Useful decision-making information can be procced through a subject-oriented data werehouse in which it will store an integrated, time-variant, and non volatile collected data. The key to find such data werehouse is to have a good data model that defnes the structure of data kept in the data werehouse. Actually the quality of correctectness and completeness of an information depens on how well the data model is constructed. One way to get good data model is by utilizing patterns. This research derived eighteen patterns f generic data model of a werehouse which can be used and chose. They are created based on analysis of data werehouse needs, existing patterns, and Kimball's case studies. To measure the level of reusability of the patterns four metrics are defined. Two metrics related to flexibility and two metrics related to comprehensibility. The test result on the patterns reusability shows that the flexibility metrics score are adequate, while the comprehensibility metrics score are almost perfect. The patterns occur in different frequencies test has involving two case studies. It concluded that patterns which are associated with te changes in dimensions, product heterogencity and multi valued attributes are seldom or almost never used. Further patterns that are used frequently are patterns related with dimension tables, especially generic dimension pattern and date pattern.