Data Warehouse Build Change Log
This is a practical guide to building a change log for a data warehouse build project — a record of change requests and their decisions, adapted to the realities of building a central data warehouse for analytics.
What a Change Log is
A change log is a record of change requests and their decisions. For the full concept and how it works in general, see Change Log. 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 change 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
- Change ID and description
- Impact assessment
- Decision and date
- Updated baselines
Data Warehouse Build-specific considerations
Tailor the change 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 change 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.