MDM and Data Governance: Better Together

It’s not one or the other, or, one vs the other… MDM and DG are better together. And for good reasons.

Learn as Nicola Askham, The Data Governance Coach, discusses the relationship between Data Governance (DG) and MDM, how they benefit each other, and how to get maximum value from both. Specifically, you will learn:

  • Why: It’s not one or the other; MDM and DG are better together
  • What: Prioritize your MDM and DG efforts by knowing where to focus and what to prioritize for quick wins
  • How: Know how much DG you really need and when; before, during, and after your MDM implementation

Let’s begin with what is data governance?

Nicola shares, “Proactively managing your data, to support your business to achieve its strategy and vision.”  Of course, the next step is ‘the how’ of implementing data governance.  She shares her data governance framework which focuses on three areas: policy, process, and roles & responsibilities.

This framework helps clears up and clearly documents who is doing what, by when, and how to be on top of proactively managing your data.

Now, what is master data management?

Put another way, Nicola says, “Master data management is about creating data records we all know and trust, and that allows us to use this data to manage our business in the most efficient way, without making mistakes and losing money.”

So, then why do we need data governance AND MDM?

We need controls, processes, and parameters around the trusted data that you are going to use.  It is easy to let your trusted data become corrupt in a sort of slow, controlled death when you don’t have a data governance framework.  “Come, on” you’re thinking, is that really possible?  Why, yes it is!  No, seriously think about the new project that happens to want to use one of the fields in your MDM system in their project.  And without a framework describing how and when that data is to be used, think of what happens when they use the field in a new and different way just for their project.  And the next team does it, and on and on…easy to lose that trusted data foundation.

Challenges/Objections You Might Hear with Doing Data Governance at the same time as the MDM Rollout

It will take longer.  Maybe, but not usually in our experience.  A ton of time is wasted in projects going back and forth, with business stakeholders figuring out who owns the information, how should it be documented and managed, whereas if you simply begin your MDM project by documenting the key information in the DG framework – you would have all the answers, right at your fingertips and can get right down to business including the key players at the perfect time.

It will need more people.  Could be – but wouldn’t it better to have the right people helping guide and check on the relationship between the data and the business processes vs waiting to find out afterward, you completely missed the mark?

Higher costs.  Perhaps if you have no data governance team members but then possibly, yes it will cost more upfront to get this right, but more significantly, you will than get the data definitions documented, you will know the data stakeholders and the consequences of making changes to your data foundation, and in the end this actually saves you money and typically means a more successful MDM project.


What does it look like with MDM AND DG?

When is the best time to start data governance for the MDM project?

Of course, you know we are going to say ‘before’!  But seriously, if you are already going along with your MDM project, then the next best time to introduce data governance into your project is today!   That’s right, it’s never too late, to at least get down on paper and into your project plan the key elements of the data governance framework that you need to do at a minimum.

Begin Here:

It’s easy to get overwhelmed or to derail your project if you start letting yourself get bogged down in details.  Focus on these areas:

It’s also really helpful once you get an idea of the steps to take as to when you should implement these steps.  Nicola shares a great infographic that will help you:

There is a quick checklist you can download which gives you the information you need to implement data governance within your MDM project.


Benefits of Data Governance with MDM without Implementing a New System

You can still get the same benefits even if you aren’t moving data into a new system, by taking the same approach against data in your source systems.  Most source system data is not of the highest quality and so can absolutely be improved upon following the data governance approach.

Using the data maturity model, what is the minimum level you should be at when you add data governance into your MDM project?

You want to be at least at the proactive stage – that is ideal.  However, the faster you can move on from the awareness phase and get started the better.  Remember, when is the best time to start data governance – today – if it’s not already in place!

How senior should the data owners be?

As senior as possible while balancing the realistic work required against the timelines of your project.  They will need the budget, authority, and resources to make decisions & changes to the data.

Best practics for DG SLAs.

Nicola doesn’t typically put in SLAs in place for data governance but more around the MDM system: adding fields, deleting fields, response times, resolution times, or turn around time on data definition reviews.  Data governance is more a cultural change and framework implementation that takes time versus using SLAs on the mechanics of how the system is functioning.

What is the difference between data stewardship and data governance?

Data stewardship is part of data governance.  Data stewardship/data ownership are part of the roles/responsibilities within the data governance framework.

What are some of the mistakes that companies make?

Doing it from IT, should be business-led; and another common one – misalignment with corporate strategy.

Quick Wins for Data Governance

Eliminate manual data processes or workarounds due to bad data quality.

Look for the team that does customer complaints – focus on rework or issues caused by bad data – wrong addresses, missed shipments.

What are some KPIs for Data Governance?

Some of the benefits aren’t seen until you do it.  It is more focused on the progress made on the project vs value gained as at times it can be harder to quantify.  Check out Profisee’s BIR program for how we help you calculate this value potential.

Roles – Data Owners vs Data Stewards – what is the difference?

Data owners are the most senior role within the organization that has the budget, authority, and resources to make decisions & changes to the data.  That being said, on a day to day basis, they will typically appoint a data steward from their organization to actually do the work that needs to be done with the subject matter expertise on the data.  So typically a data steward will write up data definitions and give them to the data owner for approval, or perhaps document the rules around acceptance for data quality with the approval of the data owner.

In summary, we reviewed:

Building a trusted data foundation is at the core of what every business must strive for today.  This foundation is the strongest it can be when companies take the time to make data governance a part of their MDM project.  DG and MDM – better together.



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MDM vs. MDS graphic
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