Five Common Data Governance Misconceptions

I’ve been doing data governance for a long time now. And it’s safe to say that time and time again, from organisation to organisation, I come across the same mistakes and misconceptions that are limiting organisation's chances of implementing data governance successfully.

But it doesn’t have to be this way - forewarned is forearmed after all! So let’s look at the five most common data misconceptions:

Number 1: Thinking there’s such a thing as a standard data governance framework

I’ve been asked many times over the years, ‘Where can I find a standard data governance framework?’ and, as with a lot of Data Governance questions, my answer is always the same… I don't even know whether one exists. I have never looked into it because I know from my many years of experience in Data Governance that they won't work.

If you think about it, a standard Data Governance framework has been designed as a theoretical exercise.  It certainly wasn't designed for your organisation.  The only way to be successful with Data Governance is to first work out why your organisation needs Data Governance, and then to design and implement a framework that meets those needs.

I can (almost) guarantee that as any standard framework was not designed for you it is not going to meet your needs. It’ll very likely be too complex, too convoluted, and too focused on things that really aren't appropriate for your organisation.

And the cost to your organisation when your standard Data Governance framework inevitably fails to get the desired results could be huge.

It won't be well received, and you'll have to start again. And if you've already put people's backs up by making a mistake, it's going to be even harder to get them to buy into the right Data Governance framework at a later date. And let’s face it, it’s hard enough to get people excited about Data Governance in the first place…

You can read more about this here.

Number 2: Thinking data governance is a one-off project

This common mistake is easily made because it seems logical to treat the implementation of data governance like any other project. Getting stakeholder involvement is essential to successfully implementing a data governance initiative and getting their buy-in. However, this is not something that can be simplified to a list of tasks.

Once you get stakeholder buy-in, you are then faced with the even bigger challenge of changing attitudes, behaviours, and even the culture towards data. I hope you can see that this is going to take something a bit more sophisticated than conventional project management.

When a data governance initiative is led as a project, it appears that progress is being made as tasks get completed. However, nothing substantial will change until the people change. And to change behaviours, attitudes, and culture, you must win hearts and minds. This is almost always overlooked when the success of the initiative is measured by deliverables ticked off a checklist. A proper change management approach is what is needed.

Without getting the stakeholders on-board, you will struggle to integrate your data governance framework so that it becomes business-as-usual. Without stakeholder buy-in, the organisation will eventually resort back to their old ways and the data will suffer...

In short, the whole initiative will have been a complete waste of time and money, and subsequent attempts to re-implement data governance will be resisted by stakeholders as they will assume that it’s a waste of time.

Number 3: Thinking it can be done quickly

This follows on nicely from the last misconception… Implementing data governance will take a reasonable amount of time!

In fact, I’d go as far as to say it will take a very long time to implement it fully across your organisation and to be honest, you probably will never get to that stage because as your company or organisation evolves and changes, your data governance framework will also have to evolve and change to match your needs.

This is not a short sprint. I wouldn't even call it a marathon. This is just an ongoing activity that we will always have to be doing.

Number 4: Think you can DIY Data Governance based on internet advice

It's true. There's a lot of good advice on the internet on how to do data governance (I hope I've contributed to some of that myself) but I do urge you to be aware when you start googling Data Governance and ‘how to do it’.

There is a whole range of advice available ranging from the excellent and very practical, simple advice to very complicated and confusing, ambiguous advice… not to mention all the advice that is downright wrong!

And this wrong advice is usually the most dangerous because as you’ll find out as you embark on your journey there are a lot of terms and roles that can be easily confused if you’re not getting good advice.

For example, Data Protection (also known as Data Privacy) is often confused with data governance. It specifically revolves around the protection of personal information and although more recent Data Protection regulations, like GDPR, do have requirements that are more easily met if you have a Data Governance Framework in place, Data Governance is a separate discipline.

Likewise, Data Retention, which focuses on how long you should hold onto data before deleting it, is something which your Data Owners should be consulted on but is a fundamentally different discipline. And while these separate disciplines all carry value in their own right and can - and should - be aligned with your Data Governance framework, they are ultimately separate. 

Unfortunately, the confusion surrounding the links between these different areas can feed into the misconception of Data Governance as a sort of grand, big Brother-esque surveillance program designed to watch business users’ every move with their data.

This isn’t the case at all! Data Governance is actually more about getting your business users to care about their data and its quality.

 Number 5: Thinking you need to have a team of consultants to help you

Many people are put off implementing data governance because of this misconception and understandably so because this will be very expensive. And given what I've laid out in the previous point around the wide variety of advice that’s out there you can see why people might feel so overwhelmed that they endeavour to bring in someone who can take care of it all for them… but trust me, you can do this without bringing in an expensive team!

Now, don't get me wrong, I am a data governance consultant and I think that data governance consultants can add real value, but be careful… as I’ve said before, this not a project.

You do not want a team of consultants on site for months or even years doing this for you. When the budget runs out, they will walk out the door taking with them all the knowledge and network that they've built up over time.

If you need help, make sure that you work with the consultants in a way that helps you to implement data governance and ensures that you get the skills and knowledge you need to run and support your data governance initiative yourself.

 Next Steps

If you are just starting out in data governance, then this is a good place to start as it links to a number of blogs that will give you the basics:

• If you feel you need a bit more information why not come along to my next live online data governance training course on the 7th & 8th June.

• If you can’t make the date or the times don’t work for you, then you’re in luck as there is a pre-recorded online version also available.

• If you feel you know what data governance is but are still facing some challenges in your implementation, then why not join my next data governance mastermind.

Or if you feel that your needs are a little bit more complicated than that, why not book a call to discuss how I can help you.

The Rocky Horror Data Show: Not everyone’s on board…

Data shouldn’t be a wild and untamed thing, but sometimes it is just that - wild… and untamed. And unfortunately for our friend Tim, he’s about to find out just how wild and untamed data can be. As this is ‘The Rocky Data Horror Show’… where the data is not what it seems.

Episode 3 - if you missed the previous episodes you can read them here:

Episode 1 - The Rocky Horror Data Show - where the data is not what it seems

Episode 2 - The Rocky Horror Data Show: Did you get what you asked for?

Tim is settling in well at the Magical Wish Factory, and he is really enjoying working with and getting to know all his new colleagues. However, he’s still getting a lot of resistance when it comes to the thing he’s actually there to do - implement data governance!

Tim is facing two major issues - the first is just trying to build a decent data glossary, and the other is a little more serious… some senior stakeholders still don’t really understand why he’s there or why the Magical Wish Factory even needs data governance to begin with. In fact, some think it can just be done with the wave of a magic wand…

Unfortunately, the former seems to be a symptom of the latter. Despite Tim’s pleas, no one is putting any effort into their data definitions and Tim suspects its because he’s not got the buy-in of all the senior team. Their impassiveness is seeping down through the ranks - and no one is taking the initiative seriously.

Tim suspects that part of the reason for this is some senior stakeholders still don’t fully understand why data governance is so important and has been telling Janet, the head of IT, about his suspicions.

Tim explains: “Understanding why we are implementing data governance is crucial for several reasons, but most importantly everyone needs to know the ‘why’ in order to guide them through the data governance strategy and the change management adoption that we’re trying to put it place.

“Unfortunately, it’s not enough to just know that you should have data governance, it’s also important that everyone here at the Magical Wish Factory understands why we should have data governance. If not everyone understands and can relate to the ‘why’ then there is a risk that the whole initiative could come off the rails.

“You need everyone, particularly those who will be involved in the implementation of the data governance initiative, to buy into it for it to be a success. You cannot start to manage your data as an asset and realise the value of it if you don't address culture change and adopt a change management strategy early in the process.”

Tim tells Janet that in fact, just last week, the Chancellor asked him what exactly his role was and why they couldn’t just use the magic of Artificial Intelligence instead. This is particularly concerning because change management starts from the top - and if senior people aren’t on board then the people Tim is going to and asking for things like data definitions aren’t going to be on board either.

Tim knows that magic AI is only as good as the underlying data and that the magic needs quality data to learn from, or before long that’ll be a mess as well so he’s decided to sit down with the senior team and explain why: AI needs the right data in order to learn.

“So, as a consequence, if we've got missing or inaccurate data, our wrong and potentially inaccurate data can and will guide the magic AI in the wrong direction and it will make the wrong decisions and the consequences could be costly and maybe even disastrous.

“If we’re going to spend time and money integrating AI into the Magical Wish Factory, then I really feel quite strongly that if we want to reap the proper rewards of these brilliant magical technologies, we must implement data governance first so that we get the results we want.

“It’s quite simple: we need to make sure we've got our house in order before we start embarking on an AI and ML (magical/machine learning) journey.”

Now, as the senior stakeholders begin to understand better why the Magical Wish Factory needs data governance and get on board with the initiative it’s time to address the issue of those dodgy definitions…

 Stay tuned for episode four of The Data Governance Coach’s new series ‘The Rocky Horror Data Show’ and follow the adventures of Tim and Janet as they try to implement a successful data governance initiative at the Magical Wish Factory.

And if you want to chat about your Data Governance Training requirements, why not book a call by using the button below?

Helping Charities Do Data Governance

As you probably know I help organisations understand and manage their data better. Typically people turn to me because their data is a mess and they need help unravelling it or because they realise they are pouring cash into new initiatives that are failing because of poor quality data.

I have been designing and implementing Data Governance frameworks for almost two decades and have helped hundreds of organisations implement Data Governance, giving me a unique level of experience and insight into the typical challenges they face.

From my experience it is clear that a lot of organisations don’t value their data as an asset. As a result they don’t manage it correctly and this can lead to rising costs and inefficiencies and wrong decisions being made. This is not good news for any organisation, but is really bad news for a charity and the causes they are helping.

Having worked with a number of charities, it is clear to me that Data Governance can make such a difference and enable them to help more. Yet charities find it hard to justify the costs of getting training on how to do Data Governance.

A few years ago I started offering free places on my training courses to charities, because I feel it is important to give charities the skills to make sure that data is used to solve problems and make better informed decisions.

I am so passionate about the value that Data Governance can bring to the charity sector that I recently joined Pledge 1%.

My pledge is to offer a free place to a different charity organisation on every single one of my public Data Governance courses.

I want to be part of a world where everyone understands the value of data and uses it to make a positive impact. So I’m thrilled to be part of Pledge 1% and committing to supporting charities on their Data Governance journeys and truly harnessing the power of their data.

If you are a charity and would like a free place on my February Live Online Data Governance Training and Clinic - get in touch by emailing nicola@nicolaaskham.com

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

The Six Principles for Successful Data Governance

There are six principles that underpin all successful data governance initiatives. These are principles that I have developed over many years of experience of successfully (and sometimes not so successfully in the early days) implementing data governances in dozens of organisations.

 These are principles that I believe underpin all successful frameworks and, if followed, will lead to your organisation to successful data governance. They are:

• Opportunities: Identify the benefits of data governance for your organisation.

• Capability: Set yourself up for success by ensuring that you have the right resources and knowledge.

• Custom-build: Design a Data Governance Framework which is tailormade to your organisation.

• Simplicity: Avoid complexity and make it easy to embed Data Governance.

• Launch: Implement on an iterative basis and start to see the benefits of your work.

• Evolve: Develop your framework as your organisation evolves to make further gains.

Let’s look at each of the principles in a little more detail:

Opportunities

Why is your organisation doing Data Governance? What's the value proposition?

You need to be clear what benefits you hope to deliver and why that is important for your company. In my experience, starting Data Governance for best practice purposes are doomed to failure.

You need to truly understand why your organisation is implementing data governance. If you don’t know ‘why’, 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.

People will often spout generic benefits like ‘oh there will be efficiencies’ or ‘there will be better opportunities if we do data governance’, but they can't explain why when challenged and the consequence of this is that when you're meeting your stakeholders at the start of a data governance initiative - particularly your senior ones -  they want to be able to know ‘what's in it for me’ and if you can't answer that in a way that they really are interested in and benefits them, they're just not going to be interested.

Capability

So many people (me included - but that’s a story for another day!) find themselves doing Data Governance by accident and, usually without any previous experience or knowledge of what exactly you should be doing.

Coupled with the fact there is so many confusing, conflicting (and some downright wrong) things on the internet it is easy to get confused or alternatively get stuck in analysis paralysis as you read just one more article before designing your Data Governance framework.

That is why I try to offer bite sized simple pieces of advice with my videos and blogs and why I started offering training to give people the knowledge and skills they need to be successful.

Custom-Build

For it to be successful, your Data Governance Framework must be designed for your organisation - there is no standard framework that will work for you or anyone else.

A data governance framework is a set of data rules, organisational role delegations and processes aimed at bringing everyone in your organisation onto the same page when implementing Data Governance.

The only way to be successful with Data Governance is to first work out why your organisation needs Data Governance, and then to design and implement a framework that meets those needs.

I can (almost) guarantee that any standard framework is not going to meet your needs. It’ll very likely be too complex, too convoluted, and too focused on things that really aren't appropriate for your organisation.

Simplicity

I’ve never yet seen an overly complex framework/approach to Data Governance that has worked. Don’t try to allow for every possible eventuality - you will tie yourself and your business users up in knots and create something that is too complicated to implement, and everyone will resist adopting it.

I have found (the hard way) that simplicity is best - remember you can always add detail as your Data Governance approach matures and you find a need for an extra level of detail but start simple and grow from there.

Launch

Launch is linked to simplicity. Over the years I have seen many organisations fail in their Data Governance initiative because they try to do everything at once, however it is really is too much to do all at one time.

I often call this the ‘big bang approach’. It is likely that it will be too big and scary to your senior stakeholders to try and do that all at once and that you won’t get approval but, if you do, it is unlikely to be successful as it is too big a change all at once for the business users to take on board.

It is far better to take an iterative phased approach and slowly but surely roll your Data Governance Framework out across your organisation.

Evolve

Do not make the mistake of thinking that designing and implementing a Data Governance Framework is a ‘once and done’ activity - Data Governance is not a project!

You need to constantly review and evolve your framework as your organisation evolves - perhaps it will restructure, and you must agree a new approach to Data Ownership or perhaps you enter new markets or merge with another organisations.

All these things will impact your initiative’s ability to remain relevant and provide the appropriate support to your organisation which is why your framework needs to evolve too.

When a data governance initiative is led as a project, it appears that progress is being made as tasks get completed. However, nothing substantial will change until the people change.

To change behaviours, attitudes, and culture, you must win hearts and minds. This is almost always overlooked when the success of the initiative is measured by deliverables ticked off a checklist.

 Follow these principles to ensure that you design and deliver a Data Governance Framework successfully.

 And if 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 2021 Round-Up

Happy New Year! And welcome to 2022. 

Hopefully, you’re feeling excited about what the new year will bring, and when it comes to implementing Data Governance you’re raring to go.

And if you’re not feeling like that - maybe you’re still eating mince pies and aren’t even daring to look ahead at what’s coming up - you’re still in the perfect place!

I’ve compiled my top 10 blogs from 2021. 

Reading some of my most popular content from the past year will definitely help kick those January blues. 

Or, if you are raring to go, they will help you take those next steps to implement Data Governance. 

So get stuck in and enjoy! 

I wish you all the best for 2022, let’s hope it’s a good one. 

1. What you need to know about Data Governance roles and responsibilities 

2. What is the number one Data Governance mistake?

3. What is Data Custodianship?

4. What is Data Ownership?

5. What's the difference between data governance and data management?

6. The difference between a Data Catalogue and a Data Glossary

7. What is Data Governance?

8. Why does my company need Data Governance?

9. Can I fast-track the creation of my data glossary by using standard definitions?

10. How do you manage data ownership on a big data platform?

If you need a deeper dive into a structured approach to design and implement a Data Governance Framework successfully, don’t forget that I offer both face-to-face and online training! You can find out more about these on my website here: https://www.nicolaaskham.com/data-governance-training/ 

If you want to chat about your Data Governance Training requirements, why not book a call by using the button below?

Data Governance Interview with Dr Gerald J Wong

I was lucky enough to work with Dr Gerald J Wong earlier this year.  He is the Data Strategy and Governance Lead at the UK Hydrographic Office (UKHO), which is a world-leading centre for hydrography and an executive agency of the Ministry of Defence (MoD). The UKHO specialises in marine geospatial data that helps others to unlock a deeper understanding of the world’s oceans. This data is shared with governments, defence users and academia, as well as available through their portfolio of ADMIRALTY Maritime Data Solutions.  

Originally specialising in Nuclear Physics and Optical Engineering whilst in academia, Gerald joined BAE Systems Avionics (now Leonardo SpA) to invent and patent sensor technologies. After diversifying with an MBA from the Edinburgh Business School, Gerald then moved to the International Defence arm of the UK Meteorological Office. There he supported weather impact predictions for remote sensors and Big Data issues for UKMO partners such as NATO SHAPE (Supreme HQ Allied Powers Europe) and several national Air Forces throughout Western Europe. Following five years at the Met Office, Gerald transferred into the UKHO to support their transformation from paper charting towards modern on-demand digital services, including Marine Spatial Data Infrastructure (MSDI). 

How long have you been working in Data Governance? 

I have formally been evolving Data Governance (DG) at the UKHO for over two years. Prior to that at the Met Office I handled ‘Big Data’ and associated Governance issues for five years. As a result, I have been operating within the DG space for far longer than my present role title suggests, and I suspect that many readers would be also able to credit themselves with much more DG experience than traditional or conventional “job histories” usually imply. 

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

My journey into Data Governance was a gradual evolution from starting as an end-user of “simple data” during my early Physics and Engineering roles in a closed-loop environment (experiments with clear start, end and/or reset points). This evolved via “richer data” forming a crucial input into decision-making analyses around weather impacts for sophisticated, but well-defined static scenarios, which started to include the need for Data Governance. The final step was moving upwards to formal Data Governance within a dynamic ecosystem of complex real-world dependencies and feedback loops, namely the oceans and human activity above and below the waves, which is dependent upon the physical environment, yet also can affect the physical environment, leading to future changes in human decision-making, and so forth. 

This natural evolution tracked my career development from roles with constrained remits – laboratory experiments – to roles that included increasing needs to consider human (mis)behaviour around data and technology, which also includes how to practically integrate data and information to support real-world, socio-economic decision-making.  

This evolution closely mirrors the typical hierarchies of corporations and institutions, from the end-user Tactical level of ‘how’ to do something with data, the middle-management operational level of ‘what’ to do with data, and finally the Thought Leadership level of ‘why’ to adopt a certain business strategy for data in the first place. Hence in today’s information economy with increasing adoption of Artificial Intelligence, there is a rapidly growing need for competency and experience in Data Governance – whether that be within marine geospatial data, cyber technologies, green manufacturing, logistical supply chains or retail customer sales patterns. 

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

One crucial characteristic is a healthy scepticism and a drive to improve ineffective practices, especially where they’ve become entrenched as tradition, convention or the “way it’s always been done here”. I like to counter such perceptions within organisations, particularly those that genuinely want to evolve, with the view that “if you always do what you’ve always done, you’ll always get what you’ve always gotten”. Long-term existing practices evolved in the past to meet some requirement at that time in that environment and may have once satisfied a need very effectively, but the problem is stagnation while the market and competitors have moved forward. 

Another important trait is avoiding unwarranted change for its own sake, as the mirror opposite of static tradition or convention, but this time as the modern trend of “continuous disruptive change without strategy”. This type of “burn it all down” or wrecking-ball approach to Data Governance omits that many long-term practices can still be effective and that change needs to be incremental, integrated and monitored – not only with corporate structures but also human behaviour, means, motivation and opportunity (often the true critical factor). Adapting, modifying, and repurposing established policies or existing processes can help preserve “change capital” for those changes that are genuinely novel or necessarily disruptive. It can also mitigate frictions with those invested in existing practices, such as their users, instigators, designers, and owners; instead bringing them onboard and engaging them with the repurposing and updating. 

The third characteristic in a triangle of ideal traits with the other two, is to have a keen applied interest in human behaviour around the use (and misuse) of data or information. Traditional or conventional “Hard Governance” centres around the assumption that people only make the wrong decisions because they have the wrong information or not enough of it. Hence the traditional view of Data Governance coalesces onto hard compliance measures and management surveillance, which includes formal audits, regular in-depth reporting, restrictive checklists, with a focus on top-down, non-negotiable command and control. This approach was suited to traditional mass manufacturing of standardised products but is insufficient by itself for modern data services that are digital-first by design and characterised by near real-time changes.  

Soft Governance works with the grain of human behaviour to achieve better results by enablement and empowerment, rather than by command and control alone – principles take precedent over prescription, thus allowing an organisation to leverage the deep insights and frontline experiences of their entire workforce. Shortcut thinking, lack of active engagement and wrong assumptions are some of the key targets for a Soft Governance approach, which still always requires the ultimate backstop of Hard Governance – but meaningfully targeted and monitored using a risk-based approach. Combining the two approaches can yield outsized and transformative results. 

Finally, some supporting characteristics to boost the Big Three above include being able to transcend organisational hierarchies, stovepipes and functional siloes. It is crucial to not bury Data Governance within your Data, Digital or Technology domain but to reach out, persuade, influence, and engage far wider afield – especially with customer-facing or revenue-generating areas. The mission is to demonstrate that Data Governance is not merely a cost centre to meet a required need at a minimum level, which is the traditional, outdated viewpoint, but is a key investment in an external marketable strength that can grow business opportunities. Governmental, private and industry users of digital information services are increasingly keen to partner only with trusted providers whose Governance they can have evidenced confidence in for the assured data they consume. 

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

When starting a journey within Data Governance, the main problem with resources is the sheer proliferation of information! The key step for any aspiring learner is to self-govern their own reading by always keeping in mind that “bigger picture” Data Governance is commonly conflated with the technical details of Data Management. Though these fields are clearly interdependent to some extent, this conflation can happen even within respectable publications, so critical thinking is needed by those starting out in DG. 

The following three books are my recommendations for building a firm foundation in Data Governance, supplemented by the insights and experience from whichever business sector they operate within. Both the second and third recommendations may be surprising to those expecting technical tomes or lengthy academic textbooks. They are both inspiring reads and essential prompts for thinking differently about DG to unlock progress that is not shackled by outdated assumptions, mainly that people are automatons of a sort and behave in entirely predictable, logical ways around information. 

“Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” by Robert S Seiner is my first recommendation and is a compact, accessible book when compared to more formal textbooks, which can be intimidating and hard to apply for some. Using clear language, memorable quotes and supportive graphics, the book gives an excellent grounding in modern Data Governance, emphasising the value in a low-resistance approach by repurposing existing corporate structures and artefacts. 

“Thinking Fast, Thinking Slow” by Professor Daniel Kahneman is renowned within its field with the author’s underlying research into Behavioural Psychology earning him the 2002 Noble Prize in Economics, by evidencing the existence of cognitive biases within people’s behaviour. Cognitive biases are systematic deviations from rational behaviour that might have served humanity in the past (“Thinking Fast”), but now can interfere with rational decision-making in the modern world (“Thinking Slow”). Confirmation bias is one of the best-known examples, but there many more that can subtly exert their influence, even over professionals and experts. These can all cause real-world effects, including injury and loss of life, especially in safety-critical ‘outlier’ situations under time pressure and uncertainty. It is a relatively long and engaging read, but each chapter is self-contained to an extent with excellent opening quotes and memorable takeaways to encourage recall. 

“Inside the Nudge Unit” by (now) Professor David Halpern is an excellent follow-on from the previous suggestion, however this time showing the application of Behavioural Governance within a real-world Governmental setting. Halpern is the CEO of the Behavioural Insights Team that was instituted in 2010 by UK Cabinet Office to directly support Government efforts to create outsized effects with relatively small changes of the right type. By giving case studies and real-world examples with their outcomes, this book can inspire readers to begin considering what nudges they can instigate to encourage their existing Data Practitioners to become active and engaged “Data Citizens”. This is needed for modern DG as risk-adverse Hard Governance is akin to “The Law” that commands people what to do or not under specified circumstances. It cannot detail every possible set of circumstances and doesn’t inform how to go above and beyond to create a “Data Community”, which exploits opportunity in new circumstances and requires risk-informed value-judgements. This is ideally achieved by Soft Governance to empower those on the frontline with their wealth of both experience and insight via principles and guidelines, with the backstop of traditional Hard Governance to formally manage the most common and significant risks. 

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

The biggest challenge I’ve encountered is the institutionalisation of Hard Governance as the sole way to carry out effective Data Governance, where DG is seen only as a “Cost Centre”, with a need to have minimally acceptable Governance at the lowest possible outlay – normally for meeting auditing needs or an externally-imposed requirement. This naturally focusses upon documented check and balances, rigid procedures, detailed checklists, all supplemented by top-down command and control that is enabled by management surveillance. This was overcome by explaining that modern and holistic Data Governance places Soft Governance as a first step, which seeks to unlock both the experience and expertise of frontline Data Practitioners, by getting them involved and engaged with DG via principles of Best Practice or other channels for bottom-up Governance.  

Gaining traction with the wider workforce takes patience and consistent effort, who are naturally suspicious that DG represents yet more traditional hard measures and controls upon them. By giving “quick wins” via the simplification or removal of outdated procedures that currently hinder them the most, it helps develop trust and the momentum that is needed for more involved changes later. I consider such an approach as stockpiling a notional resource of “Change Capital”; that is built by trust, common understanding, open conversation, and evidence of success. Change Capital is a perishable resource that can be wasted, expended, or will fade over time, so ‘investing’ it wisely in further DG change efforts that will grow it can lead to accumulating DG benefits. 

Another challenge associated with the established practice of traditional Data Governance is to neglect that different communities of internal stakeholders have different measures of DG value. Drawing upon the analogy of Change Capital, it is as if these difference communities from frontline Data Practitioners to Strategic Leaders are using different “currencies” when they measure the value of DG activities. It is crucial to be aware of and accommodate such differences, to balance the Change Capital between them.  

As an example, without sufficient traction with Data Practitioners, any attempted change will not be sustainable and/or will be undertaken “to rule” with the least possible compliance. On the other hand, lack of traction with Strategic Leaders will result in under-resourcing, lack of management support, and limited room to manoeuvre around any deeper changes.  

The ideal solution I’ve implemented a few times is to encourage a common vocabulary around DG, which can “speak value” to as many stakeholder communities as possible, including externally. Using and explaining terms like Soft Governance (with Hard Governance in support) to show how DG can unlock and retain workforce talent, whilst also being able to show market partners the quality of internal DG can be an efficient way of leverage DG changes as a marketable strength and not just a Cost Centre of old. 

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

I’ve always had an affinity for the geospatial, ever since completing my Doctorate in remote sensing while at BAE Systems Avionics and Heriot-Watt University. It was a natural shift to considering weather impacts on military operations during my half-decade at the UK Meteorological Office, with a limited side dabble in Space Weather. My current role at the UK Hydrographic Office to embed Best Practice in modern Data Governance is the next step in a career chain from data creator to data analysis on its impacts and then finally to its governance within an organisation.  

My current focus in developing the Data Governance needed for an MSDI (Marine Spatial Data Infrastructure) is helping to bring together all my past insights via a vision for an inclusive socio-economic “ecosystem” of marine geospatial information. Someday I would like to progress to the grand vision of an NSDI (National Spatial Data Structure) and its Governance to unify the domains of air, land, sea, space, and cyberspace into a single coherent ecosystem of policies, people, processes, technologies, ethical Best Practice, and inclusive socio-economic outcomes. 

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

The most important piece of advice I would give relates to professional strengths to make oneself - and the practice of Data Governance - indispensable to any organisation. The October 2011 article in Harvard Business Review on Leadership Development under the title “Making Yourself Indispensable” was a key milestone in my own professional development. Although it is important to improve on weaknesses, the necessary step of further developing a personal strength is much less clear for many. Increasing how much you do of something you’re already good at will yield only incremental improvements – beyond a certain point, being even more of a technical expert won’t transform someone into an outstanding leader. The authors suggest that “nonlinear development”, using the analogy of athletic cross-training, can yield exponential results greater than the sum of its parts.  

An example given is developing the capability to explain technical problems both more broadly and more effectively, that when coupled with existing technical expertise can work together even more than alone. The leadership of major Big Tech companies exemplify this characteristic as an example. Overall, it is not enough to be a “pure specialist” in Data Governance, but the skillset to persuade and influence different stakeholder communities, along with the ability to demonstrate interdependency and common interests between them, via the common language of DG, is paramount for longer-term career progression. 

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

Sometimes effective changes around existing practices that personally connect with people daily can be more than just policies, procedures, technology and data or information. Many years ago, an organisation that I worked for ran a ‘Change and Innovation Scheme’ that invited submission for changes which might make a large improvement over enough time or repeated instances. That organisation had a cafeteria with glass doors and closers to keep them shut normally, but which always seemed to result in dropped lunch trays and spilled soup on a regular basis. The owners of the change scheme were probably expecting a selection of technical and business submissions, but one that got the most votes was to permanently keep the cafeteria doors open, thus leading to no more stained shirts and soup puddles on the tiled floor! The moral of the story is not to prejudge the changes that can make a real daily difference, but to embrace them and support them, thus showing that no suggestions are too small or trivial.  

When transforming the practice of DG at an organisation, insights via frontline Data Practitioners is crucial throughout, so one of the tasks of a DG team is to cultivate DG innovators at frontline, not just merely innovations themselves. If you lose an innovator because they felt that their suggested changes were trivialised, then you lose all the insights that they would potentially share in future, or even worse the innovator themselves to another business that will value their lived experience and insights. The message is that Data Governance also must include the people element and the improvements that they can bring to any DG journey for an organisation.

Do I need Data Governance before Artificial Intelligence?

Companies across all sectors are getting excited about using artificial intelligence and machine learning and, let's face it, who could blame them? They're definitely exciting technologies and the rewards promised include things like big revenue boosts and competitive advantage and massive cost savings - and who wouldn’t want that?

It’s no surprise that there's a rush of companies trying to adopt it. And that means that for some, the question of what should come first, AI or Data Governance, can be a little like the chicken and egg debate.

Let's face it when you've kind of faced with that kind of prize, why would you want to stop everything and do something as time consuming as data governance before you do your exciting AI and ML? But for me, there is a clear answer…

Artificial intelligence works by mimicking human processes by ingesting large amounts of ‘training’ data and analysing it for correlations and patterns and using these patterns to make predictions about future states. 

For example, a chatbot - the kind you may encounter on an online retailer’s website or in place of technical support - is fed examples of text chats and can learn to produce lifelike exchanges with people and provide help and assistance, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.

All AI starts out as a program or algorithm written and taught by a highly skilled programmer. The learning aspect of AI programming focuses on acquiring data and creating rules for how the AI will turn the data into actionable information. These rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.

The reasoning aspect of AI programming focuses on choosing the right algorithm to reach your desired outcome and the self-correction process is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.

That all means one thing: AI needs the right data in order to learn.

So, as a consequence, if you've got missing or inaccurate data, your wrong and potentially inaccurate data can and will guide these exciting technologies that your organisation has spent a fortune on in the wrong direction and so they will make the wrong decisions and the consequences could be costly and maybe even disastrous.

If you’re going to spend time and money integrating AI into your organisation, then I really feel quite strongly that if you want to reap the proper rewards of these brilliant technologies you must implement data governance first so that you do get the results you wanted. It’s quite simple: make sure you've got your house in order before you start embarking on an AI and ML (machine learning) journey.

If you are doing that and you haven't started doing data governance yet, there's a free checklist you can download from my website to help you get started.

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.

The Rocky Horror Data Show: Did you get what you asked for?

Data shouldn’t be a wild and untamed thing, but sometimes it is just that - wild… and untamed. And unfortunately for our friend Tim, he’s about to find out just how wild and untamed data can be. As this is ‘The Rocky Data Horror Show’… where the data is not what it seems.

Tim is now a couple of weeks into his new role as the new Data Governance Manager at the Magical Wish Factory, until now data governance there had been left to the head of IT, Janet. (If you missed the first blog in this series you can read it here).

When we last seen our friends Tim and Janet, they were looking at changing the culture within their organisation to successfully implement a data governance initiative and over the last few weeks, chaos and miscommunication have reigned.

Tim and Janet are quickly learning that people all around the organisation have different definitions of common business terms - and it’s giving them a serious headache and double the workload!

“WHAT are we going to do about this!?” Janet cried, banging her head on the desk.

“Well, this is all part of that culture shift we were talking about - this is step one in getting everyone singing from the same hymn sheet” Tim replied.

“Well, what can we do to fast track this? Is there a standard list of definitions we can email around?”

“If only…” replied Tim.

You see, this isn’t Tim’s first data governance rodeo, so Tim already knows that if the Magical Wish Factory is to succeed with its new initiative this important step of creating a Business Glossary that’s tailored to the organisation is not one that can be skipped over.

Tim went on “…The thing about Data Governance Janet, is that it takes a long time. And particularly in the early phases, it takes quite a lot of effort including creating a Business Glossary that suits our business needs.

“I can guarantee you that the data definitions we used in my last job at the Bubble Gum & Lollies Plant have no relevance to the Magical Wish Factory, even though they’re in the same sector.

“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 us, will very rarely work for anyone else. Only by creating your own data glossary can you be sure that you have the correct definitions within it.”

“Without these, you can’t be certain that you are using the right data or if it is good enough to use.  What if a decision had been made in the past based on incorrect data... perhaps we stopped granting wishes that related to cake, because one of the senior wish granters is shown a report that says they’re no longer popular, but after they stopped granting them, they realised that it had been the data for another product with a similar name, like cookies, for example!”

“Well, that would be terrible!” replied Janet.

And so, Tim and Janet set about creating a Business Glossary that was bespoke to the Magical Wish Factory. This starts some small, but significant, changes to the culture within the organisation.

First, Tim and Janet simply start making sure that they are defining what they are asking for from those that hold the data. For example, instead of just asking for a report containing a list of field names, the pair start including a very brief description - it doesn’t need to be much just enough to enable someone to work out what it is you want. And after setting a good example, they ask others to start doing the same.

And every time Janet and Tim define something they store that definition in a central location, thus slowly but surely building up a comprehensive Business Glossary that can be shared with the rest of organisation, allowing them to lay the foundations for the culture change needed at the Magical Wish Factory.

Stay tuned for episode 3 of The Data Governance Coach’s new series ‘The Rocky Horror Data Show’ and follow the adventures of Tim and Janet as they try to implement a successful data governance initiative at the Magical Wish Factory.

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.