The Ph.D. thesis of Daniel Fasel addresses the challenge that numeric values from a data warehouse can be difficult for business users to interpret and may lead to misinterpretations. To better understand numeric values, business users may require an interpretation of these values in meaningful, non-numeric terms. However, if the transition between non-numeric terms is crisp, true values cannot be measured, and a smooth transition between classes may no longer be possible. This chapter introduces a fuzzy classification-based approach for data warehouses and a modeling approach for integrating fuzzy linguistic variables into a meta-table structure. This structure allows fuzzy concepts to be incorporated into the dimensions and facts of an existing classical data warehouse without affecting its core, enabling simultaneous fuzzy and crisp analysis. A case study of a movie rental company underlines and exemplifies the proposed approach.