Some Basic Recommendations for Data Quality

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)
• Nullability
• 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.