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…

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

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 Horror Data Show’… where the data is not what it seems.

Our story begins on a very exciting day for Tim - his first day in his brand-new job. Tim has just been hired as the new Data Governance Manager at the Magical Wish Factory, which is a brilliant organisation at the start of its data governance journey.

The Magical Wish Factory has been introduced to data governance by the head of IT, Janet, who heard about it all at the annual conference of the Magical Creatures Association. Janet was really excited at the prospect of undertaking a data governance initiative at the Magical Wish Factory, after listening to a talk by Nicola Askham, The Data Governance Coach, about all the benefits at the conference.

The problem for Janet - and now Tim - is that Janet didn’t listen to everything The Data Governance Coach had to say. She only listened to the why and not the how, and unfortunately some mistakes have been made along the way - mistakes that Tim will now have to try and unravel.

The first issue Tim discovers is that everyone at the Magical Wish Factory believes that IT owns the data, and now that Janet has undertaken the beginnings of MWF’s data governance - it does! It’s now almost impossible to get business users to engage. They don’t like the data governance initiative as they feel that it is just one more thing that IT are “doing to them”!

Thankfully for Janet, Tim is quite experienced in data governance, and he knows how to overcome these issues because he’s read ‘The 9 biggest mistakes companies make when implementing data governance (and how to avoid them all)’ by The Data Governance Coach.

“The problem here Janet,” Tim explains “is that when stakeholders believe data governance is IT led, it can be really hard to get them to buy into what you’re trying to achieve.”

“The key to data governance success is getting stakeholders to take ownership of their data and take the lead in data governance initiatives. You’ve been left with data governance because they’re confusing the infrastructure with the data. True data governance will only really happen once we get the business to take ownership of the data.”

“So, how do we get them to take ownership of their data then? I’ve been trying for months but no one seems to be listening. That’s why you’re here now, Tim.” replied Janet.

“Well, that’s the first step actually. I am still new here. So, I’m an impartial expert and can act as a catalyst for change. I will facilitate the discussions at senior level between the various parts of the business and this will help the business to understand the benefits and increase their desire to take ownership of the initiative.”

“Wouldn’t it just be easier if we just divided it up between us - I don’t think anyone is going to listen…,” said Janet.

“Quite simply, no,” said Tim.

Tim knows from experience that successful data governance needs a consistent message - everyone needs to understand why and what the organisation is trying to achieve. But he did agree that just one person couldn’t do Data Governance for the whole of the company.

“There are quite a few different roles and responsibilities needed when you are designing and implementing a data governance framework, why don’t we look at The Data Governance Coach’s blog, and see what Nicola has to say.”

Tim and Janet head to nicolaaskham.com and stumble across one of her most recent blogs on the who’s-who of data governance. Tim explains that as well as his role of Data Governance Manager they need data owners, data stewards, data producers, data custodians and a possibly a chief data officer.

“We’re definitely going to need some buy-in to fill these roles” concedes Janet, “so where do we start?”

“At the beginning… with the culture…” replies Tim.

Stay tuned for episode 2 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.

If you have any questions you'd like covered in future videos or blogs please email me - questions@nicolaaskham.com

Or 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.

What is the number one Data Governance mistake?

Nine years ago, I wrote a report detailing what I believe to be some of the biggest mistakes you can make when implementing data governance, so in answering this question, you wouldn’t have been wrong to assume that I’d point you in the direction of that report… you can view the report here.

However, over the years I’ve come to realise that I actually missed the biggest mistake you can make off of that report. Many years of experience have taught me that what I once thought was the worst data governance you could make is a little further down the list. 

But that doesn’t mean it’s not still important and in fact, it’s quite difficult to choose an absolute number one.

So, before we look at my number one mistake, let’s take a quick look at some of the other big pitfalls and how you can avoid them when implementing your new data governance initiative.

Some of the biggest data governance mistakes 

The Initiative is IT-led 

In my experience, IT-led initiatives are too focused on tools that do things like cleansing data. The problem is that unless a business changes the way that data is captured at the point of entry, the quality of the data will never improve. 

One way or another the business needs to recognise the necessity to take ownership of their data and take charge of the data governance initiative. This is often easier said than done and may require an independent expert from outside the organisation to act as a catalyst. 

Data governance as a project

This common mistake is easily made because it seems logical to treat the implementation of data governance like any other project. But, 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. 

Attempting the big bang approach 

I will own up and raise my hand here. I have tried the big bang approach and I still have the scars to remind me that it is a bad idea! By the big bang approach, I mean attempting one major initiative to implement everything to do with your data governance framework. The result of the big bang approach is that the initiative will most likely be too big to get started in the first place.

Thinking a tool is the answer

If the whole data governance initiative centres around a tool, it is unlikely that the business would ever engage because they would be under the mistaken belief that the tool would do all the work for them. 

The answer is to take a structured approach when implementing data governance. Before you start thinking about potential tools, make sure you fully understand what you are doing and why you are doing it. 

The number one data governance mistake

Since I wrote that report in 2012, which will be a nine years ago, the biggest mistake I have seen is organisations failing to address culture change as part of their data governance initiatives. This mistake is by far the biggest and most common I see and can ultimately lead to the complete failure of a data governance initiative.

I have seen situations where people have actually designed a really great framework that is ideal for their organisation, but it's been not successful because it's not implemented properly because they failed to address the culture change side of things.

The result of that is your business users, your stakeholders, they just feel that data governance is being done to them and definitely not for or with them as it should be.  In this scenario they tend to do as little as possible of what you're asking them to do, or even nothing at all, if they can possibly get away with it. 

Simply, can't start to manage your data as an asset and realise the value of it if you don't address that culture change.

How to avoid the number one data governance mistake

The first and most simple thing is to apply some really good change management techniques and if you are not well-versed in them, I'm sure there are people in your organisation who are, but it boils down to lots of very good quality communication with all of your business stakeholders.

This is going to be different communications for the different groups of stakeholders about their role in the data governance implementation and making sure there is good training in place for everybody in your data governance framework who has a role to play like data owners or data stewards.

It's really important that you bring these people along the journey with you, because if you don't address the culture change your data governance initiative is never going to deliver the benefits you were hoping for.

The rest of the report I published back in 2012 is also still available on my website for free, if you’d like to take a closer look at some of the other mistakes I’ve identified over the years that aren’t addressed 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.

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

The language used when discussing data governance can be incredibly subjective. Data governance is full of jargon and buzzwords, which all mean different things to different people. And it can be very confusing for people who are new to data governance or who move between industries and organisations to keep track of what means what and where.

This is something I have spoken about a lot recently in my blogs and videos as I try to break down some of the key words, phrases and definitions and make them as accessible as possible to you.

The question of the difference between data governance and data management is one that comes up every now and then, and I was most recently reminded of it after I spotted an article on LinkedIn, which, for me, unfortunately did not cut the mustard in terms of helping the reader fully understand the difference between these two terms and what they mean in a practical sense.

The wrong answer

This particular article said that data governance was all the things you can do to manage your data, so the rules and what you would want them to do, and data management was the technical implementation of it.

However, that is not a useful definition and I believe it will confuse data users, particularly those who are in the middle of trying to implement a data governance initiative.

I believe that before we begin to understand the difference, we first need to understand what both of these things actually are.

What is data management?

Data management is the umbrella term for all the different disciplines that you can use to manage and improve your data better and data governance is just one of those disciplines.

What is data governance?

Data Governance is all about proactively managing your data to support your business achieve its strategy and vision.

What's the difference?

I believe the best way to explain the difference is to refer you to the DAMA wheel.

DAMA is a data management professional association, of which I am a director of the UK chapter. And if you are interested in data governance or are involved in the implementation of data governance initiatives within your organisation then you probably should consider joining your local chapter.

Now, DAMA International, amongst many things, produces a data management book of knowledge known as the DMBoK (Data Management Book of Knowledge). It’s a comprehensive guide to data management standards and practices for data management professionals.

What you will find in there are chapters on everything related to data management.

There are descriptions and summaries of every single discipline and for many years now they have put all of these together in a diagram called the DMBoK Wheel or the DAMA Wheel.  This wheel is broken up into segments with each data management discipline given its own section around the outside and in the middle, we have data governance.

Now, this wheel can sometimes be the source of people’s confusion as they interpret it a little incorrectly. They believe that data management and data governance are the same because data governance is at the centre of the wheel and therefore that the terms must be interchangeable, but I can tell you that's not the case at all.

Data management is the umbrella term for all the different disciplines that you could use to manage and improve your data better and data governance is just one of those disciplines. DAMA puts data governance in the centre of that wheel because it actually underpins everything else.

Now, many of us start data governance initiatives primarily to monitor and improve data quality, but data governance is aligned to and supports all other data management disciplines - whether you're talking about master data management, reference data management, data warehousing, data modelling or data architecture.

There are so many out there disciplines out there, but once you have a good framework in place, through having data governance, you have clear roles and responsibilities and it is very easy for you, whatever discipline you work in, to know who to contact to get consistent decisions made around your data.

Quite simply, data governance is one of many data management disciplines. Data Governance gives you the framework of roles and responsibilities and processes to enable you to understand your data and manage it better. So, data management is the overarching umbrella term and data governance is just one of the disciplines that sit within that.

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 Neil Rutland

Neil is the Data Governance Lead at CLS. I have been helping CLS with their Data Governance periodically for a while now and always enjoy working with Neil. He has a long history of working in data-related disciplines, making the move into data governance at the end of 2019. Before this he worked in data analytics, application support and helpdesk roles which have given him many different viewpoints on data as it travels through an enterprise.

How long have you been working in Data Governance?

I started working in Data Governance in October 2019 when I transitioned from a role in Data Analytics. At the time I had little experience of Data Governance and it was a steep learning curve to get up to speed with both the work that had already been done on the initiative and the basic principles of data governance. It has been an enjoyable process to align the things I am learning with the existing work and see how I can take it forward with new concepts.

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

Perhaps a little by accident, but a happy accident! I have a varied background in data with previous roles including application support, warehouse development (ETL) , business analysis, data analytics and business intelligence development. This has given me the opportunity to see data at many points in its life cycle and I have developed a good overview of the potential pain/failure points.

It was a natural move for me when I became aware of the Data Governance initiative because it combines many areas of interest from data storytelling to the more fundamental ‘foundational elements’ like the data glossary and development of data quality rules.

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

I like this question because I think there are many attributes that contribute to ‘success’ when it comes to Data Governance. 

I’m still relatively new to the area so I’d prefer to say what characteristics I have that make me ‘suitable’ for a Data Governance role… success at this stage is a very difficult thing to measure!

I think pragmatism is a key trait. It is OK to aim for the stars, but it is better to get there in small steps than get lost in planning for one big leap.

I also think calmness is important. There are many times when you will be challenged when trying to implement Data Governance and you need to be able to take a step back and rationalize these challenges. The challenges I have faced have provided the best learning opportunities and helped me understand what I can do better, both technically and in dealing with stakeholders.

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

I’d certainly recommend the DAMA DMBoK to anyone with a serious interest in a pure Data Governance role. There is a lot to absorb but it gives a useful framework for organizing your thoughts. This is critical when you are new to a subject and may need that extra bit of support to believe in your own opinions.

I also come from a background in analysis and visualization so I am keen to read Data Storytelling for Data Management by Scott Taylor. I think this is an area that can really be exploited to ‘sell’ the good work that data governance does.

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

To date, I have only been involved in one implementation and the biggest challenge was taking over an initiative that saw a significant knowledge drain.

I was required to learn about data governance, how it had been applied to the project and also keep the project moving. Whilst it was challenging, the focus it gave certainly helped me learn quickly.

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

I think I am in the right place at the moment. The data is plentiful, complex and has many uses around the organization and market?. It is an ideal starting point as I am learning how to structure my work and interact with people to get the best results.

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

Deliver something tangible.

Data Governance is such a broad, and at times abstract, subject it can be difficult to get people to understand the collective journey you are on. I have found that most stakeholders, even sceptics, respond well to tangible deliverables and this can really help drive an initiative forward.

There’s always ‘one more thing’ that you can do with Data Governance. Don’t let your enthusiasm to fix everything stop you from making progress towards fixing something. This is something I am occasionally guilty of and am working to correct

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

My whole Data Governance experience has been memorable. As a result of the onset of the Covid-19 pandemic in early 2020, the world went into lockdown 3 weeks after we had started working with our Data Stewards. Transitioning to a remote working environment whilst trying to lay the groundwork for data governance has been challenging but full of opportunities to learn and adapt. 

I’m looking forward to the rest of the journey!