We are at the start of a massive, AI-driven feedback loop. A loop between a universal language, Python, a universal engine, Spark, and universal storage, Open Table Formats, that will accelerate us from simple automation to fully agentic, automated data management. This session helps D&A leaders assess their strategy for navigating this disruptive transition and its opportunities and risks.

D&A leaders have a key strategic decision to make over the next few years. What does their strategic and long-term data management platform looks like and where to source it from? There are four options that this session will discuss: utilizing the all encompassing data and AI platform from their cloud service providers, extending their ISV solution providers to enable their data platform, engaging their enterprise SaaS application providers to support D&A use cases, or taking a blended approach.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

In this session, two experts will have a dynamic dialogue presenting how federation and self-service are reshaping data management. Together, they’ll debate the risks and rewards of centralization and decentralization, exploring how, through federation and self-service, organizations can balance control with empowerment, and offer an actionable strategy for navigating the evolving landscape of data management.

D&A leaders must develop DataOps as an essential practice to redefine their data management operations. This involves establishing business value before pursuing significant data engineering initiatives, and preventing duplicated efforts undertaken by different teams in managing the common metadata, security and observability of information assets within the data platforms.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

D&A leaders struggle to prioritize and justify data management spend, especially amid cloud-driven cost unpredictability. Value stream analysis links data production to direct and indirect business outcomes, driving quantifiable benefits. This session will link core research on cost management and FinOps with a means of using active metadata to measure value resulting in holistic cost optimization.

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.

Almost every GenAI use case requires organizations to extract, qualify and govern significant volumes of unstructured data. Data management leaders must deliver workflows that orchestrate entity extraction, vector data embeddings and semantic data enrichment with structured data pipelines to deliver GenAI-ready data. Join this session to learn more.

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 Lakehouse architecture which is becoming the cornerstone of modern data management initiatives. Through practical, step-by-step guidance, you’ll gain clarity on the purposes of data zones and the design principles of the Lakehouse architecture.

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.

Data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI. Data observability tools give organizations integrated visibility into the health of their data, data pipelines and data landscape. Data quality tools enable business users to manage data at its sources by setting rules and policies. Together, these tools help organizations build a strong foundation in data management for BI and AI initiatives.

Data management platforms emerge through the convergence of several individual data management capabilities. D&A leaders keen on data platform modernization should join this breakout session to learn about the dynamics of this emerging market and the benefits of reducing architectural silos to meet data demands for both current and innovative use cases.

This session will explore the challenges faced by data management leaders and offer actionable recommendations to address them. By understanding and overcoming these obstacles, leaders can optimize their data management strategies and drive more informed decision making.