What needed to be understood.
The U.S. and German files used different formats and source structures. The final model needed consistent InfoObjects, transformations, time characteristics, currencies, master-data lookups, persistent storage, and a reporting layer.
How the analysis was built.
- Created custom InfoObjects, key figures, dimensions, and source-to-target mappings.
- Loaded material master data and transaction sources.
- Built an advanced DataStore Object and grouped fields by order, customer, product, time, and measures.
- Created separate transformations and DTPs for German and U.S. sources.
- Calculated Net Sales during loading and used a time-dependent customer lookup for Sales Organization.
- Built and activated a CompositeProvider with navigation attributes for analysis.
What the evidence showed.
- Both source files loaded successfully into one harmonized sales structure.
- The aDSO provided detailed persistent storage, while the CompositeProvider served as the virtualized reporting layer.
- The exercise connected operational data acquisition, ETL, dimensional modeling, and analytics-ready reporting in one cumulative build.
What should happen next.
- Standardize naming, field definitions, and validation rules before multinational loads.
- Add automated reconciliation checks for record counts, totals, currency, and duplicate DTP executions.
- Document transformations and ownership so reporting teams can trust the semantic layer.
What this project does—and does not—prove.
This was a controlled Global Bike academic environment. The public case study describes the architecture and work performed without redistributing copyrighted course manuals.