Data Warehouse Build Assumptions Log
This is a practical guide to building a assumptions log for a data warehouse build project — a register of the assumptions the plan depends on, to be validated, adapted to the realities of building a central data warehouse for analytics.
What a Assumptions Log is
A assumptions log is a register of the assumptions the plan depends on, to be validated. For the full concept and how it works in general, see Project Assumption. 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 assumptions log 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
- Assumption
- Impact if wrong
- Owner
- Validation status
Data Warehouse Build-specific considerations
Tailor the assumptions log 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 assumptions log 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.