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Consider a BI model for a chain of 300 retail stores. One data element being modeled could be
actual sales (the “facts”). The first dimension may be the company‟s merchandise items; second
dimension may represent different points in time; and the third dimension may be the store locations.
This cube is illustrated in Exhibit 20.3. Each of these dimensions is represented at the lowest detail
level but could also be aggregated (items to product lines or items to department, store locations to
districts, time to weeks/months/years). An executive might examine the men‟s department sales. She
might then probe to learn what product lines of items sold better than others. After finding an
underperforming product line, she may check how the product line did in different districts of stores.
She might drill down, looking at individual items in individual stores, and compare their performance
to that of a prior week or year. This process is like taking the Rubik‟s cube and continually rotating the
levels, looking at each of the cube‟s faces. Each face represents data for a piece of merchandise for a
store for a period of time. That is why this process is referred to as “slice and dice.” You can slice and
turn the data any which way you desire. The data can also be viewed and sorted in a tabular or
graphical mode. The same theory applies, whether the database contains retailing data, stock market
data, or accounting data.
OLAP features are beginning to be incorporated into the personal productivity products. Database
tools can do cross-tab queries (aggregating data based on values in fields), and SQL, the language
used to extract data from relational databases, has an OLAP function. Spreadsheet software is
beginning to incorporate pivot tables (a blend of cross-tab functionality and OLAP aggregation) into
their native offerings. Low-end data-analysis tools are rapidly becoming a commodity.
Exhibits 20.4 and 20.5 show examples of a decision support system‟s output. The output is from a
demo from Information Builders that was also used in Exhibit 20.2, a sample dashboard.
Exhibit 20.4 illustrates how an analyst might customize a view of the data. The user picks from the
drop-down lists at the top of the screen to slice and dice the cube. The output can be viewed as a Web
page (HTML), a graph (as illustrated), a spreadsheet, or a pdf file.
Exhibit 20.4 Custom view of data from Information Builders’ online demo.
Source: Information Builders.