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Data quality poses an important challenge to corporate data management and is a critical success factor for organizations. A lack of it can deteriorate business operations and impair corporate decision-making and innovation. Numerous data cleaning tools have emerged from literature and practice to address this issue by detecting data errors and deriving data quality rules for data validation. However, existing solutions are often limited in their usability and have a rather technical scope. The body of literature lacks the prescriptive knowledge necessary for designing data cleaning tools. To address this shortcoming, we applied an Action Design Research approach to develop a customized data cleaning tool for detecting data errors and deriving data quality rules within master data sets at Boehringer Ingelheim. In this paper, we summarize our lessons learned in the form of generalized design principles, which support the design of future data cleaning tools.



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