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It can represent a view of the business at a specific point in time or be a historical record of the business over some period of time. Derived data is traditionally used for data analysis and decision making. Data analysts seldom need large volumes of detailed data; rather they need summaries that are much easier for manipulation and use. Manipulating large volumes of atomic data can also require tremendous processing resources. Considering the requirements for improved query processing capability, an efficient approach is to precalculate derived data elements and summarize the detailed data to better meet user requirements.

In Figure 12, Product ID, Description, and Picture are attributes of the PRODUCT entity. For clarification, attribute naming conventions are very important. An attribute name should be unique in an entity and should be self-explanatory. For example, simply saying date1 or date2 is not allowed, we must clearly define each. As examples, they could be defined as the order date and delivery date. 38 Data Modeling Techniques for Data Warehousing When an instance has no value for an attribute, the minimum cardinality of the attribute is zero, which means either nullable or optional .

The logical data modeling phase is primarily enterprisewide in scope and generic to all applications located below it in the pyramid. The logical data model typically consists of several hundred entities. It is a complete model that is in third normal form and contains the identification and definition of all entities, relationships, and attributes. For further specific analysis, the entities of the logical data model are sometimes partitioned into views by subject areas or by applications. Some methodologies divide this phase into two phases: • • Generic logical data model - the enterprise level Logical application model - application level of data view The physical data design applies physical constraints, such as space, performance, and the physical distribution of data.

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