Building a data platform is not a project with a launch date. The approach here is four phases, each with explicit exit criteria. You do not move to the next phase until the current one is proven in production with a real domain team using the data.
Key Takeaways
- 01 Start with a narrow pilot domain to prove value.
- 02 Automate infrastructure (IaC) from Day 1.
- 03 Standardize templates early to enable self-service.
- 04 Iterate based on real domain feedback, not theoretical needs.
Checklist
- □ Foundation (IAM, Networking, CI/CD) established.
- □ First domain pilot successful and in production.
- □ Self-service templates for ingestion and transformation ready.
- □ Federated governance board established.
Phase 1: Foundation
Establishing the "paved road" and secure landing zone.
- ✓ Outcomes: Secure cloud environment, baseline observability.
- ✓ Deliverables: IaC repos, IAM roles, CI/CD for infra.
- ! Risks: Over-engineering the foundation without a use case.
- → Exit Criteria: Can deploy a new data project in minutes.
Phase 2: First domain
Proving the architecture with a real business vertical.
- ✓ Outcomes: One end-to-end flow from source to Gold.
- ✓ Deliverables: Ingestion pipeline, Silver/Gold tables, BI Dashboard.
- ! Risks: Scope creep of the pilot domain.
- → Exit Criteria: Pilot domain team successfully uses the data.
Phase 3: Scale domains
Moving from a central push to a domain pull.
- ✓ Outcomes: Multiple domains operating autonomously.
- ✓ Deliverables: Self-service portal, standard governance tags.
- ! Risks: Inconsistent data products across domains.
- → Exit Criteria: >3 domains producing data products.
Phase 4: Optimize
Advanced features and performance tuning.
- ✓ Outcomes: Automated lineage, quality-aware routing.
- ✓ Deliverables: FinOps dashboards, ML-ready extensions.
- ! Risks: Diminishing returns on optimization efforts.
- → Exit Criteria: Operational costs plateau while data volume grows.