Data Trustability: The Bridge Between Data Quality and Data Observability

As organizations become increasingly data-driven, ensuring data is trustworthy is just as important as making it observable. While data observability helps monitor data flow, freshness, schema changes, and pipeline performance, it often focuses on metadata issues and may miss critical record-level data errors that directly impact business decisions.

Data trustability extends beyond observability by leveraging machine learning to detect data quality issues such as completeness gaps, inconsistencies, duplicates, anomalies, and invalid values. By identifying errors earlier in the data lifecycle and reducing reliance on manually defined rules, organizations can improve data reliability, scale quality controls efficiently, and strengthen confidence in their data assets.