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This session will explore how AI is helping organizations move beyond traditional fraud detection to proactively predict and prevent claims leakage. It will cover the role of data, analytics, and intelligent automation in improving accuracy, reducing losses, and enabling smarter decision-making across claims management processes.
India’s DPDP Act is redefining how organizations handle personal data, making privacy and governance non-negotiable. Enterprises must move beyond compliance checkboxes to embed privacy into the core of their data architecture. And have to do it by May 2027.
Skyflow enables this shift by isolating sensitive data, enforcing strong governance, and minimizing exposure.
This foundation allows AI and analytics to run on protected data without compromising user trust.
The result: a secure, compliant, and scalable AI data stack built for India’s privacy-first future.
In this high-impact session, Anurag Chaudhuri explores how modern enterprises are evolving from traditional data pipelines to intelligent, agent-driven systems that can autonomously make and execute business decisions. Using relatable e-commerce scenarios, the talk breaks down complex concepts like Agentic AI, decision automation, and system orchestration into simple, practical insights.
Attendees will learn how organizations can move beyond dashboards and alerts to build systems that not only understand data but act on it in real time, driving faster decisions, improved customer experiences, and measurable business outcomes. This session offers a clear, future-focused perspective on how Agentic Operating Systems are redefining the way businesses operate at scale.
In a world where AI can build pipelines and infer schemas, the role of the data engineer is undergoing a fundamental shift. The skills that once defined the profession are being automated and that raises an important question: where does a data engineer’s value truly lie?
Join this session to explore how a data engineer’s role is not being replaced, but rather elevated. Understand how you can move beyond manual execution and step into higher-impact roles, shaping business outcomes, driving adoption, and applying the human judgment that no model can match.
Every enterprise wants AI, and most have more raw material than they realize. Industry research by IDC shows that while over 80% of enterprise content is unstructured, less than 1% of it is currently ready for AI consumption. This gap is what we call the “Context Crisis”, which is the difference between data that’s stored and data that’s truly seen. Closing it is what turns models that guess into models that know, and agents that assist into agents that can be trusted with real decisions.
This keynote argues that AI reliability is a data engineering problem before it is ever a model problem. Drawing from real-world failure patterns, like dark data, broken lineage, missing metadata, unversioned documents, and more, we’ll explore how modern DataOps disciplines turn this crisis into a competitive edge. From intelligent ingestion and semantic layers to data products and governance as code, we’ll walk through the engineering blueprint that transforms fragmented documents and unversioned information into the contextual fuel agentic AI demands.
This session explores how generative AI is reshaping product discovery into a more intuitive, predictive, and user-centric experience.
