AI systems are only as trustworthy as the data that powers them. Most organizations racing to deploy AI have inherited pipelines built for reporting – not for the precision and traceability that AI demands. The result: models that hallucinate, agents that drift, and governance teams accountable for failures they couldn't see coming.

The root causes are well understood but chronically under-addressed – source data variability, pipeline failures, schema drift, missed business rules, undetected anomalies, and the absence of observability. This session examines each failure pattern and the engineering disciplines required to address them systematically, including agentic data testing and continuous monitoring. Whether you are an engineer designing pipelines or a governance leader setting standards, you will leave with a practical framework for making reliable data the non-negotiable foundation of enterprise AI.

Takeaways

1. Recognize the data failure patterns that undermine AI initiatives.
2. Understand how to engineer AI-ready data and pipelines.
3. Learn how agentic-first approaches can scale data reliability engineering.
4. Create a shared framework that aligns technical, governance, and business teams.

Speaker: Subu Desaraju, Chief Commercial Officer, iceDQ

As the Chief Commercial Officer at iceDQ, Subu is responsible for driving the company’s revenue growth, market expansion, and strategic partnerships. He leads sales, marketing, and client engagement, ensuring that iceDQ’s unified data reliability platform delivers exceptional value to enterprises.

With a strong focus on aligning product-market fit with enterprise customer needs, Subu works closely with product and engineering teams to shape the roadmap based on client feedback and market signals. Bringing over 25 years of experience in scaling enterprise products and solutions, he plays a pivotal role in positioning iceDQ as a leader in data reliability engineering.