What is the desirable “upper bound” to data quality?
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Tom Breur
February 2010 Introduction A common argument holds that every company has some “ideal” or “optimal” level of data quality that they should pursue. The underlying assumption is that trying to drive down defect rates further is prohibitively costly. This would become unsustainable because you would need to spend more money than is justified by the reduction in losses. That all sounds very sensible. Another line of reasoning that we frequently encounter is that data quality levels need to be tuned to their intended use. Extensive cleaning operations that make sense for the census, could be costly overkill for a “quick and dirty” survey. Herzog et al (2007): “How accurate do our data need to be?” |
