Day 2 | Main Hall - Thought Leadership and Strategic Insights

Many data platforms fail to deliver impact due to fragmented data and lack of clear business alignment. This session focuses on what truly moves the needle, use-case-driven strategies, better data quality, and integrated ecosystems that deliver real outcomes.

As audio data grows exponentially, building scalable storage systems becomes critical for efficient search and retrieval. This session will explore architectures and strategies to handle high-volume audio data while ensuring performance, reliability, and cost efficiency.

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.

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.

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.

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.