Introduction to Data Warehousing and Analysis Services 2005 - Professional SQL Server Analysis Services 2005 with MDX - page 17

Inmon Versus Kimball Different Approaches
In data warehousing there are two commonly acknowledged approaches
to building a decision support infrastructure, and both can be implemen-
ted using the tools available in SQL Server 2005 with Analysis Services
2005. It is worth understanding these two approaches and the often-
cited difference of views that result. These views are expressed most
overtly in two seminal works: The Data Warehouse Lifecycle Toolkit by
Ralph Kimball, Laura Reeves, Margy Ross, and Warren Thornthwaite, and
Corporate Information Factory by Bill Inmon, Claudia Imhoff, and Ryan
Sousa.
Kimball identified early on the problem of the stovepipe. A stovepipe is
what you get when several independent systems in the enterprise go
about identifying and storing data in different ways. Trying to connect
these systems or use their data in a warehouse results in something re-
sembling a Rube-Goldberg device. To address this problem, Kimball ad-
vocates the use of conformed dimensions. Conformed refers to the idea
that dimensions of interest — sales, for example — should have the same
attributes and rollups (covered in the "Aggregations" section earlier in
this chapter) in one data mart as another. Or at least one should be a
subset of the other. In this way, a warehouse can be formed from data
marts. The real gist of Kimball's approach is that the data warehouse
contains dimensional databases for ease of analysis and that the user
queries the warehouse directly.
The Inmon approach has the warehouse laid out in third normal form (not dimen-
sional) and the users query data marts, not the warehouse. In this approach the
data marts are dimensional in nature. However, they may or may not have con-
formed dimensions in the sense Kimball talks about.
Happily it is not necessary to become a card-carrying member of either
school of thought in order to do work in this field. In fact, this topic is
not strictly aligned to either approach. What you will find as you work
through this topic is that by using the product in the ways in which it was
meant to be used and are shown here, certain best practices and effect-
ive methodologies will naturally emerge.