Do you need Data Governance over a Data Lake?

There continues to be a lot of excitement about data lakes and the possibilities that they offer, particularly about with analytics, data visualizations, AI and machine learning. As such, I’m increasingly being asked whether you really need Data Governance over a data lake.  After all, a data lake is a centralised repository that allows you to store all your structured and unstructured data on a scalable basis.

Unlike a data warehouse, in a data lake you can store your data as-is without having to structure it first.  This has resulted in many organisations “dumping” lots of data into data lakes in an uncontrolled and thoughtless manner.  The result is what many people are calling “Data Swamps” which have not provided the amazing insights they hoped for.

So the simple answer to the question is yes – you do need Data Governance over data lakes to prevent them from becoming data swamps that users don’t access because they don’t know what data is there, they can’t find it, or they just don’t trust it.  If you have Data Governance in place over your data lake, then you and your users can be confident that it contains clean data which can found and used appropriately.

But I don’t expect you to just take my word for it; let’s have a look at some of the reasons why you want to implement Data Governance on data being ingested into your data lake:

Data Owners Are Agreed

Data Owners should be approving whether the data they own is appropriate to be loaded to the Data Lake e.g. is it sensitive data, should it be anonymised before loading?

In addition, users of the data lake need to know who to contact if they have any questions about the data and what it can or can’t be used for.

Data Definitions

Whilst data definitions are desirable in all situations, they are even more necessary for data lakes.  In the absence of definitions, users of data in more structured databases can use the context of that data to glean some idea of what the data may be.  As a data lake is by its nature unstructured, there is no such context.

A lack of data definitions means that users may not be able to find or understand the data, or alternatively use the wrong data for their analysis.  A data lake could provide a ready source of data, but a lack of understanding about it means that it can not be used quickly and easily. This means that opportunities are missed and use of the data lake ends up confined to a small number of expert users.

Data Quality Standards

Data Quality Standards enable you to monitor and report on the quality of the data held in the data lake.  While you do not always need perfect data when analysing high volumes, users do need to be aware of the quality of the data. Without standards (and the ability to monitor against them) it will be impossible for users to know whether the data is good enough for their analysis.

Data Cleansing

Any data cleansing done in an automated manner inside the data lake needs to be agreed with Data Owners and Data Consumers. This is to ensure that all such actions undertaken comply with the definition and standards and that it does not cause the data to be unusable for certain analysis purposes— e.g. defaulting missing date of births to an agreed date could skew an analysis that involved looking at the ages of customers.

Data Quality Issue Resolution

While there may be some cases where automated data cleansing inside the data lake may be appropriate, all identified data quality issues in the data lake should be managed through the existing process to ensure that the most appropriate solution is agreed by the Data Owner and the Data Consumers.

Data Lineage

Having data flows documented is always valuable, but in order to meet certain regulatory requirements, (including EU GDPR) organisations need to prove that they know where data is and how it flows throughout their company.

One of the key data governance deliverables are data lineage diagrams. Critical or sensitive data being ingested into the data lake should be documented on data flow diagrams.  This will add to the understanding of the Data Consumers by highlighting the source of that data.  Such documentation also helps prevent duplicate data being loaded into the data lake in the future.

I hope I have convinced you that if you want a data lake to support your business decisions, then Data Governance is absolutely critical.  Albeit that it may not need to be as granular as the definitions and documentation that you would put in place for a data warehouse, it is needed to ensure that you create and maintain a data lake and not a data swamp!

Ingesting data into data lakes without first understanding that data, is just one of many data governance mistakes that are often made. You can find out the most common mistakes and, more importantly, how to avoid them by downloading my free report here.

Data Governance Interview with Prasanna V-S

I am so pleased that for this Data Governance interview Prasanna V-S, kindly agreed to share his insights and experience. Prasanna is currently a Data Governance Specialist at Fidelity Investments at Raleigh-Durham, NC, in the United States; 

In his role, he is focused on data governance and metadata management with a goal in mind of accelerating associates’ ability to find, trust, and consume data along with a strong focus on data policies/compliance as well. He strongly believes that effective data governance is all about treating data as a business asset and in order to achieve that as a governance specialist, he strives to embed governance across different areas of the organization by playing part educator/strategist and part governance platform management.  

How long have you been working in Data Governance?

I have been in Data governance and metadata management for about 3-3.5 years now.  

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

 Yes, you’re right; But I think that is changing right now at least here in the US. I initially started my career at Accenture as a Technology consultant in more traditional areas like BI/Analytics, but I think somewhere along the line, I was exposed to areas such as a Business Glossary, Data Quality, and Data lineage which in recent years is referred to as Data governance. 

Now, I would say I got the strongest exposure to the breadth of Governance areas such as Glossary, Data catalog, Data lineage, Data guidelines/Privacy policies in my current role. I got into my current role as I was personally really excited about the opportunity to work in interesting areas like Data catalog to break data silos, advanced Data privacy-related work, etc. which in some sense are advanced use cases in governance.

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

 I believe I possess the skills to take a complex/abstract topic and simplify it for an audience (to both leadership/technical teams), which is very crucial in Data governance as a lot of the concepts/use cases are rather abstract to explain in terms of value add, etc. 

Further, I also think I possess the necessary technical skills to roll-up my sleeves and manage governance platforms, partner with technical teams (where metadata usually originates) along with strong communication skills to maintain a strong working relationship with vertical and horizontal layers in the organization. 

You work a lot with the Financial Services Industry – how mature would you say they are in Data Governance?

 I actually work in the Financial Services industry. I do keep up with my counterparts in other organizations and updates through governances conferences, etc. and my personal impression is that Financial Services is probably among the more mature sector in implementing Data Governance. 

This is partly driven by regulatory requirements and also a strong theme among organizations to enable a data-driven culture and the realization that effective data governance/focus on data quality is key to getting there.  

How clearly do you believe that Financial Services view the difference between Data Governance and Information Governance?

 I think there’s a general lack of awareness of Data governance and its necessity/benefits, etc. whereas in a lot of organizations there’s already a strong Information governance establishment, whether its IT Security/Audit, Risk, and compliance, etc. 

In some organizations, probably lesser in Financial services than in other industries, there’s not always a clear delineation between the two, and even within Financial services, I have seen a few cases where Governance related initiatives tend to be owned by Information governance teams, especially in its nascent stages. 

Obviously, it really boils down to winning that executive support/sponsorship by establishing a strong business case for governance initiatives and being able to tie it down to activating use cases tied to revenue (for instance, effective governance serving analytical teams making data discovery easier ) and supporting privacy policies, etc. as well. 

How To Select The Right Data Governance Tool

There are many tools on the market now that can help you with your data governance initiative. In particular, there are numerous products that hold and manage your data glossary, data catalogue and data dictionaries.  These have proved very popular and the number of players in the market has increased over the last few years.

If you are lucky enough to have the budget to purchase such a tool, please make sure that you're well prepared so that you can choose the right vendor for your organisation’s needs. If you select the wrong tool, it won’t help your Data Governance initiative and even worse it could distract from or even derail it!

To help you avoid making such a mistake I want look at some of the common pitfalls in DG tool selection and the kind of questions you need to ask your vendors, so that you are really clear on what you're looking for before you embark on a tool selection process.

Let's look at the most common pitfalls first.  The three main ones that I've seen are:

·      Little or no business user involvement

·      Unclear requirements

·      Overly complex initial implementation

Taking each of these in turn:

Firstly, there's little or no business involvement early in the process. Many people wait until the tool is purchased and even being implemented before they involve business users.  In my experience, this is a huge problem and should be avoided at all costs.

I have seen a few implementations go wrong because the eventual business users were not involved in selection.  Think about it from their point of view.  They have not asked for such a tool, nor does it help them to do an existing task more quickly or easily.  So, when you come to implement your shiny new tool, the business users feel they're having some IT tool foisted upon them. Generally they do not react well and I can recall one instance when the whole implementation had to go back to the drawing board.  Once the business users understood what they needed to use the tool for, their requirements were vastly different from what had been delivered.

The second pitfall is being unclear on what you require of the tool. Often someone has latched on to the fact that a tool could help them and dived straight in and bought one without being really clear what they want the tool to do. Please make sure that you take  time to work out what your objective is from having the tool. Once you've worked that out, progress to defining some clear detailed requirements (just a requirement to have a data glossary is not sufficient).

Finally, another common pitfall is trying to make the initial implementation too complex. Some of the more established tools on the market have been around for a while and have evolved over time to provide a multitude of functionalities, all of which can facilitate and enable your data governance and data quality activities. But please, when you're looking at selecting a vendor initially,  be very clear what you want a tool to do now. Also, consider what you definitely want it to do in the future.  Finally, you can make a “nice to have” list. Just make sure you take a thorough approach to determine clear requirements.

I've seen implementations of tools fail or the wrong tool selected because of vague or overly complex requirements (just because the tool does it, does not mean that your business really needs it).

Now we've looked at what the main pitfalls are. I wanted to share with you a few questions that would be useful to ask the vendors to ensure they're a good fit for you and your data governance initiative. Since I've highlighted the need for objectives and clear requirements, the first question to ask them is, how does their tool meet your requirements.  Notice I say how does it meet… and not does it meet. If you ask “does your tool meet our requirements”, most vendors will say yes.

What you want to know is how.  Is it simply out of the box functionality that is ready to go or will there have to be manual workarounds, or even worse a lot of customisation or configurations in the tool that may make future upgrades very difficult for you.

Secondly, I'd ask what implementation support will be given to you. You have to remember these tools are by their very nature, flexible, and you need to set them up in a way that works for your business. This means that you will need some support from the vendor. So make sure that you are very clear upfront about what kind of support they will be giving you.  Knowing what is and isn't covered will prevent any nasty surprises in the future.

Thirdly, ask what training they provide for both you and the team implementing it. Perhaps they may even support training your business users on how to use their tool.  Definitely work out what training you want and ask what training is available.

Some final thoughts on how to choose the right Data Governance Tool for your organisation:

I’ve said it already but please remember that to successfully choose the right tool for your company, it is absolutely vital that you are very clear on what you need the tool to do before starting a selection process.  Clear requirements should be the start of the process.

Make sure that you understand not only the support arrangements of the tool (as I mentioned in the last section) but also the upgrade path of the tool. I've come across more than one situation where an organisation has customised a tool to such a degree that is not possible to follow the upgrade path.  On one occasion they needed a project to redesign and implement a new data glossary to be able to upgrade and take advantage of the new functionality.

Lastly, I would say that when you're working with vendors, going through workshops or maybe an RFP process you are going to meet a whole variety of personalities. Bear in mind that these are not the people that you will be working with if you choose and select this tool. Whether you like or dislike them, do not be swayed by the personalities.  They will not be around for the implementation, and the ongoing support will be provided by other people. So don't let yourself be influenced just because you like or dislike their sales team!

Just remember that such tools can be great enablers to your data governance initiative, but they need to be put in place once your data governance initiative is already going so that you are very clear on what you want.

If you are currently looking at choosing a data governance tool why not book a call to discuss how I can support you through the process:

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Data Governance Interview with Jorg Schorning

In this interview Jorg Schorning has been kind enough to share his experiences working in Data Governance. Jorg has been working since 1987 in mostly the utilities, healthcare (care not cure) related to data, information, architecture and business processes. Nowadays he specialises in Enterprise Architecture, Data management and COBIT5 Governance related questions. He currently works for a Dutch consultancy firm Novius, where he acts as Consultant, Architecture/Data coach/trainer and COBIT Assessor.

How long have you been working in Data Governance?

Data Governance hands on: as a project leader I had to migrate lots of Data from old systems to new replacements, but always with the major responsibility to archive the old data for regulatory reasons. As an Architect I deliver services to several clients in order to get Common Data Models in place and maintained. So in total more then 20 years. Looking at the aspects of DAMA, in almost every aspect I have had assignments to optimize process and data working together.

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

First of all let me explain in general how I see governance comparing to management. Governance is steering, giving direction to, monitor this direction and evaluate to adjust the direction with a rather longtime horizon in mind and not more. Management however is more the HOW, create a PDCA cycle to realize the direction starting today and moving to the required direction.

So for me it’s not a single subject to make a career. Data Governance is the glue to make sure that useful data is created, monitored and maintained and above all adding value to the company! I like to help my clients (business executives and CIO) to get grip on the steering aspect and let them give direction to the organizational units to realize what is needed.

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

• I always use “common sense” and try to let stakeholders use this as well.

• I make an accountability framework (of only data in this case) of the stakeholders and look for clues if this is working properly, identify potential issues, focus on behavior of involved people.

• I have developed antennas’ to “feel” where there is a lack of good data, probably due to some missing aspect of data management or even missing processes.

• I look at the current use of data and the desired future use.

• Finding missing architectures (mostly information architectures).

• Identifying (data)links between the processes and the IT.

• Discussing with business what their needs and problems are on the data area

• Using a heatmap based on the DAMA Wheel to address aspects and discuss them.

• I always look at the whole lifecycle of data, from birth (creation) up to death (purge), use COBIT 2019 Managed Data and its activities.

You work a lot with the Utilities / Healthcare Industry – how mature would you say they are in Data Governance?

In some areas of the DAMA Wheel they are very mature or capable as I rather like to address it. This is from the perspective of todays’ business and handling the operational dataflow. Become data driven e.g. demands more capability on data management.

And on top of that the aspect Data Governance and the implementation of it in a practical and handy sense is and stays a hell of a job. Terms like Accountability, delegated accountability and Responsibility makes often eyebrows frown and the reaction is either a clear but wrong answer or a big question mark about accountability.

How clear do you believe the Utilities / Healthcare Industry as a whole is on the difference between Data Governance and Information Governance?

The basic difference in this question is that information is data that is put into context and thus seen as valuable business asset. Recently it is noticeable that business strategy points to data (or even better information) as an asset incorporated with the statement: “We want to be data driven!”. However only few realize how this point could be reached. That a solid basis, a data foundation is needed before getting the advanced analytics in place.

To help my clients with this struggle and to make sure that efforts to improve data management as basic capability I focus first on the direct value delivering data aspects by using a Data Heatmap based on DAMA. This heatmap per business area gives insight in people, process and technology related capabilities. So the result of the focus is make optimal use of scarce resources.

Data Governance 2019 Round-Up

Happy new year!  I hope you are rested and ready to continue implementing Data Governance in your organisations.  I find that the new year brings about renewed energy. At this time of year I am always enthusiastically consuming and reviewing content that will help me do a better job in the coming months.  I know I am not unusual in this and it is amazing the number of Data Governance initiatives that are started or re-launched at the beginning of a new year.

In case you are like me, I thought it would be useful to share a round-up of my most popular blogs from 2019. There may be one that you missed, or perhaps one of these may be particularly relevant for you to revisit:

1. What is the Difference Between Policies and Standards?

2. Data Owners and Data Stewards - What is the difference?

3. Why Data Governance Can Be Overwhelming

4. What's the difference between Data Owners and Data Custodians?

5. Why are there so many different Data Governance definitions?

6. Free Checklist To Support Successful Data Governance

7. What do you include in Data Quality Issue Log?

8. What is the impact of a poor data culture?

9. Why is a Data Governance Business Case Hard to Get Approved?

10. Do You Need To Prioritise Your Data?

I hope at least one of these is useful to you at this point in time. 

If you have a topic that you would like me to cover in future blogs please let me know.

If you need a deeper dive into a structured approached 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/  

There is currently a 20% early bird discount available on my next public course in London in March available, but only for a couple of weeks!

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

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Why Data Governance Can Be Overwhelming

Lots of people who come on my training courses say that they are feeling overwhelmed by the sheer magnitude of what they've got themselves into, and are confused by where to start their Data Governance initiative or (if they have already started) what to do next.

To be honest, that’s exactly how I felt when I was first starting out in data governance. I used to compare it to juggling.  Many years ago, before I discovered data, the Bank I worked for sent me on a leadership course. On that course, they taught us to juggle.  Some people found it just clicked and they were amazing at it, but I found it incredibly difficult.

However, at the end of a five day residential course, I did manage to successfully juggle three balls (for a short period of time). Other people on the course managed more than three balls, but we each discovered our limit of how many balls we could keep in the air at any one time.

When I first started out in Data Governance, I  felt the same. There is so much that you have to think about when you're doing data governance, that it can feel just like you are juggling.  There are too many balls for us to keep in the air at any one time.  I came to the conclusion that you can't do everything at the same time. More importantly, I worked out that you shouldn't be doing them all at once anyway. That might be good news, but where lots of people struggle is knowing which activities you do need to do and in what order.  

When I was working on my methodology I noticed that you need to do certain things in the same order for your initiative to be successful. Having said that, there's also a number of other things that will vary depending on your organisation and exactly what you're trying to do.  

The juggling analogy has stayed with me because when I had a video made to promote my online training course last year, the juggling balls came to mind. You can see that video here if you're interested.  

My methodology takes you through everything you need to consider and do in the right order. But that doesn't mean that Data Governance isn't without its challenges. There are many challenges and the biggest one is the culture change you need to instigate. At the moment, most people in your organisation probably aren't thinking about data being part of their job at all. To be successful at data governance, you need everybody in your organisation to start thinking about the data they're creating or using, worrying about the quality of it and whether they should be using it for the purposes they are.

That's a big challenge on its own and there are lots of activities, communications and training that you're going to need to do to affect the culture change you require.

A second challenge I frequently see is that data is not a top priority. Perhaps you are lucky enough to work for an organisation that is focussing on “becoming a data-driven organisation” or is embarking on a digital transformation.  This sounds great as clearly your organisation is finally interested in data, right?  Sadly most people focus on the exciting outcomes and don’t understand that their data needs to be well understood and of good enough quality to facilitate these lofty ambitions.  If this is the situation you are facing, it will take a lot of effort to convince your stakeholders that they need to implement Data Governance first so that such initiatives can be successful.

You've got to make the case for data governance before you can even start designing and implementing a framework (as I've mentioned many times in the past, there is no such thing as a standard data governance framework!) 

When you do get approval to start, there is a lot of work to figure out a framework that will suit your organisation’s structure and culture and once you’ve done that you're going to need to do an awful lot of stakeholder management.   

You need to engage your stakeholders, identify and train Data Owners and Data Stewards. You are going to need a detailed communications and training plan.  I often say that you cannot do too much communication about data governance because you are trying to affect the culture change that I mentioned earlier. These communications need to be good quality, well written targeted communications and briefings. This in itself is a mammoth task.  

So having considered all of this it's no big surprise that a lot of people get overwhelmed with data governance and just don't even know where to start.

That's one of the reasons I started offering data governance training five years ago.

I went through the pain of my first few data governance initiatives before I worked out my methodology. I realised that I could help people avoid some of the pitfalls and the pain that I had gone through by sharing my methodology and that's exactly what my training course does. I take you through stage by stage what you need to do and in what order. I also share skills, tips and techniques to make you more successful. 

What is more all attendees of my training course (both in-person and online) get a copy of the actual checklist I use when implementing data governance for my clients.  So they really don't need to feel like they're juggling.

My next public course is in London next March and if you book before the end of January there is an early bird discount available.

If you want to make 2020 a great year for your Data Governance initiative, why not come along (and if you’ve got unused training budget, why not book before the year-end to make sure your budget doesn’t get forfeited).

If you have any questions about the course and whether it is right for you please feel free to schedule a call with me using the button below:

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Rupal Sumaria - Data Governance Interview

I am really pleased that one of my clients has kindly agreed to share what it is like being new into a Data Governance role. Rupal recently changed roles from Business Intelligence Support Manager to Data Governance and Analysis Manager at Penguin Random House UK. In her role as BI Support Manager, she saw first-hand how Data Governance impacts Data Quality and focused her efforts on fixing issues, improving the process and wherever possible educating users on data rules. In her new position, Rupal will now focus on embedding good data culture and sharing existing best practices in Data Management and Quality across Penguin Random House UK.

 How long have you been working in Data Governance?

 I’m very new to Data Governance, taking on the role officially only a month ago!

 How did you get into this area of work?

My boss Pete Williams, Director of Data and Online at Penguin Random House convinced me! He pitched Data Governance to me (warts and all) and I was sold. I’m still figuring out my role, and being able to set my own direction and path is very exciting.

What is driving your org to invest in this?

The publishing and media industries are undergoing huge changes as they respond to changing consumer behaviours, growth in our digital presence and new data-savvy competitors like Netflix and Amazon Prime as we compete for consumers’ leisure time.

In order to grow our business and ensure we are as data-savvy as our competitors, it is vital we maintain clean data and embed a framework that supports our future.

 Why is data governance important to you?

Data is created, stored and used in every aspect of the publishing business, but data concepts can feel really abstract in a highly creative industry. As a result, we need to empower and engage users to have honest and open discussions about data.

Part of data governance is to change the mindset that data quality, management or storage is solely a technology problem, as data issues affect decision making across the board. It’s exciting to lead conversations about Data Governance and hopefully make an impact on the business.

 What tips have you been given so far?

The top tips I have been given are:

1.    Create a strong business case that aligns to your business strategic goals so that Data Governance resonates with your senior leaders and they support the initiative.

2.    Don’t shy away from challenging areas that think they have perfect data.

3.    Don’t focus on what you call Data Governance; it’s the practice, people and process that are important.

4.    Don’t worry about who takes on what role, i.e. Data Owners, Stewards, etc. It is more important to have a process that allows for Data Quality Issue Resolution.

and were they useful?

 The advice is all very sound and simple but harder than it seems!

 1.    The business case proved to be the easiest of the tasks as we were very prepared and our senior leaders responded positively to the initiative.

2.    It can feel quite intimidating working with senior leaders but luckily Penguin Random House leaders are very friendly, understanding and patient.

3.    The Governance term felt very heavy-handed and as our business is very focused on the power of words, we changed the name to Data Management and Quality.

4.    This has been the most challenging, we very quickly got caught up on trying to assign roles without really taking stock of our Data Domains. We are now working to re-focus on the process, making sure to give everyone the support and resources they need for their roles.

 What tips would you give to someone at the same stage?

 Data Governance can be daunting. Talk to people that already work in Data Governance to seek advice and make sure you have a sponsor from your leadership team.

Also, don’t underestimate how much time you might spend organising people into workshops and arranging meetings to get Data Governance off the ground.

How Long Will My Data Governance Initiative Take?

In this blog, I want to answer a question that I am asked several times every week. To be honest, it’s not an unreasonable question, but it’s not an easy one to answer!

Before I go into any detail trying to answer the question, I want to make one thing very clear: there is no end date on Data Governance.

Data Governance should be something that you are implementing and embedding within your organisation, so that it becomes part of business as usual. For this reason, as anyone who has worked with me or attended my training courses will know, I make a point of impressing upon everyone that Data Governance is NOT a project. If you truly embed Data Governance into your organisation it should never end.

However, having said that, it is entirely possible that you may want to do a project (or project-like initiative) in order to design and implement a Data Governance Framework in the first place. So perhaps the question should be “how long will it take to design and implement a data governance framework and start delivering some benefits?”

But to be honest, that questions isn’t any easier to answer and you could say that both are “how long is a piece of string” questions. Last year, I was lucky enough to be on a panel debate at Data2020 in Stockholm with David Dadoun from Aldo and Andrew Joss from Informatica. Whenever I participate in a panel debate, I always start with a sense of trepidation as to whether my fellow panelists will have the same views as me or not. In this case I did not have to worry because both David and Andrew were very experienced in Data Governance and had seen many of the same challenges that I had over the years. This meant that we all agreed that there is no such thing as a standard Data Governance Framework or a standard approach to implement it. It also meant that— much to the frustration of the Chairman— we took it in turns to answer many of the questions with “it depends.” The panel debate was filmed and you can watch it here if you’re interested.

The reason I tell you this is that whenever I am asked this question, I am always tempted to respond with “it depends.” However, this would not be useful for the person asking the question, so instead, I have to follow up with some supplementary questions. These will include things like:

• Do you have an agreement to commence a Data governance initiative?

• How many resources have you got to work on the initiative?

• What is the scope of your initiative?

• How big is your organisation?

• How open to change is your organisation?

And depending on the answers to the above, I may well ask “is your organisation ready for Data Governance?” Please note this final question is not the same as “does your organisation need data governance?”

Back in 2014, the Data Governance guru Gwen Thomas (founder of the Data Governance Institute) wrote a fantastic article called “When You’re Not Ready for Data Governance.” I frequently direct people to have a look at this post to help get their head around whether now really is the right time for them to commence Data Governance, because sometimes you just have to accept that now is not the right time.

So having asked the first round of supplementary questions (detailed above), if I am convinced that an organisation is ready and able to commence designing and implementing Data Governance, then I need to answer further questions. These are around what they are aiming for and where they are starting from. To help answer these questions, a lot of companies turn to a data governance maturity assessment of some kind. These are very valuable tools in helping an organisation decide how mature they need to be, and in identifying where they currently are.

Please be aware that sometimes organisations can get tied up in “analysis paralysis” and spend inordinate amounts of time and effort on completing a maturity assessment. This is not useful, and care should be taken to only go to the level of detail needed to understand what capabilities your company is hoping to attain, plus identifying its current state.

There are multiple different maturity assessments available. As with all things Data Governance  I prefer a simple approach and you can download a very quick and easy Data Governance Health check questionnaire for free here. If a more detailed assessment suits the culture of your organisation better, I recommend you look at the freely available maturity assessment published by Stanford University. Sadly they recently removed their assessment from their website, but Alex Leigh has created an excel spreadsheet version that you can download from his website.

It is only after you have gone through the analysis outlined above that you will be in a position to estimate how long implementing Data Governance is going to take in your organisation. Now clearly the timescales are going to vary, but in my experience, it is going to take you the best part of a year (and probably longer) to design and implement a Data Governance Framework over at least some part of your data or organisation. This doesn’t mean that you won’t be able to deliver some quick wins during this period, but it will take a reasonable amount of time and effort before your Data Governance Framework starts to deliver value on a regular basis.

I don’t say this to put you off starting in the first place, but I have seen so many people underestimate the amount of effort and time that a Data Governance initiative takes, and it is vital that you manage your stakeholder’s expectations from the outset.

So whilst I can’t give you an easy answer that works for everyone, I hope I’ve given you some insight into how to work out the answer for yourself.