Bad data doesn’t announce itself. It shows up as a broken dashboard, a customer complaint, or an AI model quietly producing wrong answers. By the time your team finds out, the damage is already done and trust erodes before you can fix it. As AI raises the stakes on reliable data, the cost of getting this wrong keeps climbing. 

In this session, we’ll show you how Datadog Data Observability helps teams detect, resolve, and optimize data quality and pipeline issues before they cascade downstream to AI models, BI dashboards, and customers. You’ll see how ML-powered anomaly detection surfaces silent data failures early, how end-to-end lineage helps you pinpoint root cause and blast radius in minutes, and how jobs monitoring keeps pipelines healthy and costs under control, all in one place.  

Speaker: Lara Kulkarni, Software Engineer, Datadog 

Lara Kulkarni is a software engineer on the Data Observability team at Datadog, the monitoring and security platform for cloud applications. Her work focuses on helping organizations track the health, quality, and reliability of their data. 

Speaker: Todd Pollack, Staff Engineer, Datadog 

Todd Pollack is a staff engineer at Datadog, the monitoring and security platform for cloud applications, where he works on data observability, helping teams detect and resolve issues in their data and pipelines before they affect downstream systems. 

Website: https://www.datadoghq.com/