Data Quality Control
Bad data breaks every system built on it. This workflow maintains data quality over time.
Data Quality Control
Data Quality Control addresses data quality degrades over time through errors, duplicates, and staleness, undermining automation and reporting. The workflow combines process, automation, and human review to resolve it.
Bad data breaks every system built on it. This workflow maintains data quality over time.
Problem definition
Data quality degrades over time through errors, duplicates, and staleness, undermining automation and reporting.
Observable symptoms
- ✓The problem recurs despite effort
- ✓No systematic approach to resolution
- ✓Manual workarounds that do not scale
- ✓Unclear ownership or accountability
Likely causes
- ✓No defined process for the problem
- ✓Manual handling that varies by person
- ✓No automation to enforce consistency
- ✓No review or measurement of outcomes
Required inputs
- ✓A defined process for the workflow
- ✓A system to track and enforce it
- ✓Clear ownership
- ✓Measurement of outcomes
Recommended workflow
Follow this sequence to address the problem systematically.
- ✓Define data quality standards for each field
- ✓Add validation at every entry point
- ✓Run periodic quality scans
- ✓Fix issues at the source, not just the symptom
- ✓Assign ownership for data quality
- ✓Track quality metrics over time
Automation boundaries
Automation validates at entry and scans for issues. The standards, fix decisions, and ownership require human input.
Human review requirements
A person defines standards, decides fixes, and owns data quality. Automation enforces and monitors.
Risks
Fixing data without fixing the entry process guarantees recurrence. Address the source, not just the symptom.
Limitations
No workflow is perfect. Continuous review and refinement based on outcomes is essential to keep it effective.
Measurement criteria
Track the occurrence of the problem before and after, the time to resolution, and the outcomes of the workflow.
Frequently Asked Questions
How do we measure data quality?
By completeness, accuracy, consistency, and timeliness. Track the percentage of records meeting each standard.
Who owns data quality?
A person, typically in operations or sales operations. Quality without ownership degrades.
Can data quality be fully automated?
Validation and scanning can. The standards, exception handling, and source-fix decisions require human judgment.
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