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.

当今制造业中的开发环境需要不同的开发工具才能协同工作。这些复杂的环境高度异构且不断变化，开发工具正在生成大量数据。结果，这些开发环境必须克服与数据集成有关的重大问题。在本文中，我们研究了使用链接的企业数据方法来集成来自不同开发工具的数据的汽车行业案例研究。该研究解释并应用了数据质量评估方法，作为链接企业数据的集成后阶段。

在这项研究中，将文献中重要的数据质量维度与Scania CV AB员工定义的经验规则合并。结果，开发并引入了一种全面的方法来评估这些数据质量维度。本文介绍了该方法，旨在开发一种数据质量评估工具（一个仪表板）以及用于管理数据质量的策略和协议。此外，拟议的方法包括用于计划数据质量评估活动，提取数据质量管理要求，设置优先级以加快适应，确定维度和指标以简化理解以及可视化这些维度和指标以评估总体的系统性准则数据质量。