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.

Can I use our existing IT Incident Management Process for Data Quality issues?

Getting to the root of a data quality issue

I’ve recently been going over with a client the pros and cons of utilising their IT incident management process to handle data quality issues.

IT incident management involves the creation of a log to help record and manage any issues that crop up in relation to IT supported systems, the goal being to reduce any adverse impact on your business operations. Since most organisations have such a formal process in place, it is logical to think that the obvious thing to do is to add the handling of data quality issues to your existing IT incident management process.  

However, it is not always a straightforward decision and while there are undoubtedly some advantages from combining the two processes, there are also some downsides.

This is a much debated topic with many experts taking different stances over the viability of combining the two processes. I think the answer on whether it is right or not will depend on your organisation, it’s set up and culture. So, I thought it would be useful to summarise the key points to help you make your mind up about whether it is the right option for you and your organisation.

Many of these points arose from a LinkedIn discussion I was involved in some time ago during which many felt that the use of an IT incident management process for data quality issues does come with some notable advantages, chiefly:

• Providing users a single central location to log any potential data issues. They don’t have to think whether it is a data or system issue - they just have to report it to one central place.

• You can usually reuse the  available workflows, tracking and reporting for data quality incidents.

• It can encourage more efficient meetings concerning data, as details recorded may inform broader, fleshed out reports.

But will this work in practice?  With a quick resolution often prioritised over all else, temporary fixes often result when it comes to addressing data quality issues using an IT Incident Management process.

When we are looking to fix data quality issues tactical fixes are not ideal, particularly, if it can be prevented from occurring in the first place. That’s where the implementation of a data governance framework comes in.  Data Governance is about encouraging more proactive management of data quality, seeking sustainable improvements and identifying the root cause of issues.

Implementing tactical fixes instead of addressing the source of the issue is the most common issue from using your IT Incident Management Process to handle data quality issues, but other downsides include:

• Some of the tools utilised within IT incident management aren’t necessarily connected to business processes, so any data quality resolutions which require heavy business involvement to correct, can be difficult.

•  The burden of having to tag or identify which particular issues are data quality specific.

• Data quality issues can be unnecessarily escalated if SLAs haven’t been changed to reflect the differing timescales for such incidents in comparison with normal IT issues.

As helpful as it can be to reuse an existing process it can promote a culture of ‘fixing’ rather than ‘solving’. This is why it’s so fundamental for organisations to invest in a proper data governance approach to ensure that the best decisions are being made on how best to tackle data quality issues.

However, an IT incident management process can be a viable starting point. It may be equitable to trimming weeds rather than ripping them out by the roots, though, it does nonetheless keep your organisation aware of data quality issues as and when they pop up.

That being said, it’s always worth keeping in mind the end goal of sustainable, long term improvements to your data quality and the continued management of it. An IT incident management process can be a brilliant short-term fix but it can’t compete with the confidence and reassurance a data governance framework provides over the long-term.

There’s just the small task then of convincing your organisation to opt for the cultural change necessary to reap such long-term rewards!

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.

Why does my company need Data Governance?

If you plan to implement data governance in your organisation, it’s really important to understand why you are doing it. This can often be a long and thankless process, and some might argue it’s not for the faint hearted, so understanding ‘why’ is crucial in order to get the most out of your data governance journey. 

Now usually in these blogs, I address a ‘frequently asked question’, but actually this one isn’t, and I wish it was asked much more often. In fact, I think it is so important that you're able to answer this question that it is the very first item on the free data governance checklist that you can download from my website.

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.

I've seen people make the mistake of spouting things like, ‘oh we're doing it because it's best practice’ or ‘we work in a regulated industry and it's required’, but if you do it for that reason, you're likely only to do the bare minimum to tick the boxes required by your regulator and you are going to miss out on most of the benefits that are to be had from implementing Data Governance.

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.

All this means you are going to really struggle to get stakeholders to buy into your data governance initiative and ultimately that means that you're not going to get the support you need for it or the funding and everything you've done to date is just going to be wasted effort.

So, what do you do then? This is slightly more complex because the answer will depend on your organisation’s specific circumstances. Each and every organisation is different and why your company is doing data governance will be different from another and probably even different from your closest competitors. This means there is not one standard approach that I can give you a list of that will work for everybody, but what I can do is tell you how to work it out for yourself. 

There are three things you need do to figure out your ‘why’. The first is look at your corporate strategy. Look at the objectives that are listed in there and work out if your data is currently well understood and good enough quality to help deliver those objectives. If the answer is no, then you've got a really good way of explaining why data governance is needed to help you achieve your corporate strategy.

The second thing I would do is look at your data strategy, if you have one, and if you do I hope that there are already some sections about data governance in there. If there isn't then you need to work with the person who owns the data strategy and work out what activities in there are they planning with the data and why you need data governance to support those activities. 

And then finally, I would go and search for your data quality horror stories. These are instances where things have gone wrong because either data is missing, or you've got poor quality data and things have gone wrong as a result.

If you gather together all that information you can then do some analysis to identify the drivers for data governance in your organisation. With that information in hand, you'll be able to talk to anybody, whether they are senior stakeholders or the business users down at the coal face and you're going to be able to articulate what the benefits of data governance are going to be to them and why their organisation needs it - and that is going to make you be so much more successful in your data governance initiative.

If you want to download the free checklist that I mentioned, you can download it here.

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 Interview with Karima Makrof

Karima Makrof started off as consultant focused on Oracle ERP implementation, before moving onto the soft(er) side with diverse roles within data governance, data quality, enterprise architecture, information architecture and other lead roles in Information Management. Karima currently has the role as Information Manager within Swedbank - Large Corporates and Institutions.

In her free time, she runs a lot, mostly marathons, and added cycling and swimming recently to get further challenged in Ironman triathlon distances. Interest in data management is fully integrated in summer reading, as well as detective or novels.

How long have you been working in Data Governance?

I would say I have been working in Data Governance “for a while”. One may work with Data Governance without having it said out loud… Originally, my plan was to work in the banking industry. Trading room at first. My career started differently though. Starting at Oracle, working as a consultant, I have seen and shared quite some experiences with clients having challenges with their data. Working with process improvements when they changed from current systems to Oracle eBusiness Suite. Defining roles and responsibilities, ownership, process for approval, ensuring data quality. All this was included. But I cannot really recall that it ever was called “data governance” back then.

Moving on to ESAB (Welding and Cutting), my role was to develop and lead the team to manage our main data (customer, supplier and articles), as well as set requirements and support the harmonization of processes for those central data , working with the implementation and rollout team, which meant introducing and implementing new processes, with roles and approval workflows, as well as embedding data quality in those. Operational responsibilities. Once again, I cannot recall that it was ever said we dealt with data governance. Until starting to share experience through being speaker at conferences  Following roles in my career have been with Data Governance in the center, extended to information management areas (master data management, information architecture, data quality, modelling and even the tech stuff needed to support all this).

I like bringing structure and understanding in ways of working. To get better results, being more efficient, considering data and facts to improve the decision-making process. Collaborating with people and supporting them in their approach and use of data are other parts which attract me with the data governance area. Sometimes, talking with each other might bring solutions. Changing slightly roles and how they interact, as well as setting clear guidelines on how to work with each other – this is what data governance is for me.

Having had roles including data governance for the past two decades is not really what I would have guessed at my diploma ceremony of business school. But no regret to have let the trading room dream for working with data governance and other data management areas. I learn every day. I grow every day through meeting people and figuring out how to tackle their data challenges. I find challenges to solve every day. It is also surprising that some seem to “discover data governance” now. 2021… Better late than never though. Having a background with many implementations of ERPs has also helped me to see several cases, how to resolve issues, how to NOT resolve issues and most important listen to the people to understand the overall picture… The key learning has always been to deal with people first. Technology can support at any time. But people are the most interesting part of the data governance work. Often the deal breaker (or make). Closely followed by processes. If you can get people to express their needs out loud, their challenges with data, what they want to get out of it, if you can thereafter define or adjust existing processes with clear governance (roles, responsibilities, ownership, accountabilities…) which can be realized in a pragmatic way, then the technical part is a piece of cake (almost).

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

I am open, transparent, listening and letting people express their thoughts and needs. Having the ability to get people to work together when setting them in the same room (skill which is not to be underestimated…).

To be successful with data governance, it is a daily part of my work to facilitate, to get input from different stakeholders and link the dots together. Be a spider in the web (even though I really do not like spiders, the reference is clear. Make things happen…).

Establishing a culture of feedback is also key. This is what makes the overall data governance work efficient: to know what works or not, and to be able to adjust and correct.

Being pragmatic. “How to convince people” is also not a neglectable skill. Not everyone is keen on listening to “data governance stuff” or that “there is a new process, and this is what it is impacting you…” By listening to people and understanding how they currently work (and their expectations), I am always taking a very pragmatic view and approach to explain the “What’s in it for me?” when working with different parts in a company. To be able to understand their needs and see how they can interact with each other to move towards the same direction (and yes… having a special power for getting rid of silos. Between units, between people, between processes… This is a very special skill this one ).

With the roles I have had, I also develop the ability to talk with the myriads of stakeholders in a company. To explain data governance to top management, and to people working in a factory requires adjusting your message to their view of the business. To their challenges. To get the buy-in, even when it is tough and a decision “from above”. To sell the need for structure, even when hearing “we are doing well without it (ie. it=data governance)”. Being able to have a holistic view on how to resolve data challenges, seeing the bigger picture (and ability to explain it): once again, this “connecting the dots”-ability.

Endurance... With all challenges existing in a company, data governance is just one of them. You must show that you are able to run the distance. I do not easily give in. Not afraid to question a process or a role, comment, again and again to understand, ready to try out new ways. If it fails, it is one piece of the overall puzzle: go up again, think again and learn from it. Be flexible (some might want to add "be agile" here :-) Sure. Just be certain to evaluate why it did not go as expected, why the results are not there or different. Analyse, re-boot.. Data governance work should not take decades for being implemented or leading to results. On the contrary: if it takes this long, it is probably done wrong... and you have been waiting for something that will never come...

And yes, I very much love working with Data governance and other data management areas. Having some enthusiasm to share, as well as competences in the area will definitely make one successful at data governance.

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

This is no easy question here. One may recommend a methodology bible, when others might suggest starting with the “soft parts” of data governance.

I do have a few books at home which I refer to on a regular basis. Old ones are still very good, new ones are adding to the newer challenges (especially for example if the past year with a pandemic worldwide has been affected with how we work with data governance). I like to keep myself up-to-date with this discipline. And nowadays there is a flora of books or resources making data governance more “approachable”. Below are books which would bring a good start for many (and routined practitioners too…):

Non-invasive Data Governance, by Robert s. Seiner. I very much like the ease of description, the ways of describing the roles and the approach to implement them in a non-invasive way.

Data diplomacy, by Håkan Edvinsson. Having worked with data management, information management, data quality, data governance, information architecture… for a while, it is always refreshing to read new approaches and views on those areas. This book is bringing new thoughts, and definitely worth checking.

Data Governance, by John Ladley. This is very straightforward. Easy to read and digest. Good start for newbies (and for more experienced too…).

Data Governance for the executive, by James C. Orr. I received this book as a present a while ago. It is never leaving my side (almost). It is 10 years old now, but honestly very much applicable to daily work with executives (and others…). Great to start with!

DAMA DMbok is of course a reference to be mentioned. I would however not suggest taking it as evening reading right through. I found (and still do) this book very helpful for specific challenges faced in companies I have worked for or where some of my network is. I do not apply it strictly to the letter. But take regular inspiration from it.

Other books obviously can be mentioned related to Data Governance, depending on the background you have, the interests you have, and the challenges you are facing. Working with storytelling is having more and more focus lately and indeed, to get successful with data governance, you need to be a good storyteller. Webinars from Dataversity are often of a good level for both beginners and more experienced. And they often touch a lot of data management disciplines, with experience sharing based on real use cases.

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

Most likely to have a lack of sponsorship from Top Management. This would be the biggest challenge. People working daily with data (and we all do…) can well-define what they have for problems. There might even be some solutions (with or without related costs). But with data governance, you often (or always?) have to make people understand this is done for the long run. And compared data governance activities (to be implemented) to quick wins in tweaking a system or setting a firefighting squad to solve a problem, the choice is most often going for the quick fix. Top Management support and understanding is key. Data governance is an investment in the long term.

There is no magic silver bullet. I might be good at what I do, I might be able to convince people, and even find solutions, but I do not have a magic wand which could turn all this into perfection (although sometimes I wish I could be a data fairy…)

Jumping onto the latest technology or tool in belief that it will solve everything and beyond, is too often how data governance implementations start. A fool with a tool is however still a fool… Data governance is about transformation, not technology. You will get there eventually though…

Another challenge quite close to this one is the lack of resources (read: involved in the change) and right after, the belief that implementing a tool will solve data governance challenges. Change management is often lacking in most data governance or data management efforts. Which unfortunately leads to the negative view people might have to areas like data governance.

Having resources is good (and necessary), but you need the right skills set and competences in the people both leading the data governance work and the ones hands-on. Challenges are most likely here too when awareness, data literacy and change management are not part of the competences in place. Communication is also a challenge, as for all implementations.

Get the buy-in and official support from Top Management. Set a team with right competences and skills, as well as good at communicating, in order to lead the work (note: competences and skills needs can change over time. Be flexible. Adapt. Adjust.) Define the broader picture and have it communicated to all. Explain what’s in it for all and everyone. Set a clear framework with roles and responsibilities, question and revise your processes. Is that really so difficult?... Data Governance is nothing that should be done “because you have asked kindly”. It must get the proper attention, resources and correct competences, and not be left aside. Incorporate data governance seamlessly into processes will be key to success.

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

No industry in particular. Having been working in automotive, manufacturing, finance, banking, I would say that data governance challenges are quite similar. Solutions and ways of working might even be similar. Implementation can differ though. Then, there are of course regulations which are existing and very stringent in some industries like banking, making data governance needs higher, and more focus on certain areas (risk management for example).

I am working a lot through networking, exchange of experience with other practitioners from other companies and industries. So it feels sometimes like working in another company through their exchange.

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

First of all, get yourself some experience and competence related to data. Might be technical, might be process, might be people-related. As long as data is involved. This will help understand the overall picture. Do not underestimate experience with data and all the challenges all around. When you have suffered due to customer duplicates issues or delivery addresses not matching to real ones, you never forget… You know where it hurts, and how painful it is to get a proper understanding of the business when data is flawed.

Then, make it tangible. Make it pragmatic. No need to get a full-blown data governance framework, supporting a possible data strategy or else, if there is no clear understanding or plan for “how to realize all this”.

In the “non-invasive” way, what I like is to incorporate data governance in the existing structure of a company. Do not define new fora if you might have some already set and working. Include responsibilities into existing roles. Do not recruit an army of data stewards without having clear what they are going to do, and how they interact with other roles. It does not mean that it is easy. Work with change management. Get people involved.

Better start little and grow. Set a broader goal for the overall company, but if you are not ready for lots of sweat and tears (and probably fail overall), get all this piloted and then rolled-out. Manage expectations in a transparent way. Make no promise you cannot take. And yes, failing is ok. As long as you learn along the way. Learn from others’ mistakes and success too. Be patient. Resilience is key…

Have fun. Never be afraid to question existing and suggested processes. Working with data governance is actually fun. Meeting different people, with different goals and perspectives. Having them moving towards the same horizon…

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

Apart from having received cute nicknames such as MDM Queen, Master Data Fairy or Data Governance police? The most memorable experience is probably the connections I have made with people. Working with data governance, with data quality, information management, master data… from all over the world, from all industries. Sharing experience and hearing the struggle from others like yours have been brightening many days (which sometimes felt like despair). There are constantly new things to learn, new challenges to take, new silos to battle, and new friends to find. Thanks to data governance, running friends have been met through MDM and Data governance networking exchanges. This is priceless…