Data Quality: revisiting dimensions towards new framework development

Abstract

In today’s information-driven world, accessible, reliable, and accurate data is crucial for informed decision-making and effective operations across various domains. Within this context, ensuring Data Quality is crucial to maximizing the added value of the information shared. However, assessing Data Quality presents challenges due to several aspects, such as the lack of consensus on which quality dimensions constitute it, or the absence of systematic methodologies for the development of quality frameworks. This study addresses these issues by identifying commonly used quality dimensions and proposing a structured approach to facilitate and foment effective quality assessment and assurance mechanisms. To that end, we conducted a comprehensive literature review regarding Data Quality dimensions and aggregated the identified ones into an intelligible structure. Through this process, 66 quality dimensions were identified and a coherent arrangement that allows for the proper development of quality frameworks was proposed. The results showcase a robust and adaptable structure offering valuable insights for practitioners and researchers. This contribution significantly enhances the overall understanding and application of data quality dimensions, thereby advancing the development of effective data quality frameworks.