We’ve all heard of the many horrors of poor data quality. Companies with millions of records with “(000)000-0000” as customer contact numbers, “99/99/99” as the date of purchase, 12 different gender values, shipping addresses with no state information, etc. The cost of “dirty data” to enterprises and organizations is real. For example, the U.S. Postal … Continue reading Data Quality – A Simple Six-Step Process