Conformed dimensions

Conformed dimensions

Conformed dimensions can be used to analyze facts from two or more data marts. Suppose you have a “shipping” data mart (telling you what you’ve shipped to whom and when) and a “sales” data mart (telling you who has purchased what and when). Both marts require a “customer” dimension and a “time” dimension. If they’re the same dimension, then you have conforming dimensions, allowing you to extract and manipulate facts relating to a particular customer from both marts, answering questions such as whether late shipments have affected sales to that customer.

Suppose now that you add a “marketing” data mart to help you analyze product promotions. Again, with conformed customer and time dimensions, you’re able to analyze the effects of a particular product promotion on sales. (Analyzing facts from more than one fact table in this way is termed “drilling across.” My previous article, “Thinking dimensionally aids business intelligence design and use,” explains the function of facts and dimensions.)

As this example shows, the very same conformed dimensions—in this case, time and customer dimensions—have meaning in the context of three independentlydevelopeddata marts. These dimensions become enterprise property and can be used later in other marts as you evolve the enterprise data warehouse.