This is the first part of a three part article by Jonathan Wu. Each section deals with the importance of data integrity and it's effect on business intelligence. Please look for the next two sections to be released shortly .
Introduction There are numerous factors that can cause a data warehouse project to fail.
From poorly gathered functional business requirements to lack of adequately skilled project team members, no factor is more devastating to the organization than the failure of the user community to accept the data warehouse because the information that it contains is of questionable quality. The purpose of this article is to address data integrity through the establishment of a control environment that mitigates and identifies data problems within a data warehouse. For the purposes of this article, data integrity is defined as information that adheres to a strict standard of value and completeness. That is, the data is both accurate and the entire population of relevant data is contained within the data warehouse. The credibility of the data warehouse solely rests with the integrity of its data.