The metadata repository is the right place to store data quality standards: those that can be automatically transformed into database constraints such as referential integrity, data types, data nullability, data domains etc. as well as, more importantly, those business rules that require human interaction.
The hardest part is the perseverance and discipline necessary to maintain the data quality standards, but also to instruct and monitor users that standards are consequently applied.
My additional comment
To answer the question .., related to my comment "The hardest part is the perseverance and discipline necessary to maintain the data quality standards, but also to instruct and monitor users that standards are consequently applied.":
The weakest element in the integrated system of people - processes - tools is undoubtedly the human factor. Users that enter data do not only need to be trained and monitored in their doing, but the organization has to create a cultural climate that rewards high quality of data.
Example: If people that enter data are paid by number of correctly and completely created/updated objects (persons, addresses, products, orders etc.)), the resulting data quality will naturally be higher than if those people are paid by time.
In general, there needs to be a system of incentives that make it attractive for users to contribute to data quality. A simple, but important factor to increase their motivation is also to ask users on a regular basis for their feedback about difficulties and possible improvements of the process.