Zapping Data Anomalies
Given the importance of an enterprise's data warehouse, the keeper of an organization's corporate memory, it's troubling to read reports of data warehousing failure rates of between 20 percent and 75 percent (“The Application Revolution,” by Jim D'Addario, posted on The Data Warehousing Institute Web site, December 2006). According to experts, data quality issues account for up to 70 percent of those data warehouse failures.
Take It With You
It is an overwhelming challenge to address all data quality issues for a large data warehouse as it is being built, and it's even more challenging to address in an existing warehouse. What's needed is a way to prevent, identify and monitor anomalies – which are irregularities or deviations from the norm – and to improve the quality of, and confidence in, data content.
This document outlines a process that provides a straightforward method for preventing and protecting a data warehouse from data anomalies that can lead to bad decisions, downtime and lost productivity.