Data quality program management for digital shadows of products

Abstract Nowadays, companies are facing challenges due to increasingly dynamic market environments, a growing internal and external complexity, as well as globally intensifying competition. To keep pace, companies need to establish extensive knowledge about their business and its surroundings based on insights generated through the analysis of data. The digital shadow is a novel information system concept that integrates data of heterogeneous sources to provide product-related information to stakeholders across the company. The concept aims at improving the results of decision making, enabling advanced data analyses, and increasing information handling efficiency. As insufficient information quality has immediate effects on the utility of the information and induces significant costs, managing the quality of the digital shadow data basis is crucial. However, there are currently no comprehensive methodologies for the assessment and improvement of the data quality of digital shadows. Therefore, this paper introduces a methodology that supports the derivation of data quality projects aimed at optimizing the digital shadow data basis. The proposed methodology comprises four steps: First, digital shadow use cases along the product lifecycle are described. Next, the use cases are prioritized with regard to the expected benefits of applying the digital shadow. Third, quality deficiencies in the digital shadow data basis are assessed with respect to use case specific requirements. Finally, the prioritized use cases in relation with the identified quality deficits allow deriving needs for action, which are addressed by data quality projects. Together, the data quality projects constitute a data quality program. The methodology is applied in an industry case to prove the practical effectivity and efficiency.