Root Cause Analysis and Resolution

Fixing a data quality issue without addressing its root cause guarantees recurrence. The root cause analysis framework: what is the error? Where was it introduced? Why was it introduced? What prevented detection? How do we prevent recurrence? The "what" is the symptom. The "where" identifies the process step. The "why" identifies the human, system, or process failure. The "prevention" identifies the control that was missing. The "how to prevent" designs the control. Without the full chain, the fix addresses the symptom and the error returns next week from the same source.

1. Identify the Pattern Is this a one-time error or a recurring pattern? One-time errors get corrected. Recurring patterns get root cause analysis. The distinction matters because the investment in prevention is only justified for patterns.
2. Trace to Source Where in the data pipeline was the error introduced? Data entry, integration, transformation, or calculation? Each stage has different error mechanisms and different prevention approaches.
3. Implement Prevention Add a validation rule, fix the integration mapping, update the training material, or redesign the data entry form. The prevention should make the error impossible or detectable at the point of introduction.