Data Warehouse Build Project Checklist
This is a practical guide to building a project checklist for a data warehouse build project — a step-by-step checklist covering the project from start to finish, adapted to the realities of building a central data warehouse for analytics.
What a Project Checklist is
A project checklist is a step-by-step checklist covering the project from start to finish. For the full concept and how it works in general, see Project Management Checklist. On a data warehouse build project it plays the same role, tuned to this kind of work.
Why it matters for a Data Warehouse Build project
Data Warehouse Build projects live or die on building a central data warehouse for analytics. A well-built project checklist gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how data warehouse build projects drift into avoidable delay and cost.
What to include
- Initiation checks
- Planning checks
- Execution checks
- Closure checks
Data Warehouse Build-specific considerations
Tailor the project checklist to the risks that most often derail data warehouse build projects:
- Source-system integration
- Data modelling decisions
- Governance and access control
Example
On a real data warehouse build project, the project checklist would be shaped by building a central data warehouse for analytics. In particular, it should explicitly account for the project’s biggest risks — source-system integration, data modelling decisions, governance and access control — rather than treating them as afterthoughts.