Data Governance Interview - Bonny McClain

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I haven’t done a Data Governance interview for quite a while, but while preparing for a webinar I am doing with Bonny on Data Governance in Healthcare (you can register here) it became clear that I had to ask her to do an interview as she has so much expertise to share.

Bonny curates data from the intersection of health policy, health economics, and healthcare to create powerful storytelling narratives. Real insights come from the ability to hold tensions and bring multiple data sources to the conversation.

The data revolution is here, and her expertise is tackling industry specific problems-- rendering them solvable with data-- relying on a wide variety of tools like Python, R, SQL, and Tableau data visualization.

A big advocate of data literacy Bonny is a life-cycle data consultant. The ability to appreciate the overall concept while simultaneously thinking about detailed aspects of implementation allows different levels of abstraction to be curated creatively and empathetically.

How long have you been working in Data Governance?

Since I first began working with electronic medical record (EMR) systems. Working with relational databases it became painfully obvious that data systems and data assets were not being managed across their lifecycle. Data quality issues were impacted by non-existent information governance often exacerbated by the move from paper records and charts to electronic databases.

 

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

In smaller community medical practices and even larger health systems—information was being generated and shared across brick and mortar institutions. As physicians became curious about data questions that extended beyond administrative concerns there was a palpable need to understand “data dictionaries” and the schema and architecture of data storage. Understanding data assets was a natural evolution and requirement to glean insights from the digital data being created.

 

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

Because I work with the populations that rely on the quality of data—the emphasis is on usability but not at the expense of safety—I have the data analytic skills (recent certificate in applied data analytics from Columbia School of Engineering) to identify which measures and variables are needed to answer a data question. Knowing that pieces of information often live in different parts of a database generates concern and an evolution of skills to ensure that patient records are matched to avoid duplicate records, missing data, inappropriately merged records, current medications and procedures—all with an eye to seek out potential opportunities for harm if data is incomplete, incorrect, missing, or low value.

 

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

You should mention that I snorted and began laughing. Although in full disclosure I work with many smaller organizations that haven’t been able to prioritize data governance for one reason or another. Our conversations about many data governance policies being articulated as “best endeavors” or a best effort illuminated for me why many of the best intentioned guidance falls short without out a complete and accountable data governance strategy.  

I do stress scalability with new clients so they don’t feel overwhelmed and tempted to park the whole process until “later”. I meet clients where they are—and most make significant process relatively quickly. Healthcare is unique as bad data here—can lead to deleterious outcomes and harms.

 The typical data client actually has low data literacy and maturity and often answers a data question before the analyses have been initiated. They want data that shows what they actually believe to be true—and in the face of a contrary outcome—move on to the average analyst that will gladly only report the curated outcome they seek.

Given that this is the typical healthcare professional—you can imagine that governance strategies that sit upstream from information governance are not recognized or prioritized.

 

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

I may be wrong but I feel the labels are not applied to the different behaviors correctly. Although I do believe that compliance, quality care (value), cost containment, evolving payment models, and safety are understood as information—and rely on proper custodial processes of information assets--the industry as a whole is at a crossroads of how data governance influences the rigor of information downstream from policies and processes.

 

As a final reminder Bonny and I will be having a conversation about Data Governance in Healthcare on the 1stMay – the webinar is free and you can sign up here: 

https://usergroups.tableau.com/datagovernanceforhealthcare

 

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How to Write a Good Data Governance Policy

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It won’t surprise you to learn that I sometimes find myself writing a data governance policy for my clients.  Sometimes my clients assume that I will just take a previous policy I've written and tweak it for them. However, this really wouldn't be very helpful for the new client, as it wouldn't be designed to meet their needs!

As with all things data governance, I don't think that there is such a thing as a standard approach, and there certainly is not one for a data governance policy. If there is no such thing as a standard data governance framework, why would you think that a policy written for another organization would work for you?

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

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

·     What is the scope of your data governance programme?

·     What is it that your organization is going to do to manage its data better?

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

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

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

 I admit sometimes the differences are subtle, but for a policy to be valuable, these subtleties really do need to be addressed.

So how do you write a good Data Governance Policy from scratch?

If you are just starting to draft a data governance policy, then I recommend that you take the following approach:

Assemble key senior stakeholders in a room together and get them to tell you what principles they want included in the policy.  What are the high level things that you want to achieve by having a data governance framework and policy in place? This could be things like “all data has a data owner”, or it could be describing which data will have data quality standards and monitoring in place, or which data will have definitions in your data glossary.

For some clients, this list of principles has been as long as twelve and others as short as six.

 I find that getting principles agreed is a lot easier than asking a group pf people what they want included in a data governance policy. Plus the conversation around the principles will give you a really good idea about what they want covered in their policy.  (And this additional information will also be important input when designing a more detailed data governance framework for your organization.)

Once you've drafted and circulated those principles for feedback, you should be able to make amendments and agree a list of principles. With the principles agreed drafting your policy in accordance is fairly straightforward.

However, don't make the mistake of believing that once it is drafted that everyone will immediately approve it because they already agreed the principles.  Seeing the detail in black and white often gives rise to more questions, suggestions or changes from your key stakeholders.

At this point, I really have to emphasize that for data governance to be successful, you need the senior stakeholders engaged. So the answer is not to tell them they're wrong, or to railroad them into accepting what you want to have in the data governance policy. If this framework is to be successful, it needs to have buy in from everyone. So you need to take all input at this stage very seriously.

Drafting a data governance policy is one of the many things on my data governance checklist.  

You can download a free version of the checklist here. (Don't forget this is a high level summary view, but everyone who attends either my face to face or online training gets  a copy of the complete detailed checklist which I use when working with my clients.)

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What’s the difference between Data Owners and Data Custodians?

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I often get asked about the difference between Data Owners and Data Custodians.

 If you've read my other blogs, you'll know that I'm not fixated on sticking rigidly to the standard role names, but for the purposes of this blog, I’ll to stick with what I consider “best practice” role names and consider both roles in turn:

A Data Owner is a senior business stakeholder who is accountable for the quality of one or more data sets. They are usually a senior business person who has the resources, budget and authority to be able to make changes to that data if necessary. 

Data Custodians are very much an IT role. They are responsible for maintaining data on the IT infrastructure in accordance with business requirements. I think the confusion between the roles and who should be making decisions about data is rooted in a long term lack of Data Governance.   

Before a company has Data Governance in place, it's common that the business has not been trained in articulating their data requirements and therefore it is often down to IT to interpret or make decisions in order to help the business.  However, once you have a Data Governance framework in place, the business usually gets much better at articulating data requirements and IT’s job gets easier. 

Another difference between the roles is that for Data Owners, I'm looking for named individuals who own one or more data sets. When it comes to Data Custodians. However, I use that more loosely as a collective term for all of the IT department who are supporting your infrastructure.  That said, it's not impossible to have named custodians. I've worked with a number of clients where they have named their Data Custodians.  These have usually been smaller organisations, where there is only one subject matter expert for each system and that person has been named as the Data Custodian.

Another frequent question is whether IT is ever a Data Owner? Now this is an interesting questions as generally, I would say, no. They might own meta data, or performance data around the systems, but nothing more than that. However, recently, I have come across circumstances where IT might own some data. 

One such example is maybe your Data Security or Information Security Team who may be monitoring telephone calls or internet activity. This often isn't being done for any direct business purpose, but rather to protect the business as a whole. If this data isn't being collected to meet the requirements of a business Data Owner, then it could easily be argued that IT own that data.

I also often get asked if only IT can be Data Custodians?  Generally, it is the case that Data Custodians sit within IT, but there are instances where business teams or functions may also act as Data Custodians. For instance, you may have a BI or Analytics Team sitting in the business reporting line, who perhaps manage and support your data warehouse.  Because of the work they do it is quite common for such a team to be considered as the Data Owners for all data that's in the data warehouse. However, this is not a correct approach, because all that data has a Data Owner when it sits in the source system and that person still owns it even when it's on a data warehouse. Of course, in a data warehouse a lot of data is aggregated, combined and often new data is derived or calculated. It is common for the BI or Analytics Team to be involved in these activities, but it does not mean that they own the new or aggregated data. It is more appropriate to consider them as Data Custodians for the data, whilst it's being managed, manipulated and reported on by that team.  After all they should be carrying out these activities to meet business requirements and the person who approved those requirements is the Data Owner.

I hope that this has clarified the difference between Data Owners and Data Custodians.  Roles and responsibilities are only one of many things that you have to address as part of implementing Data Governance. If you are struggling to get your head around everything else you should be doing and the order in which to do it, please download my free checklist to help you plan your initiative.

Data Quality Issues - Who Is Responsible for Resolving Them?

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One of the first processes, I believe that you should introduce in your Data Governance initiative is a Data Quality Issue Resolution process. In fact my last blog covered what you should include in a Data Quality Issue Log to help you get started with collating and resolving issues.

But the log itself is only part of the answer and I have been asked on numerous occasions to clarify what the Data Governance (or Data Quality) Team are responsible for doing when managing and resolving data quality issues.  This often gets asked by newly formed teams.

There seems to be a lot of confusion around who does what and I have often come across the expectation that the Data Governance Team will do or solve everything.  There are times when I truly wish that I had a magic wand and could simply fix all the data problems, but sadly that isn’t the case.  Over time, of course your Data Governance Team will develop knowledge and expertise about the data your organisation creates and uses, but they are not responsible for deciding what the remedial actions should be and especially not for undertaking any manual data cleansing that may be required.

However, I am not saying that they have no part to play in the process.  The best way to understand what the Data Governance Team are responsible for is to look at a high level simple data quality issue resolution process:

Raise Data Quality Issue

It will usually be a Data Consumer (business user of that data) who spots an issue and will be the ones to notify the Data Governance Team.

The Data Governance Team will then log the issue on the Data Quality Issue log and identify the data owner(s) of the data concerned.

The Data Governance Team notifies the Data Owner of the issue, who will advise whether or not they are the correct owner of the issue.

In addition, the Data Governance Team reports the current status of all open material data quality issues to the Data Governance Committee (usually as part of their regular agenda).

The Data Governance Committee reviews the open material data quality issues and prioritizes/directs on the remedial activities if needed.

Impact Assess and Root Cause Analysis

The business user who notified the issue, the Data Governance Team, and the Data Owner(s) assess and agree about the impact of the issue.  If the issue is agreed to have a material impact its resolution will be prioritised.

The Data Governance Team works with the Data Owner(s) to identify the cause of the issue.

The Data Owner(s) consider possible remedial actions to rectify the issue.

Remedial Action Plan

The Data Owner(s) proposes an approach to resolve the issue and prevent it from re-occurring.

The business user who raised the issue agrees whether the proposed action plan is appropriate (the Data Governance Team can facilitate discussions between the parties if needed).

The Data Governance Team updates the Data Quality Issue Log with the agreed actions and target dates.

The Data Owner(s) plans how and when the remedial activities will take place.

Monitor and Report on Action Plans

The Data Owner(s) and their team(s) undertake the agreed remedial actions (N.B. this may need the support of IT).

The Data Governance Team monitors progress on remedial actions against agreed target dates and reports on progress to both the impacted business user(s) and the Data Governance Committee.

The impacted business user(s) advise if timescales for resolution are not appropriate.

The Data Governance Committee then reviews progress and prioritizes/directs on the remedial activities if needed.

Of course, in practice, solving data quality issues takes more than the four steps listed above, but the additional steps will be sub-sets of the stages discussed above.  In addition, keeping it simple like this will help your stakeholders quickly understand who is responsible for what.

As with all things Data Governance, communication is key. I would recommend creating a simple high-level diagram of your data quality issue resolution process and using that in your communications to help people not only understand the process but also everyone’s role in it.

The Data Governance Team’s role in the data quality issue resolution process can be summarized as followed:

  • Identifying which Data Owner is responsible for the data which needs fixing and liaising with them

  • Maintaining the Data Quality Issue Log

  • Monitoring and reporting on open issues and associated action plans

I hope that has clarified the situation for you and remember you can find out more about what to include in a Data Quality Issue log here or download a free template for an issue log here.

Setting up the Data Quality Issue Process is just one of many things you need to do when starting a Data Governance Initiative - you can get a summary of all the things you need to consider by downloading my free Data Governance Checklist here.

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What do you include in Data Quality Issue Log?

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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

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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.

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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.


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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.

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