What are the six dimensions of data quality?

Six Dimensions of Data Quality

Data quality is typically measured across six dimensions:

1. Completeness Are all required fields and records present with no missing values?

• Example: What percentage of customer records have an email address?

2. Accuracy Does the data correctly reflect real-world facts?

• Example: Does the order amount match the actual transaction?

3. Consistency Is the same data consistent across systems and tables?

• Example: Does the customer name in CRM match the billing system?

4. Timeliness Is data available when needed and does it reflect the current state?

• Example: Is the reporting data refresh within the agreed SLA?

5. Uniqueness Is there no duplication of records that should be unique?

• Example: Are user IDs truly unique with no duplicate inserts?

6. Validity Does data conform to defined formats and business rules?

• Example: Is the date in YYYY-MM-DD format? Are phone numbers properly formatted?

Practical Application

Data engineers typically automate these quality checks within pipelines using tools like dbt tests, Great Expectations, or Soda Core.