Ramke Ramakrishnan

This session details how Agentic AI will impact existing data management architecture and technologies, which new use cases it enables in data management and engineering, and which skills will be needed or become obsolete. We’ll also cover how to prepare budgets, teams, and operating models for these changes. These are now valid, frequently debated questions as Agentic AI evolves.

In this session, you’ll explore a reference architecture that serves as a blueprint for future-proofing your data and analytics environment. Through practical, step-by-step guidance, you’ll learn how to align your technology stack with business objectives — whether you're modernizing an existing architecture or building one from the ground up.

This workshop will cover the end-to-end process of managing a data product, from ideation to launch. It will include product roadmap development, stakeholder management and go-to-market strategies.

Urgent investments in AI-ready data and operational use cases have put the spotlight on foundational data management. The Data Fabric has emerged as a long-term data management architecture that you should now pursue for sustained data, analytics, and AI success. This session will help participants understand what data fabrics are and their implications for your data architecture. It will also address how to build and where to buy data fabrics.

Over the next five years, data ecosystems with data fabric features and generative AI will shape Data Management (DM). DM must broaden its scope to support AI initiatives. You will learn which components of the data management reference architecture are impacted and how to support AI initiatives and the relationship between an expanded DM architecture and AI-ready data.

This session details how Agentic AI will impact existing data management architecture and technologies, which new use cases it enables in data management and engineering, and which skills will be needed or become obsolete. We’ll also cover how to prepare budgets, teams, and operating models for these changes. These are now valid, frequently debated questions as Agentic AI evolves.

This session details how Agentic AI will impact existing data management architecture and technologies, which new use cases it enables in data management and engineering, and which skills will be needed or become obsolete. We’ll also cover how to prepare budgets, teams, and operating models for these changes. These are now valid, frequently debated questions as Agentic AI evolves.

In this session, you’ll explore a reference architecture that serves as a blueprint for future-proofing your data and analytics environment. Through practical, step-by-step guidance, you’ll learn how to align your technology stack with business objectives — whether you're modernizing an existing architecture or building one from the ground up.