A healthcare analytics company was losing credibility with stakeholders due to frequent dashboard failures and data inconsistencies. There was no clear ownership of data assets and no systematic approach to data quality.
Approach
1
Mapped data assets and established ownership model
2
Implemented data quality rules at ingestion and transformation layers
3
Built anomaly detection for key metrics
4
Created lineage tracking for impact analysis
5
Established incident workflow with clear escalation paths
6
Set up freshness SLAs with automated monitoring
Results
Data incidents reduced by 75%
Stakeholder trust scores improved significantly
Mean time to detect issues reduced from days to minutes
Clear accountability for every data asset
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