In fact, it’s Jim’s post that started me thinking about whether data and metadata quality issues stem from the same root source.
I work with a product that, among other things, infers table relationships and proposes ER diagrams based on statistical patterns found in the data. We analyze the actual data values, rather than documentation and naming conventions, because the truth is often in the data. (Of course, data quality issues add noise to that truth, but that’s a topic for another day.)
Why do we analyze the data? Because database documentation, ER diagrams, naming conventions and subject matter experts’ memories are usually incomplete, if not errant or flat-out missing.
In other words, this solution exists because organizations have predictably poor metadata.
As with data quality, solving metadata quality means addressing the root problems. Technology solutions can help, but will never resolve the challenge in and of themselves.
I’d argue that similar human motivations come into play for metadata quality as with data quality. For example:
- short-term thinking
- believing documentation is someone else’s role
- lack of understanding of the impact
- task overload
Jim lists some great behavior data quality readings. I think they’ll be worth a re-read from a metadata perspective.
What behaviors do you see as root causes for poor metadata quality?