Dirty data is expensive. A silent productivity killer, it triggers ripple effects that chip away at cost effectiveness on all levels. In a single year, the US economy suffers losses approaching one trillion dollars on poor quality data in the category of customer data alone.
But it's not just customer data. Poor quality data can be found in financial, human resource, decision support and other sales and marketing data. Each user or business action that touches an erronious record in a database uses additional resources to cope so that business can proceed. The costs stack up and businesses tend to let themselve be exposed. Why? because it's difficult, and often a time-intensive job to detect, and act appropriately on each erronious record in a database.
Your company has dirty data. You cope. You hardly notice, but you follow business rules to make the necessary adjustments along the way. Rarely do you fix it at the source. Why not let a program—a tool—find the easy ones, and for the hard ones you teach it the business rules that apply, once and for all. Let the tool surface the problematic records and auto-correct them or suggest fixes for you to approve. Build that tool.
