If the data quality field is to make significant progress in terms of its acceptance in the business world, the
costs associated with low data quality must be made more explicit, prominent, and measurable. They most
be compared to the cost of assuring data quality, so that an optimal investment point for data quality can
be approximated. A systematic method for data quality cost benefit analysis can help companies to
determine such an optimal level of investment in data quality. Today, however, we are very far from such
a methodology to calculate the optimal level of data quality. One reason for this is the lack of overview of
all relevant data quality costs, either the costs of assuring data quality or the costs of low quality data. By
classifying data quality costs, we can open a diagnostic perspective that is both systematic and
informative. This paper has made a first step in this direction by providing an overview on such possible
cost classifications and by analyzing their mutual influence and their progression. Future research should
strive to further consolidate these classifications and validate our cost progression models, ideally through
real-life observations in the data quality field.