With tool sprawl, hidden costs, and integration nightmares, is the MDS era over? Panel can debate the rise of unified platforms vs. best-of-breed, and what the next architecture era looks like.

In this session, we’ll explore how large analytics enterprises can evolve from traditional data systems to AI-driven, modular architectures. The talk introduces five engineering pillars—Data Foundations, AI Agent Building (including persona-based “ask” capabilities), Agent Catalog & Monitoring, Internal Agent Marketplace, and Business Integration. Attendees will learn how data engineers can design semantic layers, build reusable AI agents for merchandising and customer analytics, and integrate them into live business workflows. The session highlights practical strategies for measuring impact, optimizing AI performance, and creating scalable solutions that drive measurable business outcomes.

Creating a single source of truth for customer data is no longer optional. This session will dive into the strategies, architectures, and governance models needed to integrate fragmented data systems into a unified ecosystem that powers seamless experiences and data-driven growth.

What does data governance look like when your biggest data consumer doesn't sleep, doesn't follow SOPs, and makes thousands of decisions every second? When a majority of your users bypass official AI tools entirely? When a single ungoverned prompt can leak training data, customer PII, or regulated information across your entire enterprise? These aren't hypothetical questions — they're the operational reality of GenAI in 2026. And the governance frameworks most enterprises rely on were built for an era when data consumers were human, deterministic, and slow. This talk presents DataGovOps: governance as code, dynamic access policies, AI-assisted classification, and end-to-end lineage from source through agent output. We'll explore why legacy governance fails under agentic workloads, how to architect consent and access as programmable policy, the recursion problem of governing AI with AI, and practical patterns for staying compliant under regimes like GDPR & DPDPA — without strangling the velocity that makes AI worth deploying in the first place.

Internal data platforms are becoming a core competency. The panel can explore how data engineering is borrowing from DevOps and platform engineering self-serve infrastructure, developer portals, golden paths and whether it's working in practice.

In the era of AI-driven decision-making, data quality has become more critical than ever. This session explores how poor-quality data can amplify errors at scale, leading to flawed insights and risky outcomes. It will highlight the importance of robust data governance, validation frameworks, and continuous monitoring to ensure AI systems are built on reliable, high-quality data.

What if your data pipeline could generate user intelligence before a single real user touches your product? This session introduces Synthetic Users - AI agent swarms that simulate real human behaviour at scale and the data pipeline architecture that transforms their interactions into actionable product intelligence in hours, not months.

As AI rapidly reshapes how organizations build intelligence, this session explores what it truly means to go beyond the algorithm. Drawing from real enterprise experience at one of India’s most iconic consumer brands, the talk traces the evolution from classical analytics and ML to multimodal GenAI and agent‑driven decision systems. Using the end‑to‑end product lifecycle as a lens from design and manufacturing to go‑to‑market and continuous intelligence - it demonstrates how AI delivers impact only when grounded in strong data foundations, business context, and governance. The session also reflects on the changing role of data scientists into AI orchestrators, highlighting why system thinking, judgement, and business storytelling matter more than ever in the age of AI.