Stop the Silent Data Drift: Achieving End-to-End Observability with Datagaps

Key Takeaways

1. Why rules alone aren’t enough: Learn how hidden anomalies slip past static checks - and how observability finds them.
2. ML-driven detection in action: see how Datagaps flags volume shifts, schema drift and freshness issues in real time.
3. Unified health scorecards: discover how quality rules + observability create a single, quantifiable view of data trust.
4. 3-step framework (Collect → Detect → Alert): instrument reports, apply time-series models, and deliver context-rich notifications.
5. Real-world impact: hear how customers cut issue-resolution time by up to 70% & reclaimed analyst hours.