Data Warehouse Build Quality Plan
This is a practical guide to building a quality plan for a data warehouse build project — how quality will be planned, assured and controlled, adapted to the realities of building a central data warehouse for analytics.
What a Quality Plan is
A quality plan is how quality will be planned, assured and controlled. For the full concept and how it works in general, see Quality Management Plan. 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 quality plan 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
- Quality standards
- Quality metrics
- QA activities
- QC / inspection approach
Data Warehouse Build-specific considerations
Tailor the quality plan 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 quality plan 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.