The mission of the data warehouse in the Corporate Information Factory is clear. It serves as the repository of integrated, cleansed, historical snapshots of detailed data for use in strategic decision-making applications. To fulfill its mission, the design of the warehouse should meet these requirements:
Similar to the Saturday Night Live cone-heads, data warehouses "consume mass quantities" of data. In response, data warehouse designers need to optimize/shorten the process of getting data in. They accomplish this via the data model. Many data warehouse designers use the normalization techniques developed by Chris Date and Ted Codd in the '80s for their data models. Third normal form is the level of normalization most common for warehouses. That is, "every attribute is dependent upon the key, the whole key and nothing but the key" (so help me Codd). This level of normalization goes a long way in satisfying the four design requirements; however, until recently, it wasn't practical for supporting data analysis.
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