Design Data Architectures That Don’t Break Under Change
Data architectures rarely fail on day one. They fail when they can’t adapt.
As source systems evolve, business logic shifts, and reporting demands grow, many architectures become fragile—requiring constant rework, slowing delivery, and increasing long-term risk.
In this recorded panel, practitioners break down what it actually takes to design architectures that evolve with the business. You’ll get a practical look at how approaches like Data Vault, dimensional modeling, and hybrid patterns perform in real environments—and what tradeoffs matter when change is constant.
What You’ll Learn
- What makes a data architecture adaptable in real-world environments
- Where Data Vault, dimensional, and other modeling approaches fit best
- How to design for change without creating unnecessary complexity
- How to reduce rework while supporting governance, lineage, and auditability
- What teams should prioritize today to build architectures that scale and evolve
Speakers
Moderator
- Simon Spring – Head of Product, WhereScape
Panelists
- Kevin Marshbank – CEO & Principal Consultant, The Data Vault Shop
- Frank Martens – Data Automation Lead, Quest for Knowledge
- Joe Barter, Business Data Analyst, Business Thinking
- Andrew Milner, CTO, Slipstream Data
