Data warehouse operational
processes are costly and critical for the success of a data warehouse project,
and their design and implementation has been characterized as a labor-intensive
and lengthy procedure (Demarest, 1999; Shilakes & Tylman, 1998; Vassiliadis,
2000). Several reports mention that most of these processes are constructed through
Data Warehouse Refreshment
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an in-house development procedure that can consume up to 70% of the resources
for a data warehouse project (Giga, 2002; Strange, 2002). Complementary reports
(Friedman, 2002; Strange, 2002a) address the factors that influence the cost of its
implementation and support: (a) staff (development, application support teams,
on-going support and maintenance, and operations); (b) computing resources (dedicated
ETL server, disk storage for ???temporary??? or staging files, CPU use of servers
hosting source data, and annual maintenance and support); (c) tools acquisition
(annual maintenance support and training); (d) latency (timeliness of the delivery
of data to the target environment impacts the overall effectiveness of BI); and (e)
quality (flaws in data distilled from ETL processes can severely limit BI adoption).
Each of these components directly influences the total cost of ownership of a data
warehouse implementation and operation.
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