Data Quality Management for Interoperability

Abstract

The challenge of enhancing and generalizing interoperability as an important pre-requisite for Digital Twin is often hindered by the fact that data quality requisites depend on the purpose for which the data will be used and on the subjectivity of the data consumer. Data quality is getting as important as product quality in manufacturing process. In this chapter we present how to systematically handle the data quality requirements and support a comprehensive data quality management. After the definition of Digital Thread as a data highway, the classification of data quality is discussed based on data quality dimensions and standards related to field of manufacturing. The data quality metrics is discovered upon the guidelines developed by national and international harmonization bodies from global automotive industry. Three practical examples from design and manufacturing as well as data migration in industrial context give insight in practical challenges and achievements in the field of data quality as well as future directives. The discussion section emphasizes the high importance of data quality for the generation of Digital Twin.