Following up on my post about word quality and inspired by a blog post by Joyce Norris-Montanari called “Things That Don’t Work So Well – Doing Analytics Before Their Time” in which the word “unmaintainable” is used I want to challenge my English spell checker even further with the rare and apparently not really existing word but frequent issue of unmaintainability.
I have previously on this blog pondered that you can’t expect that because you get it Right the First Time then everything will be just fine from this day forward. Things change.
This argument is about the data as plain data.
But there is also a maintainability (this is apparently a real word) issue around how we store data. I have many times conducted data quality exercises as deduplication and matching with and enriching from external reference data in order to reach a single version of the truth as far as it goes.
An often encountered problem is that this kind of data processing can get us somewhere close to a single version of the truth. But then there is a huge obstacle: You can’t get these great results back to the daily databases without destroying some of the correctness because the data structures don’t allow you to do that.
Such kind of unmaintainability is in my eyes a good argument for looking into master data management platforms that allows you to maintain your master data in the complexity that supports the business rules that make your company more competitive.
Liliendahl on Data Quality 
Good points, Henrik.
One of the most common mistakes made in data analysis and modelling is that of seeing complexity being of a higher order than simplicity.
The opposite is in fact the truth. Real power in data modelling comes from being able to identify and model the underlying simplicity that is the cornerstone of data quality in any enterprise, no matter how big.
The sad fact is that there are too few people with the skills to identify and model this simplicity. To add to this there are far too many who attempt to model data in an enterprise without the essential blueprint, the Logical Data Model.
Complexity is created, not by the inherit complexity of data, but by essentially bad practice and lack of skill.
If data structures are complex it is because practitioners are missing the simplicity. If they are missing the simplicity, they are missing the point!
Regards
John
Thanks for commenting John. To make it clear: My point is that neither simplicity nor complexity is a measure of quality; it is ability to support the business rules. I agree that in practice many data models are either too simple or too complex or just too bad.
By the way I’m looking forward to your webinar on DataQualityPro about Business Systems Modelling for Data Quality today.