Data Warehousing: Get it Right

The typical causes of an unsuccessful data warehousing project are:

· Failure to use a proper data warehousing project methodology

· Ineffective project team structure

· Failure to involve and actively engage the business users

· Failure to deploy application releases (i.e., “Big Bang” vs. iterative development)

· Underestimating data cleansing efforts

· Lack of executive support and sponsorship

· Inadequate testing

· Failing to plan for ongoing support and  development of the data warehouse

Maximize the success of your data warehousing initiative using the following best practices:

· Identify and engage a strong project sponsor.

· Build out the data warehouse environment incrementally, within an overall architectural framework.

· Get access to source data early in the project.

· Establish relationships and commitment from source system owners.

· Begin prototyping in order to demonstrate analytical capabilities early and repeatedly.

· Understand the information needs of different user types.

· Don’t underestimate the extraction, transformation, and load (ETL) effort.

· Don’t underestimate the care and feeding of the data warehouse after implementation.

Steve Molsberry

Practice Director, Business Intelligence

Stonebridge

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• BI
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• Stonebridge

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