Introduction to Data Warehousing and Analysis Services 2005 - Professional SQL Server Analysis Services 2005 with MDX - page 4

A Closer Look at Data Warehousing
In the topic Building the Data Warehouse , Bill Inmon described the data ware-
house as "a subject oriented , integrated , non-volatile , and time variant collec-
tion of data in support of management's decisions." According to Inmon, the
subject orientation of a data warehouse differs from the operational orientation
seen in On-Line Transaction Processing (OLTP) systems; so a subject seen in
a data warehouse might relate to customers, whereas an operation in an OLTP
system might relate to a specific application like sales processing and all that
goes with it.
The word integrated means that throughout the enterprise, data points should
be defined consistently or there should be some integration methodology to
force consistency at the data warehouse level. One example would be how to
represent the entity Microsoft. If Microsoft were represented in different data-
bases as MSFT, MS, Microsoft, and MSoft, it would be difficult to meaningfully
merge these in a data warehouse. The best-case solution is to have all data-
bases in the enterprise refer to Microsoft as, say, MSFT, thereby making the
merger of this data seamless. A less desirable, but equally workable, solution
is to force all the variants into one during the process of moving data from the
operational system to the data warehouse.
A data warehouse is referred to as non-volatile since it differs from operational
systems, which are often transactional in nature and updated regularly. The
data warehouse is generally loaded at some preset interval, which may be
measured in weeks or even months. This is not to say it is never measured in
days; but even if updates do occur daily, that is still a sparse schedule com-
pared to the constant changes being made to transactional systems.
The final element in this definition regards time variance, which is a sophistic-
ated way of saying how far back the stored data in the system reaches. In the
case of operational systems, the time period is quite short, perhaps days,
weeks, or months. In the case of the warehouse, it is quite long — typically on
the order of years. This last item might strike you as fairly self-evident because
you would have a hard time analyzing business trends if your data didn't date
back further than two months. So, there you have it, the classic definition that
no good topic on data warehousing should be without.