The human eye and mind recognize that the differences between the two sets of data records are probably the result of mistakes or inconsistencies in data entry. Weeding out and fixing or discarding inconsistent, incorrect or incomplete data is what's called data scrubbing or cleansing.

"Dirty data" has been a problem for as long as there have been computers -- or maybe for as long as people have attempted to gather and analyze information. It's a large part of the "garbage in" that can result in the worthless "garbage out" of a computing process.

The issue of data hygiene has become increasingly important as more and more organizations implement complex customer relationship management (CRM) or knowledge management (KM) systems and build data warehouses that merge information from many different sources.

Without data cleansing, the IT staffs of those companies face the unappetizing prospect of merging corrupt or incomplete bits of data from multiple databases. A single piece of dirty data might seem like a trivial problem, but if you multiply that "trivial" problem by thousands or millions of pieces of erroneous, duplicated or inconsistent data, it becomes a prescription for chaos.

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