Swaroop Jagadish

Most data teams are losing the governance battle. The data volume grows, headcount does not. And AI adds a new layer of urgency: agents are only as good as the context they can trust. OVO Energy will share how it moved from fragmented, manual metadata to an intelligent context platform. Learn how its team connected a complex, multi-source data environment, and accelerated time to insight. No headcount growth required.

Data leaders face an impossible mandate: govern more data, faster, with fewer people—while enabling AI. Metadata management breaks at enterprise scale without automation. This session reveals how organizations shifted from static catalogs to intelligent context platforms. Hear from Balyasny Asset Management and T. Rowe Price and how they’ve automated data governance, unified fragmented data landscapes, and cut time-to-insight.

AI agents need seamless access to enterprise data to deliver real value. DataHub's new MCP server creates the universal bridge that connects any AI agent to your entire data infrastructure through a single interface. This session demonstrates how organizations are breaking down data silos by enabling AI agents to intelligently discover and interact with data across Snowflake, Databricks, BigQuery, and other platforms. See live examples of AI-powered data discovery, real-time incident response, and automated impact analysis. Learn how forward-thinking data leaders are positioning their organizations at the center of the AI revolution by implementing universal data access strategies that scale across their entire ecosystem.

As AI reshapes every aspect of data management, organizations worldwide are witnessing a fundamental transformation in how data governance operates. This panel discussion, hosted by DataHub, brings together two forward-thinking customers to explore the revolutionary journey from traditional governance models to AI-autonomous systems. Our expert panelists will share real-world experiences navigating the four critical stages of this evolution: AI-assisted governance, where machine learning augments human decision-making; AI-driven governance, where algorithms actively guide policy enforcement; AI-run governance, where systems independently execute complex workflows; and ultimately, AI-autonomous governance, where intelligent systems self-manage and continuously optimize data stewardship processes. Through candid discussions of implementation challenges, measurable outcomes, and strategic insights, attendees will gain practical understanding of how leading organizations are preparing for this transformative shift. The session will address key questions around trust, accountability, and the changing role of data professionals in an increasingly automated governance landscape, providing actionable guidance for organizations at any stage of their AI governance journey.