Data Warehouse Build Lessons Learned
This is a practical guide to building a lessons learned for a data warehouse build project — what went well and badly, captured to improve future projects, adapted to the realities of building a central data warehouse for analytics.
What a Lessons Learned is
A lessons learned is what went well and badly, captured to improve future projects. For the full concept and how it works in general, see Lessons Learned. 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 lessons learned 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
- What happened
- Impact
- Root cause
- Recommendation
Data Warehouse Build-specific considerations
Tailor the lessons learned 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 lessons learned 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.