A conformed dimension is a dimension that has exactly the same meaning and content when being referred from different fact tables. A conformed dimension can refer to multiple tables in multiple data marts within the same organization. For two dimension tables to be considered as conformed, they must either be identical or one must be a subset of another. There cannot be any other type of difference between the two tables. For example, two dimension tables that are exactly the same except for the primary key are not considered conformed dimensions.
Why is conformed dimension important? This goes back to the definition of data warehouse being "integrated." Integrated means that even if a particular entity had different meanings and different attributes in the source systems, there must be a single version of this entity once the data flows into the data warehouse.
The time dimension is a common conformed dimension in an organization. Usually the only rule to consider with the time dimension is whether there is a fiscal year in addition to the calendar year and the definition of a week. Fortunately, both are relatively easy to resolve. In the case of fiscal vs. calendar year, one may go with either fiscal or calendar, or an alternative is to have two separate conformed dimensions, one for fiscal year and one for calendar year. The definition of a week is also something that can be different in large organizations: Finance may use Saturday to Friday, while marketing may use Sunday to Saturday. In this case, we should decide on a definition and move on. The nice thing about the time dimension is once these rules are set, the values in the dimension table will never change. For example, October 16th will never become the 15th day in October.
Not all conformed dimensions are as easy to produce as the time dimension. An example is the customer dimension. In any organization with some history, there is a high likelihood that different customer databases exist in different parts of the organization. To achieve a conformed customer dimension means those data must be compared against each other, rules must be set, and data must be cleansed. In addition, when we are doing incremental data loads into the data warehouse, we'll need to apply the same rules to the new values to make sure we are only adding truly new customers to the customer dimension.
Building a conformed dimension also part of the process in master data management, or MDM. In MDM, one must not only make sure the master data dimensions are conformed, but that conformity needs to be brought back to the source systems.