In my November, December and January columns and the data quality article published in the October issue of DM Review, I laid a solid foundation for building a data warehouse. We have done a careful job of gathering all the overlapping design constraints; we have established a good set of boundaries with all the groups we interact with; we have captured a perfect subset of changed data to feed our data extraction; and we have built a powerful architecture for cleansing the data once it is under our control.

Our next big task is to divide the data into dimensions and facts. We call these designs dimensional models. Dimensions are the basic stable entities in our environment, such as customers, products, locations, marketing promotions and calendars. Facts are the numeric measurements or observations gathered by all of our transaction processing systems and other systems. End users instinctively understand the difference between dimensions and facts. When we deliver data to our business intelligence (BI) tools, we take great care to make dimensions and facts visible at the user-interface level in order to exploit the users’ understanding and familiarity with these concepts. Perhaps another way to say this is the dimensional data warehouse is the platform for BI.

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