Inspired by the initiative of Prash Chandramohan (@mdmgeek) here, below please find some basic notes and recommendations for Data Quality.
1. Create a business data model while limiting its scope to data which
- You are legally entitled to collect
- Have a clear business purpose
- Have a purpose that you can explain to the respective target group (customers, employees, suppliers etc.)
while avoiding to re-create entities / attributes that are rightfully already defined within the organization.
2. Define all business metadata regarding
- Their (business) meaning
- Format (length, data type)
- Range of values (where meaningful and possible).
3. Define use cases and related rules that serve a purpose-specific data quality.
4. As much as meaningful / possible: In business processes, programmatically
- Enforce the rules for business data (quality)
- At least, suggest a use-case-specific selection of values.
5. Educate business staff according to their role and responsibility in business processes about the purpose / use cases of data, in particular about the impact of
- Their choice of values when creating or updating data
- Deleting data.
6. Monitor the quality of data on a regular basis while applying / interpreting (use-case-specific) rules, e.g. using the Friday Afternoon Measurement (even if it's not Friday!).
7. Provide feedback to business staff and / or business analysts.