The most common symbol for Easter, which is just around the corner in countries with Christian cultural roots, is the decorated egg. What a good occasion to have a little “which came first” discussion.
So, where do you start if you want better information quality: Data Governance or Data Quality improvement?
In order to look at it exemplified with something that is known to nearly everyone’s business, let’s look at party master data where we face the ever recurring questing: What is a customer? Do you have to know the precise answer to that question (which looks like a Data Governance exercise) before correcting your party master data (which often is a Data Quality automation implementation).
I think this question is closely related to the two ways of having high quality data:
- Either they are fit for their intended uses
- Or they correctly represent the real-world construct to which they refer
In my eyes the first way, make data fit for their intended uses, is probably the best way if you aim for information quality in one or two silos, but the second way, alignment with the real world, is the best and less cumbersome way, if you aim for enterprise wide information quality where data are fit for current and future multiple purposes.
So, starting with Data Governance and then long way down the line applying some Data Quality automation like Data Profiling and Data Matching seems to be the way forward in if you go for intended use.
On the other hand, if you go for real world alignment it may be best that you start with some Data Profiling and Data Matching in order to realize what the state of your data is and make the first corrections towards having your party master data aligned with the real world. From there you go forward with an interactive Data Governance and Data Quality automation (never ending) journey which includes discovering what a customer role really is.
Liliendahl on Data Quality 
I think theoretical arguments can be made for starting with either of DQ or Data Governance. Because they are both integral parts of ongoing data value management in any organization, I think you can successfully enter the cycle at any point with an improvement project.
In the real world, I think the answer is actually to go with whichever one you can fastest form the strongest backing for using business value arguments. Then, if you design and manage the first projects well, you can “sneak” in a lot of the ideas of the other one with implementation of the first.
If you can get one project (Data Gov or DQ) in and use some cultural change techniques to introduce the other by describing them as two parts of an interlocking whole, then the question (hopefully) becomes less important.
I would agree with Kelly on this.
My experience has been that it doesn’t really matter how you get the ball moving as long as you have some degree of internalised roadmap for the “end game” (high quality, well governed information).
Also, my experience has been that being too married to the terms “Data Quality” or “Data Governance” can be a barrier, particularly when the organisation is more concerned with making a particular problem (cost of direct mail, inability to identify customers properly,challenges with product data).
The unfortunate truth I’ve witnessed is that the first route in is often a cleansing project to fix a particular hot-button issue.
Thanks Kelly and Daragh for sharing your thoughts. So, in short: “Use business value arguments (decorate) to get the ball (egg) moving”.