Data Governance Interview with James Shaw

James Shaw is a Data Governance, Protection and Management professional with experience in managing data risks and assisting data transformations. He is the Data Risk Lead at esure Group.

How long have you been working in Data Governance?

I started work in Data Governance specially about 4/5 years ago, but have broader Data Management experience prior in Data Analytics and MI. Drawing on my Data Governance experience, more recently I have moved into Data Risk Management which concerns second line oversight of key risks to Data including those relating to Data Governance, Protection and Management.

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

Like a lot of fellow Data Governance, Management or Protection professionals, I started in Data analysis and reporting where I learned how to interpret and utilise data, as well as the need to manage data effectively. Through Data Analyst roles I developed a keen interest in data risk and data management specifically, and also searched for a role that was more people focused. I believe Data Governance/Management/Protection is for people who love Data, but also love people and cultural/structural change. Thankfully I was lucky enough to start my Data Governance career under Nicola Askham herself and consider myself fortunate to have had that introduction in an industry that is very young in experience.

 That Data Governance is considered ‘unusual’ is a common (and somewhat justified) view I agree, but I don’t think Data Governance as a corporate function should be as niche or unusual as it’s widely perceived. The governance and management of Data is not just desirable but essential to maintain control and discipline over any organisation just as we do with People or Systems.

If that is perceived as unusual then it is perhaps a sign that Data Governance has sometimes been boxed in as a regulatory specialism (particularly in Banking, Insurance and other highly regulated industries), or narrowly defined as data quality deficiency meditation.

 I would like to think that actually logically, the overall governance and management of Data should eventually be given as established a platform as the governance of IT assets or similar. The emergence of the Chief Data Officer and similar functions suggests that Data Governance is establishing itself in organisational Leadership and strategy plans.

How do you see Data Governance evolving over the next 5 years?

As mentioned above, I envisage the management of Data will become a high level function in it’s own right and no longer an oddity or ‘in trend’! If Data Governance/Management/Protection professionals can frame themselves in the right way, that is as persons in some capacity responsible for ensuring the integrity, availability, security, protection, ownership and understanding of organisational data, then they will be perfectly placed to deal with emerging and residual data risk, as well as supporting data transformations that seems to commonly underpin corporate strategy. If Data Governance is able to embrace this challenge, and not shrink to tick boxing or specifics in complex data regulations, then there will be an accelerating demand for professionals who can demonstrate this experience. Every year we see an increase in Data Governance/Management/Protection professionals and this of course solidifies the position of the function in its own right.

 I also expect Data Governance to develop beyond its focus on Data Quality and apply more equal weight to all aspects of data management, which will also blur the lines between the different factions of data responsibilities. For example, I imagine Data Governance working more closely with Information/Cyber Security, Data Engineering, Data Privacy professionals etc. to deliver common objectives. Which is not a bad thing! Finally, as Data Governance branches out, I would hope to see the expansion of principles embedded in Solvency II and similar quality regulations to other and all types of data, as I believe the principle that data should be accurate, complete and appropriate to be fairly universal. As too are the principles of Data Protection, in terms of understanding, protecting and using data for the right reasons, I don’t think these principles should only apply to personal data. The regulations can, potentially, establish universal principles and ethics as intended.

What are the biggest risks and threats to organisational Data right now?

Other than large scale cyber breaches and hacking incidents, which is not the domain of Data Governance, the biggest risks perhaps lie in the volume of data that is being accumulated and an organisations’ ability to control and manage it. Data volumes and input is being ramped up and the investment in Data Governance is not always keeping pace. When that data is sensitive, confidential or personal, the risks are multiplied. Challenging the assumption that more data is always better is a huge test for Data Governance because the corporate giants of today’s world and those perceived to be at the top of the pyramid consume data on a vast scale. The important point here though is that they are able to apply the equivalent resources to control and manage their data so it becomes a benefit and not a hinderance. Organisations have to be able to understand, catalog, control, and manage their data effectively otherwise it becomes polluted and uncontainable, aka. the dreaded data swamp. It should be understood that the potential consequence (other than the increased regulatory risk) of plugging in data that is not really needed, is that the data that is genuinely needed can be diluted, contaminated and harder to find. I would advocate smaller volumes of quality data that is understood, useful and manageable rather than drowning your systems in data.

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

I would say that building data related experience is a priority, but as too is developing soft people focused skills. Data Governance is about effecting organisational management and change. So whereas a MI analyst for example may be used to delivering independently, a data governance specialist needs to be effective in driving co-operation and initiatives with the wider business. Sometimes getting other people to do things can be harder than doing it yourself! I would recommend developing yourself as a rounded individual, with a balanced set of skills. Unlike some in the industry, that while useful, I would not recommend focus necessarily on developing technical computer language skills as a priority nor would I think necessary to become an evangelical in people persuasion.

 Two of the most important skills I would empathise are patience and tenacity. You have to be able to keep going until you deliver results and be creative as to achieving that – Data Governance is never going to be easy to achieve and instant results are rare.

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

In this blog we’re going to be looking at how to manage data ownership on a big data platform. Now, this question was asked to me on LinkedIn a while back, and it was a really good question in response to a debate I'd been participating in about my very strong belief that data should only have one data owner.

I feel quite categorically from my many years of experience that you really cannot have more than one Data Owner per data set. It really doesn't work, and I don’t recommend you try it. There’s no exception this rule (believe me, I've been there, done it, still have the scars). Instead, I believe that what you need to do is find one senior person within your organisation who is going to take overall accountability for that particular data, wherever it is within your organisation.

This prompted the person to get in contact and ask: ‘How do you manage this on a big data platform when you're bringing in data from source systems which clearly have a single data owner, but are combining it with data that's owned by somebody else on a big data platform and applying some kind of algorithm to it to create some new data?’

Now that’s a big question. And I can understand if you're setting up a big data platform in your organisation for the first time this can seem fairly daunting from a data ownership point of view. But actually, if you stick to my simplified approach of one data owner for data, it's quite easy to follow forward.

 So, let’s break it down. In this scenario it's quite clear that this second set of data isn't the same data, it's new data. If we took some data perhaps owned by sales and we combined it with some data owned by finance, we can apply some logic or an algorithm to it to create some new data.

 Now, that new data didn't previously exist so that new data can have its own data owner, and the data owner is the person who asked for that data set to be created, because they are the only person who can give you the requirements for how to create that data.

 They’re also the only person who really knows what that data is going to be used for, so they're the only ones in a position to be able to tell you what it means, what it should be used for, and if necessary, what its data quality rules should be.

 That’s why I think as a general rule - it doesn't matter whether it's on a big data platform or any other of your source systems - always consider whether or not the data has changed. If that data is combined with another set of data or more than one set of data to create a new data set, it can have a new data owner.

Now, you might be wondering ‘what about the source data owners?’ but in my opinion, for simplicity, you have to think of this new data set is exactly that - it's new - and you need to find a new data owner or agree a data owner based on who asked for the data to be created, and who's going to be using it.

 Now, if you have maybe two or more interested stakeholders interested in the same data set, you have two options: firstly you can get them together and facilitate a discussion to come up to a conclusion as to which is the most appropriate person to own it and the other will be a key stakeholder.

Another option is to consider splitting that dataset into subsets until you find a way of splitting it so that everybody's happy that they are owning and responsible for the data that they really should be. Doing it any other way, I can guarantee you, is not going to work. It's going to cause you loads of pain and is going to result in people telling you that Data Governance doesn't work or doesn't help them.

So, I really cannot impress upon you enough… Only one data owner per data set and it’s often better to break your data sets down smaller if necessary, so you can achieve that.

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 Interview with Emma McLeod

Emma McLeod is a Global Data Governance Controller at Watson-Marlow Fluid Technology Group (WMFTG). She is fairly new to this role, however, has been with WMFTG for nearly 6 years. Emma began as a Customer Service Representative within the UK Sales team then progressed to her most recent position as UK Sales Support Manager. WMFTG manufactures niche peristaltic pumps and other associated fluid path technologies across 10 brands. There Data Governance initiative is to provide centralised support to sales and supply sites in circa 40 countries.

How long have you been working in Data Governance?

I’m very new to Data Governance as I only started my current role a couple of months ago. Through my previous roles, I have become very familiar with the challenges and frustrations associated with poor quality data. I’m now busy learning all I can, and exploring how I can leverage my skills and experience to become an effective leader in this area of our business. 

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

I’ve had quite a varied career path so far. I started out as a primary school teacher, then progressed in various sales, customer service and management roles. I’ve always been very inquisitive and while my roles have changed I have found the skills I learned, at each stage of my career, have supported my development and helped me quickly adapt to the demands of each new challenge.

The constant between them all has been my passion for working with others to enable success and improvement. Oh, and I also really like organising things… 

When the opportunity to work in Data Governance arose at Watson-Marlow Fluid Technology Group, it was a no-brainer as it was a chance to collaborate with our global network to enhance our capabilities as an organisation.

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

Resilient, approachable and patient.

I must admit, I probably struggle with the latter at times, especially when I’m excited about a new idea or project. However, patience is vital as Data Governance takes time before you really start seeing results.

Being in the early stages of redeveloping our Data Governance initiative means there is a long road ahead, with many obstacles to overcome. It can seem quite daunting at times (and I’d be lying if I said I haven’t had a few moments where I wondered what I’ve got myself into). I find it’s those times when I need to step back and revisit the core purpose of our initiative to regain my perspective. Having confidence in what you’re working towards not only helps you remain strong when challenged by others but also builds trust with those working with you.

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

The first books I picked up were Robert Seiners “Non-Invasive Data Governance” and John Ladleys “Data Governance”, as well as the DMBOK. They are great starting points with lots of interesting insight and guidance. Though I only really felt like I was starting to ‘get it’ when I started exploring the challenges and working out the specific needs. Some things that seemed to make so much sense when written in a book simply would not work in reality. I learned quite quickly not to get too caught up in the theory.

I have just finished reading Shannon Huffman Polson’s “The Grit Factor”. While it’s not a book about Data Governance, it was a really inspiring read that focuses on vital soft skills required within leadership roles. As she explores the importance of resilience and courage to overcome adversity. I found her reflections on purpose and building a network particularly insightful, and very appropriate for anyone working in Data Governance. 

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

I have limited experience in specific Data Governance initiative implementation, however, we did focus on data quality more locally in my previous roles. 

I found the most difficult thing at that time was retaining engagement from all data users, and building the consistent behaviours required for the processes to work. Communication is key, you can create the best process going but if others don’t buy into it then it’s not going to work.

I’m trying to apply this learning to my new role by having a strong communication plan that helps me make Data Governance accessible across the business.

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

Test the foundations before you start sprinting ahead. It’s really easy to get caught up in all the different priorities that fall into “Data Governance”, especially if you’re lucky to have people already keen to start getting things done. 

Start by establishing a solid plan with a clear vision, linked to your organisation's strategy. Then take small, steady steps to achieve some initial success. This may give you some ‘quick wins’ that will help others see the benefits, but is also not going to cause any major disruption should you find you need to rework anything.

You can find out more about Emma and connect with her on LinkedIn by clicking here.

Data Governance Round-Up 2020

Happy New Year! 2020 was a year like no other, so I hope you all managed to get some well-earned rest and are ready and refreshed. After the previous year we’ve all had, I hope we’re all ready for what 2021 might bring, and I also hope, now more than ever, that more people are ready to implement Data Governance in their organisations. In case I haven’t mentioned…I think this is an essential component of any organisational structure.

The New Year is associated with lots of stereotypes and cliches: ‘new year, new me’, ‘out with the old, in with the new’, the list goes on and on, as people set their new year's resolutions hoping for bigger and better... I usually find myself avoiding these and focusing more on reviewing and consuming content that’s going to support me in the upcoming months. And I know I’m not alone in harbouring this January habit, proven by the astonishing number of Data Governance initiatives that start-up or re-launch at the beginning of the year. 

Last year I released a ‘Data Governance 2019 Round Up’ (which you can view here); after last year’s one being so well received and useful to so many,this is a round-up of my most popular 2020 blogs. There might be one you missed, or perhaps, one of these might be more relevant than ever. 

1. How to select the Right Data Governance tool

2. Data Quality and Data Governance Frameworks

3. Cyber/Data Security and Data Governance – Siblings from the same Parents

4. How to successfully implement a data governance tool

5. Communication Perspective

6. Where can I find a standard data governance framework

7. Can there be more than one data owner per data set?

8. How Often Should you Revisit your Data Governance Maturity Assessments

9. Why is it so hard to write a data governance policy?

10. Do I Really Need a Data Governance Policy?

I hope you find this list useful, and if you have any topics you would like me to write about in 2021, please get in touch and let me know! 

I wish you all the best for 2021, let’s hope this year will be a good one! 

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? 

The tale of Dick Whittington and the missing data

2020 has been an odd year to say the least, and as we approach the festive season it’s hard not to think about all the things we’ll miss out on this Christmas. One of those things being the traditional British pantomime.

 For those of you who have no idea what I’m talking about, a pantomime is a musical stage production which takes traditional fairy tales and retells them with larger-than-life characters, slapstick comedy, jokes, gags and a big baddie we can all boo at. They’re a staple of the British festive season that we won’t be able to enjoy this year.

 But, fear not, because today I have for you our very own pantomime tale - a data pantomime. So, sit back, relax and let me tell you all about our hero Dick Whittington…

 Once upon a time… there was a man called Dick Whittington. He was a humble man. Loyal, dependable and hard-working. Devoted to his wife and to his work. He was employed at the Dense Doughnut Bakery and loved it. One day, his boss, Mr Dense, tasked him with finding a way to improve customer experience within their organisation and this is where our story begins…

Our humble Dick set out on his journey to improve customer experience. Being the eager employee that he was - and desperate to please Mr Dense - Dick went looking for a magical and instant solution. He trawled and trawled the internet but was getting nowhere fast. Distracted, he decided he would start his Christmas shopping, and headed to eBay. There, he discovered a listing for a magic lamp which he thought his wife would love. And, not one to hang around, Dick clicked ‘buy it now’ and selected express shipping.

 When his magic lamp arrived, Dick polished it furiously and to his surprise out popped a Genie, who offered him three wishes.

 “Aha!” exclaimed Dick. “That is the answer to all my problems! I will use these wishes to improve customer experience at Dense Doughnuts and still have the lamp to give my wife for Christmas!”

 So, Dick set about making his first wish and he asked for lots of shiny new technology tools to help him manage his customer experience. Secondly, he wished for an instantaneous data migration from their old systems to put the data into his shiny new systems. And thirdly, he wished for a self-service portal, so that customers could access their own records and manage them themselves.

 These were good wishes, but the old saying is true ‘you get what you ask for’. It was a disaster. The Genie hadn’t thought about the data that was going into these tools. And why would he… he’s a magical Genie, not a data scientist!

 Dick couldn’t launch his online portal because the customer data was so very poor, and they didn't want their customers to know how bad it was - that would’ve had the opposite effect of improving customer experience!  Even worse, half the data they thought they had about their customers didn't appear in the shiny new tools.  So, Dick had to go on a hunt to find the missing data.

 He searched high and low, low and high shouting (and sometimes crying) out: “Data, data - where are you!” “It’s behind you” would be the loud and increasingly angry reply from a mysterious chorus of voices.

 “It’s behind you, it’s behind you… IT’S BEHIND YOU!”

 But every time Dick turned around… it was gone. Poor Dick Whittington was on a wild goose chase.

 Feeling lost and confused Dick cried out: “Oh, woe is me. If only there was one place I could go to see what data we have in this company and where I can find it. If only we had a catalogue of Data. That’s what I should have wished for.”

 After hours and hours of searching, Dick finally found some data that he thought might do the job. But, worried of repeating past mistakes - or using the wrong data and damaging customer experience rather than improving it, Dick wasn’t sure what to do. He shouted: “IS THIS DATA GOOD ENOUGH?!”

 “Oh, yes, it is!” shouted the mysterious chorus. Quickly followed by “Oh no, it isn’t!” This went on for another hour.

 “Oh, yes, it is!”

“Oh no, it isn’t!”

“Oh, yes, it is!”

“Oh no, it isn’t!”

 Poor Dick was more confused than ever. Sherry Trifle, the resident know it all spotted poor confused Dick and came over to see if she could help make sense of his predicament.

 She said: “Well if you're not sure, do you really dare use this data?”

 “But I’ve wasted hours searching and this is all I have to show for it - I have to do something!”

 “What if you find out that it’s really bad quality, or even worse that it's the wrong data and we make the wrong decisions on it?” replied Sherry.

 “If only there was some way I could know if the data was good enough to use” replied Dick.

 Sherry said she knew a guy who could help and introduced Dick to a wonky looking chap with a big basket of dirty data.

 “Hello! I’m Wishy Washy - pleased to meet you! That looks like a lot of data you have there. I could cleanse that for you if you like?” he offered.

 Dick wasn’t really sure if that was the right thing to do but he was running out of options and took Wishy-Washy up on his offer and followed him to the laundry.

 The laundry was full of hustle and bustle. It was noisy with machines spinning round and round cleansing data and making it ‘better’. Dick asked Wishy how they knew what they were doing and how they would figure out which parts of his data were good or bad.

 “I don't know - we just put it in the washing machines, and it comes out clean!” replied Wishy. Dick was worried. And he was right to be. When Dick got his data back it was definitely different. But he was still not convinced that it was right.

 It all got too much, and Dick sunk to the ground feeling hopeless and defeated, wondering why on earth Mr Dense chose him to take on this project to improve customer experience…

 That’s when suddenly POOF! In a big puff of sparkly fog Dick’s Data Fairy Godmother appeared! She explained to Dick that all the things he’d wished for from the Genie were wrong. BUT, all hope was not lost as the things he'd wished for during his journey (one place to document all the data and where it is held, a way of knowing how good, bad or otherwise the data was and finally a sensible way to fix bad data) were the wishes he should have asked for to begin with. And that he didn't need a Data Fairy Godmother or a Genie in a lamp to give him those wishes – he could simply start a Data Governance initiative!

 The END!

 Now you see, pantomimes can be fun to visit once a year but are a long, drawn out nightmare to live in. Don’t be like Dick and don’t make the wrong data wishes! Avoid working in a data pantomime, implement Data Governance and remember Data Governance is not a project - Data Governance is for life, not just for Christmas!

 If you need help getting started then I have a free Data Governance checklist, which will set you on the right path. You can download it here

 Merry Christmas!

Where Can I Find a Standard Data Governance Framework?

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

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

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

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.

So, while I believe that there is no such thing as a standard data governance framework, I do believe that there are three key things you have to include in your framework for it to be successful: a policy, processes and roles and responsibilities. And these will almost certainly differ from organisation to organisation.

A quick Google search will pull up dozens of templates, readily available for you to download - but you know that old phrase ‘there’s no such thing as a free meal?’ It applies here. The cost to your organisation when your standard Data Governance framework inevitably fails to get the desired results could be huge.

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

So, if somebody tells you that they have a standard Data Governance framework that's the silver bullet, the easy way to put Data Governance in place, please promise me that you will run a mile. It is not worth the effort, because what you'll do is waste a lot of time and effort doing something that's wrong for your organisation.

There is no such thing as a successful standard Data Governance framework. And, I would encourage you to take the time and effort to work out what your organisation needs and implement a framework which reflects that.

So I don’t have a standard framework that you can use, but you can download my free checklist by clicking here which will take you through what you need to do to design and implement a Data Governance Framework that is right for your organisation.

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 Jason Hare

In this interview Jason Hare kindly shares his Data Governance experience. Jason is a former archaeologist and open data practitioner. He has been working in IT since the later 90’s. His interest in data governance stems from his time managing municipal open data programs in North Carolina.

How long have you been working in Data Governance?

In one capacity or another, I have been involved in data governance since the early 2000’s. I did not know back when I started that there was a whole discipline around governance and data management. I knew I had a data integrity and availability problem with a piece of software I was working on and so started thinking about how to solve that problem.

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

My whole IT career was more accidental than intentional. I started bagging and tagging artefacts. The idea that we could store data electronically and use metadata to describe the provenance of artefacts was how I became interested in information technology. Knowing what I did about social science, I was always thinking about the quality of the data coming from these information systems. Since about 2000 I stopped thinking I would go back to social science and made governing data my focus.

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

I follow my curiosity. That has made my professional life fulfilling to me. My curiosity seems to run towards how to make the lives of people better through making data better. What makes me successful, or at least successful to me, if a culture change approach to governance. People and process, the understanding of why data governance is important, that is what is most important to me. 

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

I enjoy reading and discussing data governance issues with Peter Aiken. I think he was the first person I met that had ‘data governance’ somewhere in his job title. I have also read a lot of what Kelle O'Neal, John Ladley, Christopher Bradley and of course you Nicola. I like learning from my customers as much as I like reading what others think about data governance. 

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

I am sure, like many of your readers, there are so many. The biggest type of challenge I face is convincing an organisation that repeating the same process over and over and expecting a different result is not going to work. That is true for just about anything but it seems to be especially pernicious about data governance engagements.

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

I have a fondness for the public sector and how smaller local governments can make better decisions around policies that affect real people. In the US, the local government has a bigger impact on individuals than the Federal Government does. The data with which local government makes decisions is often rife with bias. This may or may not be intentional. I would like to work in local government again.

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

Read the literature but be prepared to find that the literature does not often match reality. My biggest mistake starting out was trying to fit some situation into a framework. Rarely do real-world problems fit neatly into a framework. I also find keeping up with the legal side of what we do keeps me in step with how governance is changing and cross walking with information assurance.

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

I could but I might violate my non-disclosure agreements. Ask me about “biscuitgate” and I can share with you how a little creative data governance and open data worked together to solve a social issue with an online map.

You can find out more about Jason and connect with him on LinkedIn by clicking here.

Why is it So Hard to Write a Data Governance Policy?

Today's question is one that I have been asked many times over the years, and in particular more recently since my blog post on whether you need a Data Governance policy. You may remember in that piece I told you that you absolutely do need a Data Governance policy. Which, naturally begs the question "Why is it so hard to write a data governance policy?"

I think the main reason for this is that there are very few people out there that have ever written a Data Governance policy. And, if it is the first time you have ever written a Data Governance policy, that chances are, you just do not know where to start.

I've seen many examples where people have spent a lot of time and effort researching Data Governance, what they think they ought to have in their framework and dumping everything they found in their Data Governance policy. This ends up giving you a policy that is really long - some I've seen are like small novels! And, to be fair, I may have written some that length in the early days too.

But when you come to share them with any stakeholders, particularly senior ones, they're going to be almost completely put off by the sheer level of detail. Of course, there is always a temptation to try and put too much into policies, but that scares people off, and you won't get your senior stakeholders signing it off. You may even impact the success of your Data Governance initiative at that point.

The other thing that I frequently see is people thinking that they can fast track this part of a Data Governance initiative by copying somebody else's policy. So, they look on Google or they ask a friend who's doing a similar job at another organisation if they can have a copy of their policy.

I would really warn you against this because a policy should be written to reflect how your organisation wants to do Data Governance. Picking up somebody else's and just adapting and amending a few bits means that you actually haven't got a policy that was written for your organisation. Therefore, it's extremely unlikely to be useful or relevant to your organisation. And again, more likely to put more people's backs up and damage your Data Governance initiative.

In fact, sometimes I find myself writing a policy for my clients and they often assume that I will just take a previous policy I've written and tweak it for them. However, this really wouldn't be very helpful for the new client, as it wouldn't be designed to meet their needs!

And, as with all things data governance, I don't think that there is such a thing as a standard approach - there certainly is not one for a data governance policy. Think about it this way… if there is no such thing as a standard data governance framework, why would you think that a policy written for another organisation would work for you?

Unfortunately, a lot of people don’t realise this, and I’m often asked if I would share a template or an example for data governance policy that they can copy

For a policy to be really useful (i.e. help you implement Data Governance successfully), it needs to be written with your organisation in mind. You should consider the following:

• What is the scope of your data governance programme?

• What is it that your organisation is going to do to manage its data better?

• What roles and responsibilities are you going to have to manage your data better? 

• What kind of processes are you going to implement as a result of having data governance?

Now, the answers to these questions will not be the same for all companies and I can honestly say that every organisation I have ever worked with has been unique in its approach to data governance.

I admit sometimes the differences are subtle, but for a policy to be valuable, these subtleties really do need to be addressed. So, what's the answer? As I said, this is a question I've been asked so many times over the years, and I have been asked many times to write Data Governance policies for people, so I decided that this was something I really needed to help people with. So, I'm really pleased to announce that my latest course is ‘How to Write a Good Data Governance Policy’.

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.