A Framework For The Classification Of Data Quality Costs And An Analysis Of Their Progression

Many information quality initiatives and projects need to demonstrate the potential benefits of their IQrelated activities already in their planning stage. In doing so, practitioners rely on cost estimates based on current non-quality data effects (that are then compared to data quality improvement costs). In producing such estimates on costs caused by low quality data, it is difficult to identify all potential negative monetary effects that are the result of low quality data (as well as all possible costs associated with assuring high quality data and their progression). Because of this, this article reviews and categorizes the potential costs associated with low quality data and examines their progression. This can help practitioners to identify cost saving potentials and argue a more convincing business case of their data quality imitative. For researchers, the proposed classification framework and the cost progression analyses can be helpful to develop quantifiable measures of data quality costs and to prepare – subsequently – benchmarking studies, comparing different cost levels in different organizations. Thus, this paper contributes elements of a future cost-benefit analysis method for data quality investments.