Data Quality Monitoring Framework Delivering 99% Accuracy and 50% Faster Issue Resolution

Background:

Reliable insights required strong data quality controls, but the absence of an automated anomaly detection system weakened accuracy. Repeated validations and duplicate quality checks across the pipeline slowed analysis, while reduced trust in reported metrics delayed decision-making.

Impact

Stronger data quality assurance led to faster decisions, improved compliance, and increased productivity.
Achieved 60–70% reduction in manual QA effort, 40% drop in false alerts, 30% fewer data reruns, and a 20–30% increase in trust in reported data.