As you might recall from the discussion in the Aggregating Dimensional Data section in Chapter 1, Getting Business Information from Data, a multidimensional data model is often used to perform complex analysis of historical data. For effective analysis, data should be organized along dimensions that can be then used for building cubes.
Dimensions included in a cube define its dimensionality, or in other words, its edges. For example, a cube can be organized along the Time, Store, and Product dimensions.
A dimension in turn is defined by a set of levels, each of which represents the level of data aggregation. For example, a store dimension may aggregate data at the following levels: Region, Country, State (Province), and Store.
Aside from links to dimensions, as you'll learn in this chapter, cubes contain measures representing usually numerical data that can be aggregated. Cost, quantity, and profit are good examples of measures.