01 · The challenge
Same customer, three definitions.
Different systems can assign, update, and interpret the same business entities differently. The result is fragile joins, constant reconciliation work, and reports that don’t agree with each other.
02 · What I did
What I owned and built.
- Reviewed source-to-target integration paths and the dependencies between operational platforms and analytical layers.
- Defined a consolidation approach built on canonical identifiers, mapping rules, and controlled transformations.
- Applied data engineering patterns that validate source consistency and surface cross-system exceptions.
- Connected master data decisions to downstream pipeline design, data quality, and reporting requirements.
03 · Architecture
How it fits together.
ERP · business applications
Controlled transformations
04 · Key decisions
The judgment behind the build.
Why canonical identifiers first?
Every downstream join depends on agreeing what an entity is. Settling the identifier is what makes everything after it stable.
Why surface exceptions instead of hiding them?
Silent fixes bury ownership problems. Visible exceptions give the business a list it can actually act on.
Why go domain by domain?
Each domain has its own owners and maturity. Rolling out one at a time keeps change manageable and lets wins build momentum.
05 · Impact
What changed because of the work.
- A clearer architecture for cutting down inconsistent entity definitions across systems.
- Data quality and ownership issues show up before they become downstream failures.
06 · What’s next
Where it goes from here.
Confirm implementation status domain by domain, and measure reconciliation improvements before publishing metrics.
This case study is a sanitized reconstruction. Organization names, proprietary code, data, and internal schemas are intentionally left out.