Future” replied the sparkly being, “and I am here to show you how things could be… if you just explain to these people why!”

Before him, Ebenezer was presented with a happy workplace, and a younger-looking, smiling version of himself was making his way through the office.

“Congrats, Ebby!” shouted one colleague.

“Well done, Ebenezer!” boomed another.

“What’s happening?” he asked the Ghost.

“You sat your colleagues down and explained to them their own personal why for implementing data governance, they bought into it and so far it’s been a success… The Counting House has just avoided a big regulatory fine and it’s all thanks to you…”

The glistening figure disappeared and once again Ebenezer found himself standing alone in the cold, dark office. But this time he felt warm and empowered. He knew what he had to do…

A word from The Data Governance Coach

If you plan to implement data governance within your organisation, or you have started, but your initiative has fallen by the wayside, then the first thing you must do is take a step back and assess why you’re implementing Data Governance within your organisation.

Implementing Data Governance can often be a long and thankless process, and some might argue it is not for the faint-hearted, so understanding ‘why’ is crucial in order to get the most out of your data governance journey – and here is the secret… it must be business led.

If you do not know what the business case is for the implementation of DG within your organisation, it can be easy to get side-tracked and distracted. The ‘why’ is what will guide you in your journey and ensure your organisation is getting what it needs from your data governance initiative.

Don’t be like Ebenezer.

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.

A Decade of Blogs

Ten whole years of The Data Governance Coach blogs

Yes, that’s not a mistake, you’ve read that right: I’ve been writing blogs as The Data Governance Coach for ten whole years!

I can’t quite believe it.

But when they say ‘time flies when you’re having fun’, that is precisely how I feel. Writing blogs on data governance for the last decade has been a complete joy.

Sometimes blogging can feel challenging. It’s either hard to get started or hard to keep up with - but the lovely feedback makes it all worth it!

I’ve absolutely loved being able to provide a free resource so everyone can take the next step (whatever that may be) with their data governance initiative. I never in a million years thought blogging from my little laptop would be so rewarding!

To celebrate hitting the BIG ten, I wanted to share my most popular blogs from over the last decade. Now, I won’t lie, this really was a lot of digging through the archives and some of these I completely forget even existed…

So, here goes.

1. Data Owners and Data Stewards - What is the difference?

A topic that has caused a lot of confusion over the years! In this blog, I addressed the difference between data owners and stewards and hopefully provided some much-needed clarification.

2. What’s the difference between Data Owners and Data Custodians?

This is a question that I frequently receive - and it’s an important one! Addressing roles and responsibilities is an essential part of implementing data governance. But how can we do that if we don’t know the difference between all the different roles needed?

3. What is the Difference Between Policies and Standards?

Over the 20+ years I’ve been working in data governance, this question always ends up in a (sometimes heated) debate! In this blog, I explored the difference between policies and standards and where on the earth the confusion comes from.

4. How To Identify The Right Data Owners

This one was inspired by my Free Data Governance Checklist where I talk all about how you need to define roles and assign responsibilities - and this blog covers where to begin.

5. Data Governance Interview - Jill Wanless

This was a very exciting interview that I did way back in 2013. What makes it even more exciting is that Jill is still a successful and senior Data Governance Practitioner!

6. Interview Questions For A Data Governance Manager Role

I enjoyed writing this blog so much as I got to collaborate with the data community to create this resource. Using LinkedIn we all came together and jotted down our best interview questions for a hypothetical Data Governance Manager role. I get so much feedback about what a useful resource this is, both for those hiring a Data Governance Manager and those preparing for interviews.

7. What do you include in Data Quality Issue Log?

Whenever I’m helping clients implement a Data Governance Framework, a Data Quality Issue Resolution process is at the top of my to-do list, so I thought I’d write a blog all about it and it has definitely proved a popular resource!

8. Make Sure you Follow These Practical Steps for Creating a Business Glossary

Way back in 2015, I teamed up with Collibra to teach a training course, and it seemed like the perfect opportunity to ask Carl White his thoughts on the best way for organisations to approach data governance.

9. Why are there so many different Data Governance definitions?

It’s a good question… I didn’t have a definite answer (and still don’t!) but I wrote down exactly why I thought that the data management community hadn’t pinned down any exact definitions.

10. When is a Data Quality issue not a Data Quality Issue? Part II

And last but not least, a collaborative blog on when is a data quality issue not a data quality issue, providing insights on the types of issues you will get thrown at you when you start doing Data Governance at your organisation.

When I first started blogging I wrote a list of topics for my blogs and was worried that I only had enough content for six months, I never dreamt that I would in the future be writing a post sharing my most popular blogs from the last ten years - make sure to leave a comment if you’ve had a favourite from the past decade that I haven’t mentioned above!

Thanks for reading, here’s to the next ten years!

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Data Governance for Data Mesh

In order to set your data governance initiative up for data mesh, you first need to understand traditional data governance. What does it even mean? For me, functional data governance - at an organisation of scale - is not centralised in day-to-day decision-making. The central team just can't have the context or the knowledge across all the data to make good decisions quickly, if at all.

There absolutely needs to be a central team to provide support and knowledge and set federated teams up to succeed, they have a focus on friction-reduction and value-add work. To do that, you need to create standards and processes, but you need keep your frameworks, processes, and standards as simple as possible - no one single, all-encompassing standard please!

Data quality is key

As an example of functional governance, think about universal data quality standards. Every use case may require a different combination of data quality - why optimise for completeness if it's not needed?

Data Governance should be focused on helping business stakeholders define aspects of data quality and how to measure it - that way, data consumers can understand the quality of what they consume without learning different standards for each new data source, but we aren't setting data quality requirements that aren't helpful or useful.

Data mesh for the people

Data mesh is very much about the people side. That means the data governance team needs to collaborate with people outside the central team to iterate and improve upon your data governance approach. Feedback leading to improvements is necessary, the data governance team can't issue decrees from on high.

A part of data mesh that excites me is trying to solve for the age-old challenges of ensuring the data is the right data and that we get it in front of the right people to answer questions about the business - lowering friction to leveraging our data. What "right" means is always somewhat open to interpretation of course.

Whether you are doing data mesh or not, I believe data governance can't be about obstacles. That is how data governance got a bad reputation. The phrase should spark joy, not fear or revulsion.

Instead, it must be about making it easy for data consumers to find the right data and then being able to find the right people and documentation to help them understand that data. Governance is about providing low-friction ways to provide access and drive understanding of your data and how to properly use it.

Who does what?

One of the biggest lessons I have learned working on a data mesh implementation is that while in data mesh, there are a few new responsibilities that are called out explicitly, that might fall under different roles in different domains.

Some responsibilities may fall under a data owner in one domain and under a data steward or mesh data product owner in another. The differing role types are data owner, mesh data product owner, and data steward.

Find a standard setup for roles and responsibilities and then let the domains move responsibilities around as needed - don't make the domains come up with everything from scratch but don't hold on to your standard setup closely either. Everything in data mesh is about iteration and evolution!

When will I know my data governance is ‘good enough’?

No matter what, you won't get your data governance perfect when starting. Especially with something as immature as data mesh is right now. So have clear indications but nothing set in stone. Think about what capabilities are needed early to drive value: is that some complicated interoperability standards or some data quality definitions/measurement to enable people to understand and trust the data? Probably data quality definitions.

Every data governance approach should be tailored to the organisation, but it should start from a few building blocks:

• Policy: as it mandates who will be required to do what and why? Domains just don't do data governance out of the goodness of their hearts.

• Processes and standards: lay out what you are trying to achieve and why and then give people an easy way to achieve that. That drives consistency and reduces friction, a win-win.

• Roles and responsibilities: it's very crucial to assign ownership and layout who exactly owns what; we've all been to meetings with no clear next steps, and they are almost always a waste of time. Who owns driving things forward? Be clear about it.

My top data mesh governance advice

• Look for a relatively simple first use case. What has a high chance of success where you can also get some momentum and learnings?

• Don't only look for the simple use cases early in your journey. That can lead to not being able to actually face the hard parts when they come. And with data, of course the hard parts will come.

• Communicate early and often that you want to collaborate with people and that things will change. Solicit feedback and make constituents part of shaping your governance.

• Make it clear the central team is there to help and not control - help around compliance, help with reducing friction, etc.

A general sentiment that has worked well for me in the past is telling people outside the data governance team: ‘if you don't get more value from data governance than you put into, we'll change our data governance frameworks’.

The data governance team may feel the pressure but if you aren't adding value with your governance, outside of regulatory compliance, why are you doing it? Teams will want to participate if you give them a reason to so find the value-add reasons to.

Final thoughts

If you are looking at data mesh as only making data more accessible and usable to existing data consumers… that's a big, missed opportunity. Data mesh can make data more accessible to more employees, driving better decisions. We need data literacy to get to that target outcome but implementing data mesh will lead to a lot of wasted potential if we don't expand the data consumer pool.

And don’t forget - you can actually drive buy-in for data governance. Whilst trying to sell everyone on upping your data governance game with the same message is not likely to succeed, data governance really does have value for all participants!

If it doesn't, you need to change your governance approach. So drive that home; tailor the message and speak to your organisations pain points and how you can help them address the pain.

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.

Five signs your Data Governance initiative is failing

One: There are different understandings of the terminology within the same organisation

Most organisations are full of lots of jargon and terminology which can mean different things to different people. It’s all very subjective and this is usually because of the culture within a particular organisation. The way the various terms are applied within organisations can vary their meaning. And that’s ok - but you should also be wary of it.

Data Governance takes a long time, and particularly in the early phases, it takes quite a lot of effort. Therefore, it is understandable that people look for ways to quicken this process up. One of the ways I am often asked if this can be done is by fast-tracking the creation of items like a data glossary by using standard definitions.

However, it’s not a part of the process that can be skipped or glossed over, so to speak. Part of the reason for this that organisations, even those within the same industry, very rarely use the same terminologies in exactly the same way. This means there is no bank of standard definitions to pick and choose from; what works for one client, will very rarely work for the next. Only by creating your own data glossary can you be sure that everyone fully understands the definitions within it.

Two: Disengaged stakeholders and a lack of budget

Another reason many data governance initiatives fail is a lack of support at a management level. If senior management does not buy into the benefits of data governance and only sees the associated costs, an initiative will almost never succeed.

First, there is a danger that the required processes won’t be executed correctly. Additionally, because of costs, critical improvements may not be implemented, or the initiative may need to end prematurely.

Sourcing the budget needed for an initial data governance initiative is easier today than ever before because there are regulations that justify it, for example the GDPR. However, it is crucial that management also makes sufficient long-term resources available to finance all roles and functions required for robust data governance on an ongoing basis.

If your stakeholders aren’t prepared to put their money where their mouths are, this would indicate that the initiative is not being taken seriously enough and its value is not understood.

Three: Only implementing Data Governance because of regulations

If the pressure to implement data governance comes from a regulator, then it is very tempting for organisations to look at satisfying the absolute minimum required to keep the regulator happy. This is a big mistake, as in the long run, these organisations end up doing more work than if they properly implemented data governance in the first place. They also miss out on all the business benefits that come from improving their data management practices.

The tick-box approach to data governance is normally task-focused and completely ignores the people involved. They issue a checklist of things that need to be accomplished and issue threats if the tasks are not completed. As a result, people go through the motions because they must, and they see no real benefit to their day-to-day job.

As a consequence, it’s going to be hard to embed your data governance framework within your organisation and you will always be chasing people to make sure that they have complied with the regulations.

Regulators are notorious for moving the goal posts, so if you have not embedded data governance into your organisation, every time they change the regulations and update the checklist you will probably move back to square one, which means implementing the new checklist.

Four: No data quality issues being reported

If data users aren’t reporting data quality issues, this indicates that either people don’t know about your process to investigate and fix issues, they don’t think you will be able to make a difference (maybe based on years of no-one being interested in making data better) or perhaps they don’t understand that the manual workarounds they have to do every day/week/month are the result of poor data quality and everything could be improved and streamlined if the underlying issues were addressed.

Whatever reason, it all boils down to communication. And if you’re failing to communicate with your data users your data governance initiative is sure to fail.

Five: It’s not being talked about outside of IT

The key to data governance success is getting stakeholders to take ownership of their data and take the lead in data governance initiatives. When I perform a data governance health check for companies that are running into trouble, it is fairly common for IT to be leading the data governance initiative.

This is always for the best of intentions. Even though IT does not own the data, they understand the implications of not managing data properly, and therefore they are often the first people in any organisation to realise that proper data governance is needed.

Businesses often leave IT to deal with data governance because they confuse the infrastructure with the data. If you work for an organisation that still believes that IT owns the data, then assigning IT to run the data governance initiative may seem logical.

However, an IT-led data governance initiative can be fraught with problems. True data governance will only really happen once the business has taken ownership of their data, and an IT-led data governance initiative makes that more difficult.

In my experience, IT-led initiatives are too focused on tools that do things like cleansing data. This is understandable, as companies tend to get their advice from IT vendors who are in the business of selling tools. The problem is that unless a business changes the way that data is captured at the point of entry, the quality of the data will never improve.

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.

Guest blog - Data science and data governance: collaboration for trustworthy data insights

Data scientists are storytellers. They gather data from a variety of sources, clean and combine that data, and use their programming, math, statistical and analytical skills to interpret that data. Data scientists can help businesses understand customers and market trends, forecast sales, improve processes, and help to make better, smarter, faster decisions. But the data driven results depend on good and reliable data. Advanced data models and algorithms are only as good as the data they are applied on. Without good quality data, even the best models and algorithms fail.

Strong Data Governance policies and practices ensure valid, quality data and thus ensure that data analytics and data science methods can arrive at meaningful and trustworthy conclusions. Collaboration is key for the best results.

As a data scientist, you can expect to spend up to 80% of your time cleaning, transforming, and checking your data. A discovery and understanding stage is important, but it should not be so prolonged. If the data scientist is confident that the data has been verified by the business, it is consistent and compliant with regulations, then they can focus on bringing out the stories within the data rather than double checking its content. Data governance plays the key role of validity checking to prevent confusion over the data or misunderstanding and thus meaningless data science results.

What can a data scientist do for your business? Visualisations can be produced to understand and extract insights from data about the business and its customers. Machine learning (ML) models can be developed to continuously capture insights to help make more informed business decisions. Past performance can be studied and predictions made.

One example of the use of ML is the work I carried out for a research hospital in Rome to understand public opinion. Vaccine hesitancy was identified as one of the top threats to global health by the World Health Organization (WHO) in 2019, with the growth of online communications and misinformation about vaccines an increasing area of disquiet. The hospital wanted to understand people’s stance on the subject of vaccination. Concerns had been raised about the low maternal vaccine uptake in Italy. Social media is increasingly being used to express opinions and attitudes, so we decided to use Twitter as our source of data. I trained and fine-tuned a natural language processing machine learning model to classify the vaccine stance (promotional, neutral, or discouraging towards vaccines) of Italian tweets. This is now used on a web platform for medical professionals and policy makers to monitor vaccine stance in almost real-time.

Another example of applying data science to a business is the work I have done for a local gin distillery, analysing sales data to predict future sales and profits. Data visualisations and predictions were an important part of the business plan for explaining the business and its potential to investors.

Data governance can deliver high-quality, trusted, and compliant data. Data science can deliver insights into that data. Collaboration between data governance and data science professionals increases the level of certainty in the results, models and predictions so you can make the data-driven decisions for your business with confidence.

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The author: Susan Cheatham is an independent consultant in data science and data mentoring. She gained her technical data skills and physics PhD from the European Centre of Physics Research (CERN). She enjoys communicating and sharing knowledge.