In SAP BW (Business Warehouse), data extracted from various source systems often requires transformation before it can be used effectively for reporting and analysis. Two critical elements of this transformation process are data mapping and conversion rules. These ensure that data is accurately aligned, cleansed, and formatted to meet business requirements.
This article explores the concepts of data mapping and conversion rules within SAP BW, explaining their importance, implementation, and best practices for effective data transformation.
Data mapping is the process of linking fields from the source system (DataSource) to target objects in SAP BW, such as InfoObjects or InfoProviders. This mapping establishes a relationship between the incoming data and the data warehouse structure.
For example, a source system field like "Customer ID" needs to be mapped to the corresponding characteristic InfoObject "Customer" in BW.
Conversion rules define the logic for transforming data values during the loading process. They enable modifications such as data type changes, unit conversions, value translations, or complex calculations.
Conversion rules operate during the transformation step between the PSA (Persistent Staging Area) and target InfoProviders or between different InfoProviders.
These are fields from the DataSource or source system, containing raw data to be loaded.
These are the corresponding fields or characteristics/key figures in BW where the data will reside.
The core logic that governs how source fields are converted and mapped to targets.
During the data load process:
Proper mapping ensures that every source field is accounted for, and conversion rules ensure data integrity and business relevance.
Data mapping and conversion rules are fundamental in SAP BW’s ETL (Extract, Transform, Load) process. They bridge the gap between raw source data and meaningful business information by ensuring accurate, consistent, and business-ready data loads. Mastery of these concepts helps SAP BW professionals maintain data quality and deliver reliable analytics for decision-making.