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The data quality improvement plan: deciding on choice and sequence of data quality improvements

ABSTRACT

              With the rapid growth in the amount of data generated worldwide, ensuring adequate data quality (DQ) is increasingly becoming a challenge for companies: data are, among others, required to be timely, complete, consistent, valid, and accessible. Given this multidimensionality, DQ improvements (DQIs) need to be purposefully chosen and –as there can be path dependencies– arranged in an optimal sequence. Thus, this research contributes to performing the complex multidimensional task of ensuring adequate DQ in an economically reasonable manner by providing a formal decision model for identifying an optimal data quality improvement plan (DQIP). This DQIP comprises both an economically reasonable selection and execution sequence of DQIs based on existing interrelationships between different DQ dimensions. Furthermore, a comprehensive Monte Carlo simulation provides insights in implications to put the decision model into operation. For practitioners, the decision model enables efficient allocation of resources to DQIs. The model also gives advice on how to sequence DQIs and attracts attention to the complex problem context of DQ in order to support valid managerial decisions. With the rapid growth in the amount of data generated worldwide, ensuring adequate data quality (DQ) is increasingly becoming a challenge for companies: data are, among others, required to be timely, complete, consistent, valid, and accessible. Given this multidimensionality, DQ improvements (DQIs) need to be purposefully chosen and –as there can be path dependencies– arranged in an optimal sequence. Thus, this research contributes to performing the complex multidimensional task of ensuring adequate DQ in an economically reasonable manner by providing a formal decision model for identifying an optimal data quality improvement plan (DQIP). This DQIP comprises both an economically reasonable selection and execution sequence of DQIs based on existing interrelationships between different DQ dimensions. Furthermore, a comprehensive Monte Carlo simulation provides insights in implications to put the decision model into operation. For practitioners, the decision model enables efficient allocation of resources to DQIs. The model also gives advice on how to sequence DQIs and attracts attention to the complex problem context of DQ in order to support valid managerial decisions.