As organizations embed AI into products and workflows, the cost of bad data has never been higher. Delayed data, incomplete loads, and subtle schema changes do not just break dashboards. They degrade model outputs, increase compute waste, and erode trust across the business.

In this session, we will explore how Datadog delivers end-to-end data observability across ingestion pipelines, transformation jobs, warehouses, and downstream BI and AI applications. You will see how teams use anomaly detection to surface critical issues early, map lineage to understand downstream impact, and connect data quality signals with infrastructure and job performance.

We will walk through real-world examples of how data engineers and platform teams reduce operational risk, improve collaboration, and ship AI-powered features with confidence.

You will leave with a clear approach to building a resilient data foundation that supports both analytics and AI without adding new silos.