Modern data platforms are evolving faster than most architectures can support.
Teams are being asked to deliver AI-ready data, real-time analytics, and continuous change, while still maintaining governance, documentation, and consistency. Most environments were not designed for this level of complexity.
In this joint session with WhereScape, ER/Studio product leaders and enterprise practitioners will break down what a modern data lifecycle looks like in practice.
You’ll learn how to establish a semantic backbone that connects data modeling, engineering, and governance, and how to extend that foundation through automated development, deployment, and ongoing evolution.
何を学ぶか
- Why traditional data lifecycles break down in modern, AI-driven environments
- How a semantic backbone supports governance, analytics, and AI initiatives
- The role of data modeling in aligning design, engineering, and governance
- What data product-driven delivery looks like in practice
- How to modernize existing platforms without disrupting production
- How to design architectures that adapt to change instead of requiring rebuilds
Why Attend
- Align data modeling with modern data engineering workflows
- Improve governance and documentation without slowing delivery
- Reduce rework caused by disconnected tools and teams
- Build a scalable foundation for AI and advanced analytics
スピーカー
Moderator
- Eric Snyder, GM, WhereScape | Former GM, ER/Studio
Panelists
- Simon Spring, Head of Product, WhereScape
- Jamie Knowles, Head of Product, ER/Studio
- Mark Kramm, Founder & Senior Enterprise Data Architect, Enterprise KnowledgePrints
- Kevin Marshbank, CEO & Principal Consultant, The Data Vault Shop
Build data architectures that scale with AI, governance, and change. Save your spot.
