JAGAN ANKAM (Senior Member, IEEE)  received the master’s degree in management information systems from the Sikkim Manipal University. He has worked for over two decades in the IT industry at reputed companies Tata Consultancy Services and CGI Inc. He has led several projects at TCS and has extensive work experience with various data engineering technologies and AI Cloud platforms. Also, he has experience in working with a wide range of data science and data Lakehouse projects. He is currently a Senior Consultant in computer information systems with CGI Inc - Miami Florida. His research and work interests include AI data engineering, cloud database systems, data governance, and cloud computing in the domain of finance and e-commerce. His research publications appeared in reputed venues, such as IEEE conferences, IJCESEN.

Title: Data Intelligence at Scale: Building Trustworthy AI on a Governed Lakehouse Foundation - Abstract: Nearly every enterprise today has an AI initiative underway and nearly every enterprise is discovering that having AI is not the same as getting value from it. The bottleneck is no longer access to models or compute; it's the ability to execute. Organizations with the most mature data foundations are pulling ahead, not because their models are better, but because their data is trustworthy, governed, and usable at the speed decisions actually require. Everyone else is stuck running pilots that never scale. This keynote makes the case that governance, long treated as a compliance afterthought has become the single biggest determinant of whether AI investment turns into business value. Ungoverned, fragmented data doesn't just create risk; it silently caps how far any AI program can scale, no matter how sophisticated the models on top of it are. To make this concrete, the talk walks through a four-stage data maturity model that most organizations recognize themselves in: siloed data scattered across disconnected systems; a consolidated lakehouse that unifies storage but not yet trust; a governed lakehouse with lineage, access control, and data quality built in; and finally an agent-ready lakehouse, where data and AI systems can be trusted to act autonomously because governance is enforced continuously, not audited retroactively. Drawing on real-world patterns from organizations that have moved through these stages, this session offers architects, engineering leaders, and researchers a practical way to diagnose where their own organization sits today and a clear, actionable set of priorities for the next stage of the journey, whether that means fixing foundational data quality, building out governance infrastructure, or preparing for autonomous AI at scale.