Data Quality Rules and Metrics

What Is the Difference Between a Standard and a Rule? 

A standard defines what is acceptable. 

A rule, on the other hand, is the mechanism used to verify compliance with that standard. 

Example: 

• Standard: The field must not be empty. 

• Rule: Validate the percentage of empty values on a daily basis. 

Standards define expectations, while rules enable practical verification. 

How Are Rules Built Within the System? 

Designing a data quality rule typically involves the following steps: 

1. Identify the data asset to which the rule applies 

1. Select the relevant data quality dimension 

1. Define the acceptable threshold 

1. Specify the validation frequency 

1. Activate alerts when deviations occur 

This process transforms data quality into a measurable and operational practice, rather than a general description. 

Why Do These Rules Matter to Architects? 

For data architects, well-defined quality rules: 

• Ensure consistency across systems 

• Support scalability as data environments grow 

• Prevent conflicting interpretations of quality standards 

• Connect data quality practices directly to governance frameworks 

How Are Metrics Linked to Decision-Making? 

Data quality metrics are not presented as numbers alone. They are designed to: 

• Reflect actual data usage 

• Highlight the potential operational impact 

• Clarify priority areas for action 

This makes quality indicators meaningful for both technical teams and decision-makers. 

Conclusion 

Data quality rules transform theoretical concepts into an operational system that can be monitored, enforced, and continuously improved.