A data warehouse dimensional model works well to implement a data warehouse (See Kimball & Ross, 2002). It contains a fact table in which each row contains facts about an individual or event, as well as dimensions (attributes). To conserve space in fact tables, which runs in the millions of rows, the dimensions are coded and linked to dimension tables. The single central fact table is surrounded by many smaller dimension tables leads to the description of this type of data warehouse as a ‘Star Schema’. The fact table can be fine grained meaning it holds a detailed, atomic fact or coarsely grained meaning it holds summarized facts. Finely grained fact tables can be rolled up into coarsely grained tables. The dimensional model is an efficient way to store the data versus a standard database which is optimized for transactional processing. The facts can be thought of in statistical terms as quantitative dependent variables while the dimensions are the categorical independent variables. This conceptualization of facts and dimensions it easy to build systems that business analyst can use to create useful reports.