Methodology for linked enterprise data quality assessment through information visualizations

•Planning the data quality assessment activity.•Extracting requirements for the data quality management.•Setting priorities to expedite the adaptation.•Identifying dimensions and metrics to ease the understanding.•Visualizing the dimensions and metrics to assess the overall data quality. Today's development environments in the manufacturing industry require different development tools to work together. These complex environments are highly heterogeneous and constantly changing, and the development tools are producing a huge amount of data. As a result, these development environments must overcome a significant problem related to data integration. In this paper, we examine a case study from the automotive industry using the linked enterprise data approach to integrate data from different development tools. The study explains and applies a data quality assessment methodology as a post-integration phase for linked enterprise data. In this study, important data quality dimensions from the literature are merged with empirical rules that have been defined by Scania CV AB employees. As a result, a comprehensive methodology is developed and introduced to assess these data quality dimensions. This paper presents the methodology, which aims to develop a data quality assessment tool—a dashboard—in addition to policies and protocols to manage data quality. Moreover, the proposed methodology includes systematic guidelines for planning the data quality assessment activity, extracting requirements for the data quality management, setting priorities to expedite the adaptation, identifying dimensions and metrics to ease the understanding, and visualizing these dimensions and metrics to assess the overall data quality.