May 20
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Data observability is fast becoming a foundation for any organization serious about AI. In this video, we break down what data observability actually is, how it differs from data monitoring and data quality, the five pillars every framework should cover, and which tools lead the market in 2026. You'll learn: What data observability is and how it extends beyond monitoring into alerting, triage, and remediation Why data observability is critical for AI initiatives, where agents make continuous, high-volume decisions on data The difference between data observability and data quality — and why you need both The 5 pillars of data observability: freshness, schema changes, volume anomalies, distribution anomalies, pipeline health, plus data lineage as the connective tissue Real-world use cases in financial services and asset management, including regulatory reporting, market data validation, and reconciliation A 2026 landscape of data observability tools, from unified platforms like Ataccama, to open source (Great Expectations, dbt tests) to specialized vendors (Monte Carlo, Soda, Bigeye, Acceldata) Whether you're new to data observability or scaling coverage to support AI workloads, this video gives you the framework to evaluate your current approach and plan next steps. To learn more about Ataccama's data observability offering, get in touch with our team: [link] #DataObservability #DataQuality #DataEngineering #AIReadyData #DataManagement #DataLineage #DataPipelines #Ataccama