Data Governance Interview with Neil Storkey

Earlier this year, in the days before social distancing, I was lucky enough to catch up Neil over breakfast and he kindly agreed to be interviewed for my blog. Neil is an independent data evangelist who has worked in large multinational companies from the early years of the data adoption. He passionately believes that everyone is a data citizen or as he says a ‘Citizen Steward’.

He views Data Governance like safety, it stems from individual behaviour, and how we shape that to form the day to day activities embraced as a culture.

How long have you been working in Data Governance?

In 1991 I began my data career before Data Governance was even talked about.

Somewhere around 2008 Data Governance started to become more of a mainstream activity with the software vendors adding the word Governance to their sales pitches.

I have always represented the business side of data, advocating that business stakeholders must be leading and supporting data initiatives. If I take the simplest aim of Data Governance to apply a consistent lens or approach to the use of data, then the business must take the lead.

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

I didn’t deliberately set out on the data path as a career, I kind of fell into it. I was working in Finance and realized I was spending all my time as a spreadsheet jockey, which made me question my value. I was fortunate to be asked onto a major finance transformation programme as the reporting lead where data through the migration was critical to my success. Many years of data migrations through a progressive roll-out, and a very good mentor, convinced me there was a fledgeling career in data.

It wasn’t until 2005 that my Data Management role was formalised as the global MDM manager on an SAP finance transformation programme. Across the world, Master Data Management was an emerging discipline and I was lucky to be able to network with like-minded early adopters. 

It was an exciting time to be part of something new.

Back then 90% of the focus was on the technology and IT was wrestling with adoption across their businesses. It was a hard sell to land data as a discipline, and that remains true today.

But at the heart of any data initiative is the need to articulate what it is you are trying to manage and how you measure whether it is working or not.  Data Governance wasn’t seen as an enabler to the business processes, more a compliance and control regime which business areas could choose to adopt. To overcome these hurdles Data Governance needed a business lens applied with a focus on behavioural change.

So, in 2006 I started to develop the processes that would help the business to adopt and embrace Data Management. We now refer to this as a culture change, but it is a ‘hearts and minds’ challenge.

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

Passion, integrity, honesty, resilience, patience, adaptability, storytelling, simplicity, and being able to talk to various stakeholders in the language that they feel most comfortable. I call it ‘talk business’.

Once you come to the realization that this is about changing people’s perceptions about data, how they contribute to its management and how they would benefit from making those changes, your approach becomes far more tactile. I cannot understate the importance of developing soft skills. And like any relationship, you must adapt your style to different personalities. For example, at C Level, the message needs to grab their interest in the first 30 seconds which means presenting a concept, with language that supports that message.

I always put myself in the position of the recipient and try to anticipate what I would want to know and ask, how they think, their pet subject, things that would influence a positive discussion.

I use an ice breaker unrelated to the data narrative because Data Governance will challenge their beliefs and I am trying to develop a relationship that creates trust and ultimately influence.

At the end of the day, each of us develops our own styles through trial and error. Go with those that you feel most comfortable.

Never underestimate the power of WIIFM, what’s in it for me. Understanding those personal drivers of your stakeholders and how they would benefit from Data Governance will be fundamental to your success. 

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

I’m not a big reader, but in 2006 Jill Dyché and Evan Levy published, at that time, an inspirational book called Customer Data Integration. This has been the only data book I have ever taken to heart because of its narrative. Jill is a wonderful storyteller where she brings to life the data challenges. If you ever get the opportunity to talk with Jill or listen to her, go out of your way to do so.

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

Getting started. We hear the saying ‘they just do not get it’ and use that as an excuse for not landing our data message. Looking under the covers I normally find that they do get it but data is not high on their list of priorities, or they have been subjected to the technology bias too often, or they cannot see the value in dedicating energy to a vague concept.

My biggest challenge has been turning that supertanker. Convincing stakeholders who are either disinterested or openly negative to the changes being proposed to establish a company-wide data discipline. Remember changing a culture requires commitment.

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

Company or industry for me is no different. Data is data and its management are broadly the same process.

However, I am a little different in that I look to a Chief Financial Officer (CFO) as the ultimate consumer of data, they would benefit directly through a well oiled and efficient data discipline. Harks back to my days as a spreadsheet jockey. Give me better data that I can trust.

Many people would disagree with my target audience, and to be honest, 5 years ago I would have agreed with them.

My rationale is that Data Governance starts predominantly through the management of master data. These are the foundations of every business process, customer, supplier, material, people etc. Every process executed in business has either an explicit or implicit financial impact that lands in finance. Much of the master data is touched by a finance process, for example, customer credit, material costing, supplier bank details, payroll.

Therefore, by inference finance really does have ‘skin in the game’ when it comes to consistent trusted data. Why would you not at least start the data journey in finance? 

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

Keep it simple. The saying ‘think big, start small’ really rings true to Data Governance. You want to take your stakeholders on a journey of discovery and enlightenment, not a slog up Mount Everest. 

There is no right answer, just many paths with potentially different outcomes. Choose wisely.

Finally, I wondered if you could share a memorable data governance experience?

My first day in a company as the Global Data Governance Manager I attended the inaugural Data Governance Executive Forum, only to be told by my boss that he was not able to attend and that I should chair the meeting.

My first day.

I didn’t know the people, their subject areas, what had gone in the past, even the format of the meeting. This was to be my introduction into the world’s largest multinational of this industry.

I was petrified.

I learnt a great deal about the people, but more importantly about myself. In the room were a group of supporters, a group of antagonists with the remainder ambivalent.

By the end of the 3-hour meeting we had achieved a consensus on the way forward for Data Governance, the challenges I would have to overcome and most importantly the frequency in which I would sit down with them one on one over a coffee.

The outcome was the embryo of Data Governance that would ultimately get established and span the entire company.

On reflection, if I had made a mess of that first-day induction, Data Governance would have been consigned to the ‘failed project’ bin.

The Communication Perspective

Good communications have always been vital for successful Data Governance, but with most of us working from home, communicating our message well has never been more important. Justin York often supports me with my clients and I am so pleased that he agreed to share some advice on good communication: 

We live in uncertain times where most of us have to adjust to navigating the online world of business. For this reason, clear communication is even more important in everything we do, particularly where businesses are trying to keep functioning with the majority of their staff working from home. While there is excellent technology available for professionals, which enables them to conduct operations and maintain communication, this may come with reduced effectiveness.

In these circumstances, it becomes even more important for the messages sent to your colleagues to be clear and unambiguous. This can be a real challenge, particularly if the technology you have doesn’t allow for the use of camera. You should not assume that your audience has any prior knowledge of the subject area you are trying to convey, as this may lead them to misinterpret it according to their own personal experience. Therefore, providing sufficient context in any messages sent will help to assure more effective communication.

Even outside of these challenging times, communication around subjects such as data governance can prove difficult, as some find quite dry and uninteresting. The issues around individual perspective and context are still relevant. I have worked in several large to multi-national companies, delivering change around data governance, where many individuals seemed hesitant to engage in this process. This is predominantly because they had their own viewpoint on what it will mean; often influenced by rumour (the opinion of others on the topic) or by personal experience in other organisations.

In addition to the need for senior leadership buy-in in data governance, which as we all know is key to success, we also need to engage other people in the organisation to do the things we need them to do. The key is engaging them from their own point of view (or perspective) and providing essential context to them around what we expect of them. Specifically, this can be achieved by:

§  A clear introduction the topic aligned to their business area.

·       Giving examples of the consequences of not following the initiative suggested and, as we like to say, present them with their own ‘data horror stories’.

·       Providing sufficient context to allow for an accurate interpretation of what is expected; detail such as ‘we don’t want you to get your hands dirty’ or perhaps ‘this is a leadership / decision maker role’ can engage senior leaders

·       Expecting both negative and positive feedback. Although we may prefer the latter, if we get a lot of the former then we haven’t quite gotten down to the actual pressing issue, which prevents them from stepping forward and making progress.

The message is everything. Without a clear, concise, unambiguous message then there will always be a degree of difference in how its received. The reality is that we always apply our own perspective to any message we receive, and that can colour our judgement of what has been said.

When on the receiving end of messages, I always feel it best to stop for a couple of seconds and just consider what was said, who said it and try and put myself in their shoes to get a better idea of the direction that is required.

Also, when receiving messages, we may adopt a particular ‘lens’, which will colour our view of what we are reading, for example:

·       We might treat the words as facts and accept them as such; if we do this, we tend to have no emotional attachment to them and thus we process them as they are said and we wait for the next instructions.

·       We perceive them as a personal attack; we might receive words that are directed at an audience to varying degrees as only meant for us. That can feel like a stinging assault on our emotions and we might react to those words with disbelief, though we might, after consideration, accept them as true.

·       We might treat the words in a cavalier manner e.g. well that’s not directed at me and thus the response might be ‘so what’.

When people receive communications, it is essential that we the communicator can gauge their reaction. Obviously this is much easier when the communication happens face-to-face. I personally have found that, in terms of data governance, there are three main responses:

§  “I fully understand and I’m onboard, let’s get on with it!”

·       “I’m far too busy to be engaged in this task, but I am happy to delegate it to a junior member in my team?”

·       “That’s nothing to do with me, it’s a problem for IT, I don’t own any data!”

I’m quite sure that many of you will also have experiences the same or similar responses!

The reality is that it falls to us to make clear what we are talking about, what we are trying to achieve and, most importantly, what’s in it for them; after all, who wouldn’t want quicker response times, with more accurate data!?

So in the troubled times we are living in, we need to be even more cognisant about the communications that we send, whether that’s work related or personal. Over distance and without direct interaction, perspective will come to the fore and who knows what a message might turn into.

You can find out more about Justin here.

Data Governance Interview with Theerachai Aurprasertwong

It was an honour to be asked to travel to Thailand to deliver training last December. I had the most amazing three days with Theerachai and his wider team. I was thrilled when he agreed to be interviewed for my blog, to share some of his experiences with you.

Theerachai is experienced in data governance and data architecture with more than 15 years of experience in the financial services industry. Outside of work, he is a father of a 6-year-old son and enjoys playing badminton, horse riding, archery and ski-ing.

How long have you been working in Data Governance?

Totally, 10 years. Five years at the business consulting firm. The other five years at the current work (Krungsri Bank).

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

When I worked as a data architecture under the enterprise architecture team, data quality was one of the most frequent and most important issues that we handled in the project. So, I stepped in, little by little.  I’ve fixed both short-term and long-term DQ issues. Along the way, I realized that data quality can’t be fixed on a project basis. It needs dedicated structure and framework to keep it in the good shape. A few years later, I decided to work full time to build data governance from scratch at Krungsri Bank. That was when the real data governance journey began.  

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

You need good common sense and always to be open to ideas and knowledge from outside. When you are stuck at something and you research on the internet, some concepts / discussions / articles seem impractical and unrealistic. However, if you apply them with your common sense, it can be very useful in certain situations.   

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

Books and articles on the internet are great.  To be honest, I could not understand what Data Governance was after I read my 1st Data Governance Book.  Learning by doing is my best approach. If you have a budget, you can engage an expert from time to time to do a health check on your data governance framework.

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

The biggest challenge is to turn the concept into reality. All those concepts online can look like the dreams of people from Mars!  Great communication may not enough, you need a practical way to apply data governance over time.

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

Many years ago, Data Governance was popular only in financial services and the telecommunication industry.  Now, every organization in which AI/ML or data analytics are the core competency need a strong data governance practice. Moreover, it cannot be constructed in a few months or years. It takes time and evolution.  

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

Data governance is not a sexy job compared to data science or data analytics. It is not IT project, it is the business matter. However, it will pay out great benefits to the organization and yourself over time.  

Finally, I wondered if you could share a memorable data governance experience?

Some data fields are very contentious. We have had a meeting with 20+ data stewards and we spent 4 hours trying to get an agreement on “Who is our active customer?”. We couldn’t get an agreement that day. Everyone felt exhausted and unproductive. I couldn’t remember exactly how we accomplished the first version for the data dictionary. However, it was the tipping point that shapes us today.  These days, those people are now our influencers/promoters who help us to build and sustain data governance.

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:

Schedule Appointment

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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