Ramon Chen

The Lakehouse was built for human analytics and Classic AI - batch cycles, curated queries, periodic models, dashboard consumption. Agents are different consumers: continuous, contextual, non-deterministic, and intolerant of stale or ungoverned data. In this session, Ashwin Rajeeva, Co-Founder and CTO of Acceldata, demonstrates what agent-ready data architecture requires: federated compute and access across hybrid data estates, continuous observability, and runtime governance. Drawing on Global 2000 deployments, he introduces a four-phase maturity model - from Single-Plane to Autonomous-Plane - and the architectural choices that decide whether enterprise agents reach production or stall in pilot.

As organizations race to deploy AI, many discover the real challenge isn't building models — it's ensuring the data behind them is trusted, governed, and continuously fit for purpose. Traditional data quality and governance approaches rely on manual monitoring and reactive processes that can't keep pace with modern data ecosystems. This session explores how Agentic Data Management (ADM) is transforming enterprise data management by combining AI agents, data observability, governance, and automated remediation into one intelligent operating model. Rather than simply detecting issues, agentic systems proactively identify emerging risks, investigate root causes, recommend actions, and automate resolution — all while maintaining governance guardrails and human oversight. The result: greater trust in AI, faster delivery, reduced operational effort, and stronger compliance. We'll cover: Why AI initiatives demand a new approach to data quality and governance How agentic workflows use data observability to build data products and enforce governance A roadmap for adopting Agentic Data Management while preserving control, transparency, and trust

AI needs trusted data, via Data Observability enforced policies, powered by active metadata. Harmonizing siloed data catalogs, data governance, MDM, DQ, pipelines, all while aligning cost to compliance & value. See how Agentic Data Management allows business, technical teams & Agents to collaborate at speed & scale. Shifting from reactive workflows to intelligent data products built for business value, with ROI that also lowers net total spend!

Discover how Dun & Bradstreet and other global enterprises use Data Observability to ensure Quality & Efficiency, and enforce compliance across on-prem and cloud environments. Learn proven strategies to operationalize governance, accelerate cloud migrations, and deliver trusted data for AI and analytics at scale. Join us to learn how Data Observability and Agentic Data Management empowers leaders, engineers, and business teams to drive efficiency and savings at petabyte scale.