Data Quality in the Modern Enterprise
Poor data quality is often blamed on reporting tools, but the real causes are upstream in processes, systems and governance. This whitepaper outlines a pragmatic approach to data quality.
Common failure modes
- Inconsistent definitions and master data.
- Manual workarounds and offline adjustments.
- Lack of ownership for key data elements.
Fixing quality at the source
Sustainable data quality requires changes to upstream processes, clear ownership and controls embedded into operational systems – not just downstream cleansing.