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Accelerating Generative AI Journeys using Data Products

Wednesday, July 16 11 AM ET

Key Takeaways

  • Learn how data products improve AI performance, quality, and reliability.
  • Understand how to build processes that align technical and business teams in the creation of data products
  • Discover how data products help foster trust in AI systems.

Your Presenter(s)

Sagar Paul headshot

Sagar Paul

SVP of Global Growth and Solutions at The Modern Data Company

Sagar helps enterprises with sales and go-to-market strategy for their data products. He has 30 years of experience across sales, product, and data engineering. Sagar is on a mission to bring product thinking to the world of data and drive productivity gains for technologists and businesses. Previously he was Director & Global Head of Solutions at ThoughtWorks, and Global Leader of Client Engagement at Mu Sigma.

Vikram Sharma headshot

Vikram Sharma

Global Head of Engineering for Cloud & Software at Lenovo

Vikram leads a cross-functional engineering department building and scaling Saas products. He has two decades of experience as a technology leader, across cloud engineering, data pipelines, and machine learning.

Why this matters

Generative AI initiatives often stumble—not because of the models, but because of the data. Without well-defined, high-quality, and reusable data assets, even the most advanced AI systems struggle to deliver reliable outcomes. That’s where the concept of data products comes in: a structured, collaborative approach to making data trustworthy, discoverable, and fit for purpose.

In this presentation webinar, Sagar Paul, SVP of Global Growth and Solutions at The Modern Data Company, and Vikram Sharma, Global Head of Engineering for Cloud & Software at Lenovo, will discuss how data products can accelerate your generative AI roadmap. You’ll explore what data products are, how to build them, and why they’re key to increasing both model performance and user trust. This session is designed for data and AI leaders looking to bridge the gap between infrastructure and impact.

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