A data model is one of the building blocks of the data warehouse. Starting with the logical data model, the data warehouse data model is developed; and the viability of the data warehouse is dependent on the design of this model. A question that has occasionally been asked of me is, "How do I know if my data warehouse data model is good?" Detecting a poor design is much easier than confirming a good design. A poor design can often be determined through a violation of data warehouse data modeling rules. A good design cannot be confirmed without an examination of the content. This column addresses two important characteristics of a good data warehouse data model--consistency with the logical model and relevance. Other characteristics will be addressed in future columns. A data warehouse data model is a particular type of data model. As such, it must observe sound data modeling practices pertaining to entity and attribute names, entity and attribute definitions, diagram conventions, etc. This column focuses on the characteristics that are essential in good data warehouse data models.

The first important characteristic of the data warehouse data model is that it must be semantically consistent with the logical model.1 The logical model describes the business entities and the relationships among them, independent of any system implementation. The consistency requirement applies at the entity level, the attribute level and the relationship level.

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