Conformity of a Data Warehouse

In an earlier post, Data Warehouse Basics, I reprinted something that I written a while back about what makes a data warehouse. In a series of entries, I am exploring each point in further detail and see if the meaning still holds up.  In this post, we take on "Data is conformed".

Conformity in a data warehouse occurs means essentially two things, conformed dimensions and conformed facts.  Conformity allows data warehouses to be distributed across the enterprise and unanticipated new sources to be added.  Conformity assures that definitions and uses are consistent throughout the warehouse.

Conformed dimensions assure that users will be able to drill across data sources and arrive at conclusions that were not possible without a data warehouse.  This is simply assuring that customer id 1234 for John L Smith in one fact table is the same customer id 1234/John L Smith in another fact table.  This example certainly seems obvious.  However, determining conformed dimensions is not always easy. 

Two departments may view the information very differently.  This is often the case between finance and sales or marketing.  Finance typically views things as they are tracked in the accounting system.  While marketing or sales may view them entirely different.  For example, when tracking sales by location hierarchy, finance may have a hierarchy that is based on legal entities.  However, marketing may not make any distinction between these legal entities and may be more concerned with brand or geography.  These cases may present challenges to modelers and to arriving at conformed dimensions.

Conformed facts ensure that terminology for measures in one fact table mean the same in another.  Revenue always means revenue, right?  Well, again finance's definition of revenue may be different than marketing's.  Once again, we have to ensure that our terminology is consistent.  Doing so, will allow us to derive meaningful results across fact tables. 

The concepts of conformed dimensions and facts are the cornerstone concepts to dimensional methodology. Without them, the data warehouse becomes a database of siloed data marts. 

I extend the concept of conformity to include data naming standards.  Consistent naming schemes make the database understandable and usable .  So perhaps, we should call this one conformity and consiststency of a data warehouse.