Amy Raygada | Data Track | Compass AI & Data Summit 2025
summary
Implemented a Data Mesh in a 1,000‑person e‑commerce, migrating from on‑prem to GCP, creating domain teams, data sharing and self‑service APIs, and enforcing data contracts and a data catalog to harmonize KPIs (five conflicting lead definitions reduced to one). Adoption climbs slowly — about 30% adoption in 18 months — but wins include executive allies and automation that deprecates unused projects, cutting costs (roughly 200,000 CHF saved in one year) and proving the product‑thinking and governance changes can work.
AI-written summary · about
chapters (11)
- 0:00Introduction
- 1:13Data mesh beyond technical details
- 2:38What data mesh is
- 10:29Data mesh use case
- 18:55Audience poll on data mesh
- 20:32Data mesh challenges
- 26:53Fixing lead metric inconsistency
- 29:42Driving data product adoption
- 34:26Key challenges and strategies
- 36:30Closing remarks
- 36:52Q&A
related talks
38mLesson learned implementing a large-scale data mesh transformation
44mData Mesh: Lessons from the trenches
31mData Mesh In Practice – How To Set Up A Data Driven Organization
43mWhen design meets data: Using Data Mesh for a consumer and business-driven data strategy
38mData Mesh, Now
51mData Mesh – Successful with People, Process, and Technology
20mEmbracing Data Mesh: A Modern Strategy for Scalable Data Management
23mHow To “Data Mesh”: What You Need To Know To Make It Real