Many GenAI initiatives struggle not because of models, but because underlying data architectures weren’t designed for consistent access, interoperability, or growth as AI usage expands.
To win, your GenAI solutions need a data architecture that is intentionally designed for access, consistency, and scale
When data architecture isn’t designed with GenAI in mind, teams face:
- Fragmented storage and access: Data is spread across systems without clear retrieval patterns for GenAI workloads.
- Inconsistent standards and interfaces: Teams struggle to integrate data reliably across tools and platforms.
- Scalability bottlenecks: Early designs fail under increased data volume, usage, or performance demands.
Weak data architectures will constrain GenAI capabilities, increase operational friction, and limit future growth.
In this hands-on workshop, your team designs a practical data architecture that supports GenAI access patterns, interoperability, and scalable performance.
- Design data architectures specifically for GenAI workloads.
- Map storage and retrieval layers across relevant systems.
- Define data standards and interfaces for consistent handling.
- Support interoperability across platforms and tools.
- Plan for scalability and performance as GenAI usage grows.
Designing Data Architecture for GenAI
Mapping Storage and Retrieval Layers
Defining Data Standards and Interfaces
Supporting Interoperability Across Systems
Planning for Scalability and Performance
- Design a GenAI-ready data architecture aligned to real use cases.
- Clarify how data should be stored and accessed across systems.
- Establish standards that enable consistent data handling.
- Improve data sharing across platforms and teams.
- Anticipate scalability and performance needs before they become blockers.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
Architecture diagrams, data platform references, and guided design frameworks