Data Warehouse Build Scope Statement
This is a practical guide to building a scope statement for a data warehouse build project — a definition of deliverables, boundaries and acceptance criteria, adapted to the realities of building a central data warehouse for analytics.
What a Scope Statement is
A scope statement is a definition of deliverables, boundaries and acceptance criteria. For the full concept and how it works in general, see Scope Statement. 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 scope statement 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
- Deliverables
- In scope
- Explicitly out of scope
- Acceptance criteria
- Constraints and assumptions
Data Warehouse Build-specific considerations
Tailor the scope statement 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 scope statement 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.