AI adoption has accelerated faster than enterprise data architecture. While most organizations are experimenting with GenAI and exploring agentic AI, many are discovering that their underlying data environments were never designed to support AI operating across domains, systems, and workflows.
In this TDWI-hosted expert panel, Deanne Larson (TDWI), Dave Mariani (AtScale), Suda Srinivasan (Google Cloud), and Mike Frasca (Reltio) examine why data silos persist despite years of modernization, and what organizations must do to enable interoperability for both analytics and AI.
Drawing on real-world experience, the discussion explores how data silos have evolved beyond just physical fragmentation to include semantic inconsistency and platform-level disconnects. These challenges become more visible and more costly as AI systems require consistent context, trusted definitions, and coordinated access across the enterprise.
Watch the on-demand session to learn how organizations are breaking down silos and building a more interoperable data foundation for AI.
What You’ll Learn
- Why AI adoption is exposing gaps in enterprise data architecture
- How data silos now span domains, semantics, and platforms
- Why semantic inconsistency is a primary driver of unreliable AI outputs
- How data virtualization and federation help, and where they fall short
- Why semantic layers are critical for aligning BI and AI on shared definitions
- How metadata, master data, and governance support trusted AI at scale
- What architecture patterns best support cross-domain AI inference
Why Watch
As organizations move from BI-driven insights to AI-driven decisions, the cost of fragmentation increases. Data silos introduce inconsistency that AI systems cannot detect or correct on their own.
Without shared semantics, governed context, and interoperable access, AI systems operate on incomplete or conflicting information, leading to outputs that are difficult to trust and even harder to operationalize.
This webinar focuses on what organizations are doing in practice to address these challenges. It provides a clear, experience-driven perspective on how to move beyond siloed architectures and build a data foundation that supports both analytics and AI.
Whether you’re responsible for data architecture, AI strategy, governance, or analytics delivery, this session offers a practical framework for improving AI readiness without adding complexity or duplication.
