What do you include in Data Quality Issue Log?

Whenever I am helping clients implement a Data Governance Framework, a Data Quality Issue Resolution process is top of my list of the processes to implement. After all, if you are implementing Data Governance because you want to improve the quality of your data, it makes sense to have a central process to enable people to flag known issues, and to have a consistent approach for investigating and resolving them.

At the heart of such a process is the log you keep of the issues.  The log is what the Data Governance Team will be using while they help investigate and resolve data quality issues, as well as for monitoring and reporting on progress.  So, it is no surprise that I am often asked what should be included in this log.

For each client, I design a Data Quality Issue Resolution process that is as simple as possible (why create an overly complex process which only adds bureaucracy?) that meets their needs. Then, I create a Data Quality Issue Log to support that process.  Each log I design is, therefore, unique to that client.  That said, there are some column headings that I typically include on all logs.

Let’s have a look at each of these and consider why you might want to include them in your Data Quality Issue Log:

ID

Typically, I just use sequential numbers for an identifier (001, 002, 003 etc).  This has the advantage of being both simple and giving you an instant answer to how many issues have been identified since we introduced the process (a question that your senior stakeholders will ask you sooner or later).

If you are creating your log on an excel spreadsheet, then it is up to you to decide how you record ID numbers or letters.  If, however, you are recording your issues on an existing system (e.g. an Operational Risk System or Helpdesk System), you will need to follow their existing protocols.

Date Raised

Now this is important for tracking how long an issue has been open and monitoring average resolution times.  Just one small reminder: be sure to decide on and stick to a standard date format – it doesn’t look good for dates to have inconsistent formats in your Data Quality Issue log!

Raised By (Name and Department)

This is a good way to start to identify your key data consumers (it is usually the people using the data who notify you when there are issues with it) for each data set.  This is something you should also log in your Data Glossary for future reference (if you have one). More importantly, you need to know who to report progress to and agree on remedial action plans with.

Short Name of Issue

This is not essential and some of my clients prefer not to have it, but I do like to include this one. It makes referring to the Data Quality Issue easy and understandable.

If you are presenting a report to your Data Governance Committee or chasing Data Owners for a progress update, everyone will know what you mean if you refer to the “Duplicate Customer Issue”. They may not remember what “Data Quality Issue 067” is about, and “System x has an issue whereby duplicate customers are created if a field on a record is changed after the initial creation date of a record” is a bit wordy (this is the detail that can be supplied when it is needed).

Detailed Description

As I mentioned above, I don’t want to use the detailed description as the label for an issue, but the detailed description is needed. This is the full detail of the issue as supplied by the person who raised it and drives the investigation and remedial activities.

Impact

Again, this is supplied by the person who identified the issue. This field is useful in prioritizing your efforts when investigating and resolving issues. It is unlikely that your team will have unlimited resources and be able to action every single issue as soon as you are aware of it. Therefore, you need a way to prioritize which issues you investigate first. Understanding the impact of an issue means that you focus on resolving those issues that have the biggest impact on your organization.

I like to have defined classifications for this field. Something simple like High, Medium and Low is fine, just make sure that you define what these mean in business terms. I was once told about a ‘High’ impact issue and spent a fair amount of time on it before I discovered that in fact just a handful records had the wrong geocode. The percentage of incorrect records made it seem more likely that human error was to blame, rather than there being some major systemic issue that needed to be fixed! This small percentage of incorrect codes was indeed causing a problem for the team who reported them. They had to stop time critical month-end processes to fix them, but the impact category they chose had more to do with their level of frustration at the time they reported it than the true impact of the issue.

Data Owner

With all things (not just data), I find that activities don’t tend to happen unless it is very clear who is responsible for doing them. One of the first things I do after being notified of a data quality issue is to find out who the Data Owner for the affected data is and agree with them that they are responsible for investigating and fixing the issue (with support from the Data Governance Team of course).

Status

Status is another good field to use when monitoring and reporting on data quality issues. You may want to consider using more than just the obvious “open” and “closed’ statuses.

From time to time, you will come across issues that you either cannot fix, or that would be too costly to fix. In these situations, a business decision has to be made to accept the situation. You do not want to lose sight of these, but neither do you want to skew your numbers of ‘open’ issues by leaving them open indefinitely. I like to use ‘accepted’ as a status for these and have a regular review to see if solutions are possible at a later date. For example, the replacement of an old system can provide the answer to some outstanding issues.

Update

This is where you keep notes on progress to date and details of the next steps to be taken (and by whom).

Target Resolution Date

Finally, I like to keep a note of when we expect (and/or wish) the issue to be fixed by. This is a useful field for reporting and monitoring purposes. It also means that you don’t waste effort chasing for updates when issues won’t be fixed until a project delivers next year.

I hope this has given you a useful insight on the items you might want to include in your Data Quality Issue Log. You can download a template with these fields for free by clicking here.

Running and managing a Data Quality Log using excel and email is an easy place to start but it can get time consuming once volumes increase – especially when it comes to chasing those responsible!   That’s why I was delighted to be involved recently with helping Atticus Associates create their latest product in this space, DQLog.   The Atticus team are launching their beta version in Spring this year and they are keen to hear from anyone interested in trying it for their feedback.  If you are interested in testing the beta, please email me and I can put you in touch.

Data Governance Committees, Forums and Working Groups

At some point in your Data Governance initiative you are going to need to think about setting up a Data Governance Committee. So in this blog I wanted to look at what a Data Governance Committee is and what it should do.

Before I start to look at this in detail I want to remind you that you may not call yours a Committee and that is f fine.  As I mentioned in this blog the titles of roles can be emotive and we need to be pragmatic about their use.  The same is true for your Data Governance Committee. You may have a Data Governance Forum, Data Steering Group, or something similar.  Whatever you call it is not important, it’s who sits on it and what it does that is important.

What is Data Governance Committee?

A Data Governance Committee is simply the forum where you get all of your Data Owners together. Now of course, that is an oversimplification; but it’s a good starting place. I’ve worked with some excellent Data Owners over the years, but it would be naïve to think that they could’ve run the Committee on their own. They are going to need some expert advice and guidance to help them make the right decisions about your company’s data.

These “experts” will include the following types of roles: your Enterprise Data Architect, the Head of Internal Compliance, and the Head of Operational Risk. In more than one client, the CIO has been an attendee. Deciding who you will invite as your experts will, of course, depend on the remit of your Data Governance Committee, which we will look at a little bit later. Before I move off the topic of expert advisors, I want to consider culturally how you will sell the concept of attending these meetings to the experts.

After all, by the time you set up your Data Governance Committee, you should have spent a considerable amount of time and effort identifying and engaging your Data Owners. You have succeeded in getting the agreement of senior roles within your organisation to be accountable for the data quality of a number of data sets. Then you tell them that they can’t make decisions upon their data alone…

Depending on the culture of your company, this may not go down too well! In many organisations, this isn’t really an issue. However, with some of my past clients, to avoid any unnecessary tensions, the terms of the committee have made it clear that the Data Owners are the “members” of the committee, and are the only people who can make decisions. The “experts” are “attendees”; there to provide advice and guidance to help the Data Owners make their decisions. (Notice that I don’t say “the right decisions,” that phrase could also open up a myriad of tensions and issues!)

There are three more people who are key to the success of your Data Governance Committee:

Firstly, a strong chair person. I have seen it work successfully with this role undertaken by one of the Data Owners, but it is better if the Chair of the Committee is not a Data Owner themselves so that they can remain neutral if any heated debates ensue. Ideally this person will be the executive sponsor of your Data Governance initiative, that way they have a vested interest in making it work and they are in the right position to escalate issues to the Executive Committee if that is needed at any point.

Secondly, you should be there! I am of course assuming that you are the Data Governance Manager or the person leading the Data Governance implementation (whatever your title happens to be); if so, you need to be an attendee. You are key to reconciling the agenda with the Chair Person, and in the early days you will be presenting many proposals for activities you want to start or include in your Data Governance initiative to get approval from the Data Owners.

Finally, you are going to need some type of secretariat support for the Committee. Arranging the meetings, booking meeting rooms, circulating papers in advance, and taking minutes are all crucial to the smooth and successful running of this Committee. If your organisation is very small, then you may have to do this yourself, but you may have a fellow Data Governance Team member that can help you, or perhaps the Chair Person’s personal assistant could provide some support.

So, hopefully that has given you an idea of who to invite, but you can’t invite them without being able to explain to them what it is they will have to do.

What does a Data Governance Committee do?

As with all things Data Governance, what the committee is responsible for will depend very much on your organisation and the Data Governance Framework you have designed or are designing. It will also depend on when you set it up. At the very least, the committee will be responsible for the oversight of the Data Governance Framework and for monitoring material data quality issues. The committee could also be responsible for ensuring compliance with relevant data related regulation as well as steering other data projects such as master data management, or the implementation of a Data Warehouse.

Depending on when the Committee is set up, its role in the development and implementation of your Data Governance Framework may also be dictated. Over the years, I have seen such forums set up at various times: before the Data Governance initiative has really begun; as it starts; and once the Data Governance initiative has started. My preference would be for the latter, but let’s look at each in turn:

Data Governance Committee First

This usually happens when a senior stakeholder recognises the need “to do something” about their data, and invites like-minded peers to a forum to discuss and agree what it is they can do. Sometimes this can be a useful step in identifying the need for and starting a Data Governance Initiative. At other times they can be a talking shop where attendees share their issues and everyone agrees that it is a problem, but not what will be done to resolve them.

If this is what you are facing, view this as your self-identified stakeholders, and work with them to get the initiative started. They will then be instrumental in all stages of designing and implementing your Framework. At some stage, you will have to review the attendees as it will become obvious that not all of them are Data Owners and that some new Data Owners will need to be invited.

Data Governance Committee as Initiative Starts

In my opinion, this is still a little early; things move slowly at the start and it can be challenging to keep all the attendees engaged if you don’t have much progress to report. You also have the issue of how to identify the attendees if you haven’t designed the Framework and identified the Data Owners.

In this situation, the sponsor has identified key stakeholders and invited them. This can lead to engagement issues – if they really aren’t interested – plus, you will have the same need, detailed above, to review and change the membership a few months in.

Data Governance Committee on Data Governance Has Started

My preference is to set up the committee after you have designed an initial high-level Data Governance Framework once you have identified and agreed upon who the Data Owners are, or as a step to agree them. At this stage, you will be starting with a clear purpose and the correct attendees so that they can focus on overseeing the detailed design and implementation of the Data Governance Framework.

Whichever of the above situations you are facing, you must be clear from the start that the terms of reference for this Committee will evolve and transition from oversight of development and implementation, to the ongoing oversight and governance of your Data Governance Framework.

I do hope that this has clarified what a Data Governance Committee is and what it should do. But please don’t rest on your laurels once your committee has been set up. It requires a lot of effort to make and keep such forums successful. You can read my top tips for how to go about that here.

Setting up a Data Governance Committee is just one of many things you need to do when implementing Data Governance. You can find out more of the tasks (and the order in which they need to be done) by downloading my free Data Governance Checklist.

Free Checklist To Support Successful Data Governance

Increasing numbers of organisations are becoming data driven and digital transformation is high on the agenda for many of my clients. These really are exciting times from a data point of view. The landscape in which we work is changing rapidly with machine learning and AI increasingly influencing our daily lives. If you are reading this blog, then hopefully I have no need to convince you of the importance of data governance to support digital transformation, AI and machine learning.  Right now is not only a good time, but a necessary time to be embracing Data Governance.

 So perhaps you are among the many people I speak to who say that 2019 is the year you are going to get Data Governance in place in your organisation? Regardless of the endeavour, it is common for people to renew their efforts at the beginning of the year. Most people have had a break and are feeling refreshed and energised, ready to face the challenges that implementing data governance inevitably brings.

But I want to urge caution, whilst energy and enthusiasm are vital ingredients, on their own they will not ensure your data governance initiative is successful. I used to think that was the case, but be assured you also need a structured approach and a clear plan. 

When I was planning for 2019, I decided to make it my mission to help as many people as possible to be successful in data governance. So I decided that this year there was something more I could do to help you be more successful.

Over the 16 years I have been doing data governance, I have created and refined a methodology that ensures all the necessary building blocks are addressed and in the right order. This ensures that a data governance framework can be designed and implemented successfully, regardless of the industry and culture of an organisation. It is this methodology that is the basis of my training course.

 Being a "Data Governance geek" I like templates and lists, so as part of this methodology I have a checklist. This checklist details everything I need to do or consider, plus the order in which I need to do it and it is this checklist that I use when helping my clients design and implement data governance frameworks.  To be honest, I have followed it so many times over the years that I don't need to look at it very often these days! This got me thinking, it was a shame that something I have found so valuable is just sitting neglected.

 So I have decided to share this checklist with a wider audience.  A high level version is now freely available on my website and the full version of my checklist will be given to everyone who does either my online or face-to-face training (and indeed I will be sending a copy to everyone who has ever completed my training to date). 

I do hope that you find this resource as helpful as I have over the years.

Can Software Help My Data Governance Initiative?

This is a question that never came up very often the first few years I was a Data Governance Consultant but these I am being asked it much more frequently.  I think that the increase is due to the fact that there are good data governance tools available now and that data governance is getting much more focus than previously.

In the last couple of years, I have seen a move away from companies implementing data governance because they are in an industry where their regulator requires it, to organizations starting data governance purely because of the benefits they can achieve. That also includes companies looking to extend what they had put in place to meet regulatory requirements across more of their organization.

Before I answer the question, I think it is important to emphasize that despite having the word “data” in the title, data governance is more about people than data.  Most of the work you will do when designing and implementing a Data Governance Framework is around organisational and cultural change.  It is about the roles and responsibilities around data and the processes that these roles will follow.

It is for this reason that when Data Governance tools first started emerging I have to admit that I was skeptical about whether they could really help.  In fact, if you search the internet hard, you may even be able to find some comments I’ve made in the past along those lines!  After all, in the early stages of a Data Governance initiative I seldom find myself sitting at a desk using any type of software (data governance or otherwise); I am meeting and engaging stakeholders.

However, over recent years, having seen these tools evolve, I have revised my opinion and will happily say that yes, they can help you do data governance, but when I say yes it does come with a caveat.  In order to add value to your Data Governance initiative, they must be used in the correct manner and at the correct time.

Data Governance Tools Can Only Facilitate

The tools out there can be fantastic enablers and facilitators but they cannot do your job for you.  On more than one occasion I have heard of companies who have purchased such a tool and thought that that was it – job done.  However, it soon became clear that the tool “was not working.” Upon investigation, it became obvious that the tool was not the problem.  The business believed that it would do everything for them, which clearly it cannot.

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

You will still need to get stakeholders engaged in the process because without their buy-in, the whole initiative is likely to fail. The tool won’t work unless the whole business is signed up to data governance in the first place. Tools do not relieve them of any responsibility. Instead, tools should be positioned as enablers that make it much easier for people to execute their data governance responsibilities.

Remember: it’s not the tool that causes a data governance initiative to hit the rocks. The initiative fails when too much attention is focused on the tool, and too little attention is focused on getting stakeholder buy-in and change management.

Do Not Deploy Too Soon

Another reason I’ve seen problems arise around the use of tools is when they are deployed too early in the Data Governance initiative and the organisation is not mature enough to know how it wants to use the tool.  Without fully understanding what Data Governance is, the scope of their initiative, or the functionality of the tool, it is naive to rush into using one.  A company can expend significant time and resources on setting up a Data Governance tool only to find that they have set it up in manner that does not facilitate their Data Governance initiative or meet their requirements!

So What do Data Governance Tools do?

So let’s be clear on what such tools can do. Primarily, they provide data glossary functionality; they can automatically create data lineage diagrams and have workflows that send proposed changes in data definitions or data standards to the data owner for approval.  They can also be used for logging and monitoring the resolution of data quality issues. So they really can facilitate and help embed Data Governance into your organization.

How to Use Data Governance Tools Successfully

I hate to recommend it, but I firmly believe that as you start to create and build a data glossary, your early version should be created on an excel spreadsheet.  The reason I say that is that they are easy to adapt and change as your organization matures and your business users work out what they want and need from a tool.  When you are reasonably clear what is wanted and how it should be structured, that is the time to introduce a tool into the mix.

Remember, you are trying to change how people behave in your organisation. This is not something that can be achieved by only thinking in terms of technology solutions. It is going to involve a lot of soft skills: communications, influencing, and even hand-holding as they start to embrace Data Governance.  And let’s face it: computers and software are not known for their people skills.

Tools can be very powerful facilitators, especially in larger organisations where perhaps you do not know or even know how to find out who a data owner is or what data is available; but they only facilitate and support the hard work that you need to do in person.  There really is no way to avoid it… You are going to have to go and talk to people!

Thinking that software tools are the answer is just one of the top data governance mistakes that I’ve seen organizations make.  If you want to find out what the others are (and how to avoid them), you can download a free report here.

What is the Difference Between Policies and Standards?

This question is one that I have been asked a lot over the years, and has led to some interesting debates! But before I dive into my thoughts on the matter, I think that there is another question that needs to be asked (and answered first):

Why the confusion?

Why do these two terms cause so much confusion? After all, they are commonly used business terms. It’s not as though they are specialist data management jargon. The Oxford English Dictionary defines these two terms as follows:

Policy: A course or principle of action adopted or proposed by an organization or individual (Wikipedia elaborates this further saying that: A policy is a deliberate system of principles to guide decisions and achieve rational outcomes. A policy is a statement of intent, and is implemented as a procedure or protocol.)

Standard: A required or agreed level of quality or attainment

The way I use these terms in the context of data governance pretty much follows the above standard definitions. However, confusion arises as many people use the term policies when I would use standards, so whereas I would say that in data governance terms an organisation will have one Data Policy and many Data Quality Standards, I know that a lot of practitioners say that an organisation will have many data quality policies. So to clarify, the definitions I use are:

Data Policy: a formal document that describes an organization’s approach to managing data quality i.e. what will be done

Data Quality Standard: The standard that a certain field or data set needs to achieve e.g. that a field must always be completed or the value sit within a defined range.

Conclusion

So which should you use? Well I believe that it is fine to use them how you wish (and whichever fits best with your organisation’s culture) as long as you define what you mean and communicate that definition with your audience. As with most things, confusion only arises when no explanation of what is meant is provided and those on the receiving end of your messages are left to make their own (varied) interpretations.

Not defining terms is a common mistake or omission that many organisations make, but although it causes frustration and confusion, it is not the biggest problem you will face and it is easily rectified.

Do We Have to Call Them Data Owners?

I spend a lot of time helping people become successful Data Governance practitioners, so it is not surprising that I get asked lots of questions about Data Governance. A very common one is about naming the Data Governance roles..  Many people worry about whether it is necessary to use standard or best practice role titles when implementing Data Governance.  By this I mean titles such as Data Owner or Data Steward.

Before I dive into my thoughts on this issue, I’d like to remind you of a quote from Juliet in William Shakespeare’s Romeo and Juliet:

“What’s in a name? That which we call a rose. By any other name would smell as sweet.”

You don’t have to have studied Romeo and Juliet or even seen the play to know the saying “a rose by any other name…” As with many Shakespearean quotes, this one gets used in our everyday lives, and this particular quote resonates with me whenever I get asked the question about role titles. I often find myself involved in such discussions when I start working with new clients.

If you are implementing a Data Governance Framework and have done any “Google” research, you probably have come across a numbers of role titles that you may want to include: Data Owners, Data Stewards, Data Custodians, Data Champions, Information Owners, Data Librarians, Data Producers, Data Consumers, and a host of others, I’m sure.

Personally, I favour a fairly straight-forward set up with Data Owners, Data Stewards, Data Consumers, and Data Producers as the primary roles. However, throughout the numerous Data Governance initiatives that I’ve been involved in, I’ve learned not to be precious about it.

Through my own personal experience and from watching others, I know how emotive job or role titles can be.  You don’t have to be egotistical to desire the “right” label; it’s human nature to desire recognition for the work we do.

Of course, when we are implementing Data Governance, we are not recruiting and appointing new staff into new roles.  We are identifying the correct existing people to take on some data governance responsibilities.  Remember, however, that even additional responsibilities which do not ostensibly change a job title need to have a descriptive name that fits with company culture. You might be the Finance Director, but you can also say that you are the Data Owner for Finance data.  It is then easy for other people to understand your additional responsibilities.

Role titles are neat things. They enable you to deliver a message about what you are responsible for, succinctly and quickly, so it is no wonder that people like and use them. But what happens if you have the wrong titles? Well, that depends on the culture of the organization, and the individuals concerned. At best, it could lead to confusion and make implementing and embedding your data governance framework more challenging.  At worst, I’ve seen it delay the implementation of the data governance initiative as people waste time in endless debates over what the roles should be called.

What I’ve learned through experience is quite simple: the name is important to a degree (don’t insist on one set of titles if they really aren’t going to work in your organization), but it is critical to define what that role is responsible for and what you need the role holders to be doing in order to make your data governance initiative a success.

My advice is to start with the list of things you need done and work out how best to group those responsibilities together in a way that fits your organizational structure. Try some of the “usual suspects” out and test their reactions. If they like Data Steward – go with it; if they don’t, try something else. It doesn’t matter if they aren’t called a Data Steward as long as they act like one.

As with all things Data Governance, pragmatism is vital: so in my view, “that which we call a Data Steward by any other name could work just as well…”

Data Governance Interview - Alex Leigh

Alex has worked with over fifty UK universities, most of the sector agencies including University and Colleges Admissions Service (UCAS), Higher Education Statistics Agency (HESA) and the Quality Assurance Agency for Higher Education (QAA) and a host of practitioners in the Higher Education (HE) sector. Alex designed and developed the HEDIIP data capability framework, led the team to create the HESA in-year collection model and designed both a sector level and a HESA instance of a best practice data governance approach; and is currently working with universities to develop and implement their Data Governance frameworks,

How long have you been working in Data Governance?

I was working in DG before anyone really called it DG! Around fifteen years via data architecture and running data management teams.

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

My route in was via developing and implementing Enterprise Architecture frameworks while I was working in Deloitte UK consulting practice. There was a lot of maturity around the business, infrastructure and application architecture but data felt very fragmented between the technology of building warehouses and databases and the link to the business objectives.

So I focused my efforts on creating frameworks and approaches to pragmatically align how data was managed to how it was used. That was fifteen years ago and I’m still trying to change the perception around data.

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

Passion for the right outcomes, resilience when I can’t achieve them and an ethos for openness, transparency and showing everyone why doing things differently isn’t just about the organisation, it’s about helping them.

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

I’m a big fan of John Ladely and use his books as my primary source of reference. Bob Seiner has some great ideas about implementing DG and I’m an avid reader of TDAN. I also carry around the latest DMBOK which is a big improvement on the original in terms of applicability.

There’s so much good stuff online now. I’d encourage any new practitioners to read and research lots of different ideas, as no one size fits all.

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

Believing everyone was as passionate, sold and committed as I was to doing things differently. So losing support once the initial enthusiasm had lapsed. Both from those working with data every day and those who had the budget, resource and will to help change the way data was governed.  Now I am very careful to make sure everyone is starting in the same place, and make far less assumptions around priorities.

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

I love working in Higher Education (HE). It’s collaborative, desperately in need of the professionalization of its data assets, and – finally – ready to make some of these changes. I’ve worked in 7 industry sectors, but I’ll never move out of HE. If I can help make a difference to this sector, it feels a really important thing to do. Having kids about to enter the HE environment makes this personal as well as important.

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

Don’t try to fix everything at once. At the heart of good DG is changing behaviours of many people. This is not a simple thing. Talking about frameworks or technology and the like early in a DG initiative is not helpful. Find some people who share your passion for data, look for quick wins where doing things differently makes a measurable change and communicate way more than you think you need to.

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

I once asked an attendee at a workshop why she looked so glum. Her reply was "This is how I feel about data. I work in planning and I know at the top of the ‘data mountain’ it is sunny and lovely and all the data is perfect.  The data gets to me in this dark valley at the bottom of the mountain via a stream that all the sheep has wee’d in. That’s why I look so sad"

It was an amusing anecdote but made me realise that you really have to show people how things can be better, before actually asking them to do something about it. 

Data Governance Interview - Dr. Irina Steenbeek

Irina is a dedicated Senior Data Management, Financial and IT Professional with 15+ years of extensive experience in data management, software implementation, financial and business control, and project management. During the past year, she has also immersed herself in data science. She sees that big data and data science have a huge potential for business development. These areas are crucial for the development of data management, including data governance.

How long have you been working in Data Governance?

It has been over 7 years now, since I first started working in data management. During this time, I have also engaged a lot in Data Governance, mostly through hands-on experience in SMEs and large international organizations.

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

For me, this career path started quite organically as a result of several challenges in related areas. The first challenge was to create an automated solution for management accounting reporting within an organization. During the development of an organization-wide reporting system, I came across various issues, such as massive numbers of non-reconcilable Excel reports and the presence of three separate reporting platforms. This lead to the implementation of a data warehouse in this company. This implementation also highlighted data quality issues. This was not all, but it started a long journey towards the management of the data.

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

I think that well-developed communication skills always come in handy. Establishing a common business language between your colleagues and partners, as well as clear communication with the stakeholders helps a lot if you want to set up a good data management structure. A data management function connects various departments of a company, and it is great if you can get practical support of top management, or work in an environment and culture that can enable you to execute your function in the most effective way possible.

A while ago I have written a blog on this topic: ‘Am I a successful Data Manager’ Feel free to consult it for more tips that I have to share.

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

Recently I came across The Practitioner’s Guide to Data Quality Improvement by David Loshin, published by Morgan Kaufmann in 2010. I think this book is quite useful for DQ professionals.

But in general, from my practical experience of working closely with business stakeholders, I have reached one conclusion: people are not interested to know what they need to do. They are eager to know how to do what is expected from them.

At an early stage of my professional development I used DAMA DM-BOK as my main reference point. And of course, I have done a fair share of internet browsing and analyzed the materials I could find, originating from numerous sources. So far, I haven’t been able to find any practical guide that could help starting data management professionals.

This experience brought me to an idea to write my own ‘The Data Management Cookbook’, a brief summary book on Data Management, which is here. The extended ‘Do It Yourself’ Data Management guide will be available in June 2018.

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

There were several, it is difficult to say which one was the biggest or most important. The first one is finding your own way to implementation of data management, and figuring out where to start. The second one is convincing data owners that they have to take on responsibility for their data. And the third one is more technical: the investigation of data quality issues caused by applications.

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

In my career, I have worked in all kinds of companies, large and small. All in all, I think I prefer small and medium businesses to large and famous multinationals.  Why? I know it doesn’t sound very ambitious. Smaller companies feel more like a ‘family’. And I appreciate this a lot, as this feeling creates a great work environment. You know everyone and you can get things done quickly and efficiently. It is easier to involve your colleagues in all the processes, which is crucial for successful data management. Even if the resources are limited, you can still deliver tangible results in a short period of time.

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

You do not set up data management just for fun. You do it because you have certain

needs, a certain goal will drive you to do it. GDPR or data quality are the best examples.  My advice is to first get a clear picture what your goal, your driver is. As soon as you know the direction you should be working in, it will be easier to figure out which components will need most of your attention and in which order you should proceed.

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

When implementing data management, you have to ensure collaboration between various stakeholders. These are people with different interests, needs and tempers, and you are the one who has to combine them all into one effective working system. One of the most memorable moments in my career was a question of one of such stakeholders: ‘Where do you get so much patience to permanently stay calm, have good relationships with all of us and still keep going?’. I guess only a data manager would know. :-)