There is more to building and maintaining a data warehouse
than selecting an OLAP server and defining a schema and
some complex queries for the warehouse. Different
architectural alternatives exist. Many organizations want to
implement an integrated enterprise warehouse that collects
information about all subjects (e.g., customers, products,
sales, assets, personnel) spanning the whole organization.
However, building an enterprise warehouse is a long and
complex process, requiring extensive business modeling, and
may take many years to succeed. Some organizations are
settling for data marts instead, which are departmental
subsets focused on selected subjects (e.g., a marketing data
mart may include customer, product, and sales information).
These data marts enable faster roll out, since they do not
require enterprise-wide consensus, but they may lead to
complex integration problems in the long run, if a complete
business model is not developed.
There is more to building and maintaining a data warehousethan selecting an OLAP server and defining a schema andsome complex queries for the warehouse. Differentarchitectural alternatives exist. Many organizations want toimplement an integrated enterprise warehouse that collectsinformation about all subjects (e.g., customers, products,sales, assets, personnel) spanning the whole organization.However, building an enterprise warehouse is a long andcomplex process, requiring extensive business modeling, andmay take many years to succeed. Some organizations aresettling for data marts instead, which are departmentalsubsets focused on selected subjects (e.g., a marketing datamart may include customer, product, and sales information).These data marts enable faster roll out, since they do notrequire enterprise-wide consensus, but they may lead tocomplex integration problems in the long run, if a completebusiness model is not developed.
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