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!

What you need to know about Data Governance roles and responsibilities

The Who's Who of Data Governance

If you’re just starting out on your Data Governance journey, you may very well feel like you are playing the classic 80’s board game ‘Guess Who?’ when it comes to sorting out who should be doing what.

There are a number of different roles and responsibilities needed when you are designing and implementing a data governance framework and it is easy to get confused over which roles you really need and what you should be calling them.

But don’t worry ,in this blog I’m going to go through them all and tell you everything you need to know!

Stop and breathe

Firstly, don’t start feeling overwhelmed. When implementing data governance there will be many challenges and different mountains to climb; certainly, one of them will be the naming of the different roles. What if someone tells you that the term “Data Owner” won’t work at your organistion?  

Well you certainly don’t need to panic if this does happen.  In this article I’m going to share what I consider a starting point, but you don’t need to stick to the names of the different roles too rigidly. These are just my best practices, but go with the flow of the organisation you're working with. It’s much better to use terms that suit the culture of your organisation - rather than fighting to use role titles that you’ve read in this blog or found with the help of google. It’s what the roles actually do that is the important bit.

Data Owner 

A Data Owner is a very important data governance role; in fact, so important that I have written a whole blog dedicated to explaining ‘What is Data Ownership?’, you can read it here.

A Data Owner is someone very senior in your organisation, who is accountable for the quality of a certain set of data. And I must emphasise that it really does need to be someone who is senior within the organisation because data is owned by the company.

You can expect to identify a number of Data Owners and they should all be based in the business side of things.

It’s often a bit of a debate as to who owns the data. Many think it belongs to IT, but it is senior business users who are the true data owners.

The Data Owner needs to be senior so that they have the authority to approve changes to the systems and have the budget and resources to cleanse and fix data. Finding the right data owners can be hard work but if you find and engage the right people it will make a huge difference to the success of your Data Governance initiative. 

If you want more detail on how to find them read my blog ‘How to identify the right data owners?’ here. You can also read my ‘Can there be more than one Data Owner per data set?’.

Data Owners are key but we also need Data Stewards, as Data Owners will be able to approve things but, often, they won’t want to get their hands dirty and will be unlikely to understand the details of the data. 

So what is a Data Steward?

Data Stewards 

Data Stewards act as support for the Data Owners; they are often a subject matter expert in a particular set or subset of  the data and are in a good position to work through the different processes that get rolled out. 

For a deeper dive into the difference between Data Owners and Data Stewards, read my this old blog post. 

Having a Data Steward is key to the Data Governance implementation, as they are the people who do most of the work to make it happen.  Because of how essential Data Stewards are, I've written another blog that gives a more in-depth look at the role of a Data Steward. You can read it here. 

Data Producers 

Often, a Data Producer is a more junior role (although, this all depends on the organisation). The Data Producer's main role is to enter the data into the system. 

It’s really important that these people understand that they need to capture data in accordance with the requirements of the person consuming the data. This is often a missed step which can lead to bad data. In my experience, Data Producers don’t often deliberately capture data incorrectly. It’s just that no-one took the time to tell them what was needed and that leads nicely onto the next role that of Data Consumers...

Data Consumers 

I use this term to refer to anybody that is using or consuming data, and it really is these people that are responsible for defining what makes the data good enough for them to use. 

These people should be using the Data Governance Framework to communicate what they need from the data and defining how they know whether the data is good enough to use (also known as data quality rules).

Data Custodians 

This role is related to those working in IT, and their main responsibility is maintaining the data on IT systems in accordance with business requirements. 

I must stress that IT do not own the data, just because it resides on systems they support and maintain. 

I talk more about Data Custodians in a previous blog. Read ‘What’s the difference between Data Owners and Data Custodians?’ here. 

Data Governance Manager (and team)

Data Governance frameworks do not last very long when no one is responsible for them. So… bring in the Data Governance Manager!

This person should be responsible for evolving and embedding the framework within the organisation. This is the only new role you will need to fill when implementing Data Governance for the first time.  Everyone else already exists - you just need to formalise their responsibilities regarding data.  Depending on the size of your organisation (and the investment they are willing to make in Data Governance) you may be lucky enough to have a Data Governance Team headed up by the Data Governance Manager.

And once again, it doesn’t matter what you call this person, but there really needs to be someone responsible for the initial implementation and after that checking that the cogs are still turning, things are still running smoothly, policies are being reviewed regularly and processes are being updated when they need to be. 

Chief Data Officer 

I strongly believe that you need executive sponsorship for your data governance initiative - it is the only way to make the initiative truly effective. 

However, I don’t believe that there has to be a ‘Chief Data Officer’, so don’t panic if your organisation doesn’t have one.  If your organisation has a Chief Data Officer then the Data Governance Manager should be reporting to them and they should be the executive sponsor of your Data Governance initiative.  But don’t panic if you don’t already have a CDO, find an executive who understands the value of data and get them to be your sponsor. I have helped more organisations to successfully implement Data Governance that don’t have CDOs than those that do! 

Finally at the appropriate time you will want to start gathering groups of these people together.  You can find out more about  Data Governance Committees, Forums and Working Groups by clicking here.

I hope you’ve found this blog a useful place to start understanding Data Governance roles and responsibilities  better. 

If you are just starting out, why not download my free Data Governance checklist here and get on the right track to developing a framework that suits your organisation’s needs and ambitions.

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

Or if you’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 fast-track the creation of my data glossary by using standard definitions?

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

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

The best way to explain why is to give you a real-life example of a conversation I had with a client a few years ago.

This particular client had been making slow but steady progress with their Data Governance initiative and they had decided to bring forward some target dates for the completion of certain tasks, and to help with this they appointed a project manager.

This had happened in between my visits and so when I next visited, I had a conversation with the new project manager, and it went a bit like this:

He said: ‘We've got to get the data glossary built sooner and therefore we've got to find a better way of completing the data glossary than getting the data stewards to draft it and the data owners to approve the definitions.’

And naturally, I said: ‘Well, I can't think of a way that would be successful that I've seen to date.’

He replied: ‘We've got a really good idea. There's a chap that's joined that department over there. He has just come from another company in the same industry. He's got some time spare, and we've given him the fields that we need to be defined.’ 

I replied: ‘But he doesn't know what this organisation means by those terms.’

And the project manager said: ‘But they are standard terms used in the industry.’

Unfortunately, as I predicted, it was not fine.

This team member spent quite a lot of time filling in these definitions but when we shared those with the data owners and the data stewards there was a lot of confusion and back-and-forth about various terms and what they mean in different contexts.

This will almost always be the case within organisations. In my experience, where I have worked with multiple clients in the same industry, it is very rare for people to use the same jargon in exactly the same way.

Many organisations think they use terminology in the same way, but when they start comparing - particularly when people move between companies like in our example - there are always subtleties and sometimes it is even more than a subtle difference.

I would love to say that this would be a great way to fast-track the creation of a data glossary and that a bank of standard definitions is the answer to all your prayers, but I'm afraid it's probably more likely to result in confusion.

In the example above, it also doubled the workload and prolonged the creation of the data glossary. It also risks disengaging your data owners and data stewards; therefore, I would advise avoiding that approach if you can. Sometimes it is better to take the long road for a better outcome.

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 is Data Governance?

(What you need to know if you are just starting out)

I’ve been writing blogs on my favourite topic of Data Governance for many years now and it only recently occurred to me that I had never written a blog covering what Data Governance actually is!  I’m not sure how that happened as I know that understanding what it is, is a vital first step. In fact, it is the first thing I cover in my training courses.  So, of course, I had to resolve that straight away and this blog is the result.

The business value of Data Governance, is often lost amongst the confusion surrounding what exactly it entails, not to mention the overlap with other relevant but separate disciplines, so it is important that we are clear what we mean when we use the term.

So what is Data Governance?

I’ll be honest it’s a topic that attracts some amount of confusion. A quick Google search will unveil a whole host of definitions and explanations that range from really useful, through confusing to downright wrong.  And I’ll be honest a lot of them are… well… a little bit boring!

The prolificacy of so many differing definitions a of source of much frustration, which I explore in more in this blog: Why are there so many different Data Governance definitions? 

One of the better definitions I’ve found online is this: 

“Data Governance is the cross-functional discipline of managing, improving, monitoring, maintaining and protecting data,” although I disagree with the “protecting” part (see the section below on what Data Governance Is Not for an explanation of why) it’s not a bad definition. It just won’t work for the business users you need to influence.  

Think about it from their viewpoint – does it sound like something that is going to help them do their job better; or more rules and regulations that hinder them from doing their job? My experience is that if you use that type of definition when first introducing the topic to them you are likely to get the latter reaction.

My experience has taught me that you need to explain why your organisation is (or should be) doing Data Governance first. The “what Data Governance is” can come afterwards once your audience have agreed that they want it

So I use the following definition that I developed years ago:

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

How many senior stakeholders wouldn’t want to know more about something that is going to help their organisation achieve its strategic objectives?

Why Does Your Organisation Need Data Governance?

Of course, after sharing such a definition, you need to be able to explain it in more detail if asked.  So make sure you have done your preparation and know what benefits your organisation could achieve if they implement Data Governance. If you’d like to find out about determining these I recommend that you read: Why you Need Data Governance.

Are there better names to call it?

You may be thinking wouldn’t it just be simpler to change the title to better convey the benefits of Data Governance? And that may be something you wish to consider for your organisation. As I explain in Does it have to be called Data Governance? the topic is a generally misunderstood one and if changing the banner under which it is delivered, to make it suit your organisation, clears up any confusion then I’m all for it.

As long as the scope and purpose of your Data Governance initiative has been made clear from the off, a name change can be helpful in some cases. 

Of course, that can be easier said than done if it’s not already clear what should be included in such an initiative in the first place!

What do you do if you are “doing” Data Governance?

There are many activities you may decide to include in your Data Governance initiative, but it is important to remember that one blanket or standard solution won’t work across all organisations. In fact I’ve been asked so many times over the years about standard frameworks for Data Governance – that I wrote this blog to explain why one wouldn’t work: Where can I find a Standard Data Governance Framework?

You need to design a framework suited to your organisation’s needs. It’s important that you work out what your organisation needs from Data Governance. No one is going to thank you for starting a major initiative to document data lineage if there is no immediate value in doing so! You need to look at which activities will help you deliver the benefits you hope to deliver for your organisation. In What should you include in a Data Governance initiative? I cover a number of steps you can go through to work out the ideal scope of your initiative and in Do you know what is in a Data Governance Framework I provide an overview of what a Data Governance Framework consists of.

What Data Governance is not!

Now we’ve explored what Data Governance is exactly, I’d like to end this article by looking at what it’s not.

Seems like a strange way of looking at things, right? But, given that a number of the misunderstandings around the topic arise, it’s worth clarifying a few things:

Though undeniably linked to your Data Governance framework, Data Protection (also known as Data Privacy) is often confused with DG. It specifically revolves around the protection of personal information and although more recent Data Protection regulations, like GDPR, do have requirements that are more easily met if you have a Data Governance Framework in place, Data Governance is a separate discipline supported by a different expert team in your organisation.

Likewise, Data Retention, which focuses on how long you should hold onto data before deleting it, is something which your Data Owners should be consulted on but is a fundamentally different discipline.

Records Management or Information Management do bear some similarities to Data Governance, though they focus on the handling of complete records (whether they are analogue or digital) rather than electronic data which are the building blocks of records/information. 

While these separate disciplines all carry value in their own right and can (and should) be aligned with your Data Governance framework, they are ultimately separate. 

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

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

I hope you’ve found this blog a useful place to start understanding Data Governance better. If you are just starting out why not download my free Data Governance checklist here and get on the right track to developing a framework that suits your organisation’s needs and ambitions.

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 difference between a Data Catalogue and a Data Glossary

Data Governance is full of lots of jargon and terminology which can mean different things to different people. It’s all very subjective and this is usually because of the culture within a particular organisation.

The way the various terms are applied within organisations can vary their meaning. And that’s ok - but you should also be wary of it.

 This is something I recently discussed in my ‘What is Data Custodianship?’ blog and even more recently than that I’ve noticed a lot of confusion around what a Data Catalogue is and how this differs from a Data/Business Glossary. 

It’s important to make sure you fully understand the meaning of the terminologies within the context of the organisation that you are working with so that there are no crossed wires. Don’t make assumptions about the meanings of particular terms - and if you are ever in doubt, then ask.

However, there are some distinct differences between these two things, and I am going to do my best to clear them up for you.

 What is a Data Catalogue?

A Data Catalogue is considered a core component of modern data management.

Very simply, a data catalogue uses metadata (data that describes or summarises data) to create a searchable inventory of all that organisation’s data assets. So, a Data Catalogue is a detailed inventory of all the data assets in an organisation, which is designed to help the data professionals within that organisation quickly find the data they need for whatever purpose they may need it for. It’s basically a tool to help you find that needle in your data haystack.

Data Catalogues can evolve with an organisation and over time, the metadata within a Data Catalogue can be enriched and updated to support better data discovery and governance within an organisation.

A data catalogue provides context to enable data analysts, data scientists, data stewards, and other business data consumers to find and understand a relevant dataset for the purpose of extracting business value. Data Catalogues can also support such individuals in acting upon it to realise the true value of the available data.

Functions of a Data Catalogue:

• ‘Dataset Searching’ – supporting searches for keywords, can also allow a user to check how frequently search results are used

•  ‘Dataset Evaluation’ – allows you to preview datasets to ensure you’re getting the right data you need to analyse (for instance, by previewing the data in question, checking data quality and user ratings, etc) – saves you potentially downloading the wrong data

• ‘Data Access’ – Data catalogue can aid the process of search to access

 What is a Data or Business Glossary?

A Data Glossary is an exhaustive list of all terms used across the company with definitions. It comes back to what I said at the start… organisations use lots of jargon and terminology which can mean different things to different people.

A Data Glossary defines the terminology which organisations use when discussing their processes and their data. It is purpose is to define the business/data terms within the organisation.

The Data Glossary is designed to keep everyone on the same page and using a common vernacular to ensure clarity and consistency between departments. Think of it as a dictionary for a particular organisation (but not a data dictionary – that is another thing entirely – you can read more about what that is and how it differs from a data glossary here: https://www.nicolaaskham.com/blog/2017/11/8/what-is-a-data-glossary-and-how-is-it-different-from-a-data-dictionary?rq=dictionary)

The Difference

A Data Glossary does not define the data like a Data Catalogue does. The data glossary defines the terms we use when discussing the data and who owns that data.  A Data Catalogue contain more technical metadata to help you find and locate your data.

A Data Glossary and a Data Catalogue are two different things (which can be linked to provide extra value), although both have their place and can be very useful to organisations when implementing data governance.

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.