In the era of the agentic enterprise, the speed of AI is limited by the quality of its data. To move beyond experimental AI to production-grade intelligence, leaders must automate trust.
Join Janene Ward (GM), Matthew Callender (IQVIA), and Vicky Andonova (Anomalo) to learn how global innovators operationalize data quality at scale. We'll explore how to move beyond brittle, manual rules toward a proactive, cloud-native architecture that identifies anomalies before they disrupt operations.
This session features General Motors and IQVIA discussing the transition from manual data firefighting to autonomous, AI-driven observability. Learn how these leaders use Anomalo and Databricks to build "AI-ready" foundations, reducing engineering toil while scaling trust across global supply chains and complex telemetry.
Move from "break-fix" cycles to autonomous detection that identifies anomalies before they disrupt operations.
Scale quality across diverse domains, from vehicle signals to healthcare insights, without brittle manual rules.
Use AI assistants to democratize data discovery and governance, making trusted data accessible to every team.
Lessons from migrating legacy estates to a unified, governed Databricks platform built for production AI.
