Data management is not a project with a finish line. It is an ongoing practice. Organizations that treat data quality, documentation, and protection as one-time tasks find that their data degrades over time. The organizations with the most reliable data are those that have built regular review into their routines.
Why Regular Review Matters
Several forces work against data quality over time:
- Staff turnover. New staff may not follow established conventions. Institutional knowledge about data practices walks out the door with departing employees.
- System changes. Software updates, migrations, and integrations can introduce new inconsistencies or break existing processes.
- Scope creep. Data collection tends to expand over time. New fields get added without clear purpose. Old fields are never removed.
- Changing needs. The data that was right for your organization three years ago may not be right today.
What to Review
A regular data review should cover:
- Data quality. Run your standard quality checks. Are completeness and accuracy rates holding steady? Have new problems appeared?
- Documentation. Is the data dictionary current? Are there new fields that have not been documented?
- Access and permissions. Do current staff have appropriate access? Are there former employees or contractors who still have access?
- Retention. Are there records that have passed their retention period and should be disposed of?
- Purpose alignment. Is the data you are collecting still aligned with your current organizational purposes?
Regular review only happens if someone is responsible for it. Assign a data steward or data owner for each significant dataset — someone whose job includes ensuring the data remains accurate, documented, and appropriately protected. Without clear ownership, review tasks fall through the cracks.
Building a Review Cadence
Different review tasks suit different frequencies:
- Ongoing: Quality validation rules running automatically, user feedback channels open
- Monthly or quarterly: Quality profiling, access review for high-sensitivity data
- Annually: Full data inventory review, retention review, documentation audit, backup restoration test
Continuous Improvement
Each review cycle should produce not just a status report but a short list of improvements. What quality problems were found and fixed? What documentation gaps were closed? What processes were updated? Tracking improvements over time demonstrates the value of the review practice and builds organizational commitment to it.
Key Takeaways
- Data quality degrades over time without regular review
- Review data quality, documentation, access, retention, and purpose alignment on a regular schedule
- Assign clear ownership for each significant dataset
- Track improvements over time to demonstrate the value of the practice
You have finished Data Best Practices 101. Continue with Data Governance 101 or explore Privacy 101.