10 Data Quality Best Practices Every Team Should Follow

The Cost of Poor Data Quality

Gartner estimates that poor data quality costs organizations an average of $12.9 million annually. Here's how to avoid becoming part of that statistic.

Best Practice #1: Define Data Quality Dimensions

Focus on six key dimensions:

1. Accuracy: Data reflects the real-world entity

2. Completeness: No missing values

3. Consistency: Data is uniform across systems

4. Timeliness: Data is current and up-to-date

5. Validity: Data conforms to business rules

6. Uniqueness: No duplicate records

Best Practice #2: Implement Data Quality Checks at Source

Prevent bad data from entering your systems:

Best Practice #3: Establish Data Ownership

Assign clear ownership for each data domain:

Best Practice #4: Automate Quality Monitoring

Use tools like Great Expectations or dbt tests:

# Example: Great Expectations test

expect_column_values_to_be_between(

column='revenue',

min_value=0,

max_value=1000000

)

Best Practice #5: Create Data Quality Scorecards

Track and visualize data quality metrics:

Best Practice #6: Implement Master Data Management

Create a single source of truth for critical entities:

Best Practice #7: Regular Data Profiling

Profile your data monthly or quarterly:

Best Practice #8: Data Quality in CI/CD

Treat data quality like code quality:

Best Practice #9: Root Cause Analysis

When quality issues occur:

1. Document the issue

2. Trace back to the source

3. Fix the root cause, not just the symptom

4. Update monitoring to prevent recurrence

Best Practice #10: Continuous Training

Invest in data literacy:

Conclusion

Data quality is not a one-time project—it's an ongoing discipline. Start with these best practices and build a culture of quality across your organization.