In the realm of data management, much has been written about data cleansing – a crucial process to ensure that data is accurate, consistent, and usable. Companies invest heavily in cleaning up data to drive better analytics and decision-making. However, an equally important but often overlooked aspect is metadata cleansing, especially in complex data warehouses like SAP BW (Business Warehouse) systems.
While data cleansing focuses on the quality of the data itself, metadata cleansing is about ensuring that the underlying data structures – the building blocks of the data warehouse – are well-organized, consistent, and free from costly duplication. In data warehouses, these building blocks refer to dimensions, which, together with facts (events), help in constructing data marts. Ensuring the cleanliness of these dimensions is crucial for the integrity and performance of the entire system.