Believe it or not, most every company has some bad data somewhere in their databases. They probably have a report that has been ‘vetted’ as being absolutely correct, yet presents an incorrect picture of the truth. As a former developer for a major transportation company, and trainer/consultant for an international software company, I’ve seen some bad stuff out there. And these weren’t ‘Mom & Pop’ shops, either. Many were Fortune 500 companies (5 in the Fortune 25), federal departments and agencies, and the military. The wide-spread existence of ‘dirty data’ wasn’t too obvious to me at first. But, as time went by, I realized that everyone had bad data. Everyone.

True Stories

When trying to build an OLAP cube from a bank’s data on car loans, I learned first-hand that there was more than one way to spell ‘Chevrolet’. The database held values like ‘Chevrolt’, ‘Cheverolay’, ‘chvy’, ‘Chevolet’, and more.