This episode looks at excerpts from a 2011 academic paper titled "The costs of poor data quality" by Haug, Zachariassen, and van Liempd, which investigates the economic consequences of inadequate business data. The authors propose that companies should aim for an optimal data quality level rather than perfection, defining this optimum as the point where the cost of maintenance efforts balances the costs inflicted by poor quality data. To facilitate this assessment, the paper introduces two main contributions: a definition of the optimal data maintenance effort and a classification framework that categorises the costs of poor data into four types based on two dichotomies: direct versus hidden costs and operational versus strategic consequences. A case study involving an automotive spare parts manufacturer is used to illustrate the practical application of this proposed cost framework. Join the Data Innovators Exchange for free at https://www.skool.com/data-management-innovators-4116/about Sign up for the free Data Pro Newsletter at https://www.datapro.news/subscribe

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