Framework for defining information quality based on data attributes within the digital shadow using LDA

Abstract

The amount of data, which is created in companies is increasing due to modern communication technologies and decreasing costs for storing data. This leads to an advancement of methods for data analyses as well as to an increasing awareness of benefits resulting from data-based knowledge. In the context of product service systems and product development, there are two major concepts for providing product information. The digital twin collects every information possible, while the digital shadow provides a sufficient and content-related picture of the product. Since these concepts merge data from different sources, comprehension about information quality and its relation to the data quality becomes immanently important. This paper introduces a framework to determine information quality with respect to data-related and system-related attributes. An extensive literature review with focus on “information quality” and “data quality” identifies the important approaches for describing information and data quality. A latent dirichlet allocation (LDA) algorithm is applied on 371 definitions and identify 12 data-related and system-related attributes for information quality. Those attributes are assigned to six dimensions for information quality. So the proposed framework depicts the relationships between data attributes and the influence on information quality.