Data quality rules are the key to data quality. A data quality rule is simply a statement in plain language that defines some attributes about the data you’re using.
Data is everyone’s responsibility, so get the users involved. What are the steps involved to set up a data quality process?
Profile: Analyze your data to determine the quality of the data based against your established data quality rules.
Cleanse: Make sure there are no duplicates or incorrect, bad, missing, or irrelevant data.
Standardize: Create naming conventions and data standards, enforced with validation rules and picklists, and train all users.
Match & Merge: Create your golden record, match and merge duplicates (where appropriate), and consider Master Data Management principles.
Monitor: Establish process and controls to measure the ongoing quality of data
If you can get this correct and working well, then you should be able to quickly establish good quality data.