There has been a lot written in recent years about the relationship between:

• Master data management (MDM) and data governance.
• Data governance and data quality.
• Data quality and MDM.
• Data management and data governance.

And so the list goes on...

Written through the unique view of each writer and their "experience lens," these type of articles are virtually guaranteed to create a level of disagreement – and the data community doesn't disappoint.

Take data quality, for example. Some purists believe data quality cleansing and tactical improvements should never be made unless root-cause analysis and prevention work has been completed. Others recommend a more tactical stance: Why should the business have to wait when they can get some immediate relief from the pain of bad data?

In reality, there is no right and wrong; it depends on your particular circumstance.

The same applies to data governance and MDM. Many people believe you shouldn't commence MDM without data governance already in place. While that's an admirable goal, if you're leaking revenue because customers are churning due to poorly managed master data, then it's going to be a hard sell telling the project team to hold off until you've set up a data governance council.

Setting the vision

I think one of the problems we face in the industry is that we get too hung up on these acronyms and definitions. Because most experts on topics such as data governance hate the term they've been saddled with.

A recurring theme I found when interviewing practitioners and authors of data governance was that they hated the term "data governance" because it caused so much resentment and friction among their clients. In fact, I found that most of my interviewees on Data Quality Pro chose different, business-focused terminology instead of the default industry naming conventions.

So, here's a thought. Instead of getting bogged down with where MDM starts and data governance finishes – or arguing about the overlap in definitions between the two – how about looking at your data landscape as a greenfield site?

What if your organization was getting into business today. What would your data requirements be? What policies and procedures would you create from the outset? How would you define the master data strategy?

What you'll find when you take this approach is that when these disagreements about overlaps and definitions disappear, you're left with just  "the vision." What follows is a discussion around how you would transform your present business to that new world view.

When you take this stance, your focus won't be the discussion around what to call the individual data disciplines. Instead, you'll be able to decide how you're going to deliver the end-game. And you can call that whatever you like.