Data Quality in the Automotive Industry

Measurement and Analysis of Data Quality in Various Production Processes

In the course of digitalization, organizations have access to very extensive and ever-growing volumes of data (keyword: "big data"). The volume of data will increase tenfold between 2016 and 2025, creating a wide range of opportunities for companies. For example, the targeted and well-founded analysis and use of this data enables improved, data-driven decision support and process management. Especially since the start of Industry 4.0, the importance of data in the production environment and the associated acquisition of insights as well as the generation of efficiency gains has been steadily increasing. Ensuring sufficient data quality is an essential basis for the profitable use of data. If this is not sufficiently given, erroneous analysis results and wrong decisions result, which cause more harm than good ("garbage in, garbage out"). However, empirical evidence shows that the data used by companies is often characterized by low data quality. This is caused in particular by insufficient sensitivity to data quality and data quality problems, a lack of transparency of the data quality level (data quality measurement) and hardly any systematic quality improvement and control.

Against this background, there is also a need for our practice partner from the automotive industry to create the basis for methodical data quality management. In the production environment, the data of the various production technologies and controls in particular represent a central asset and an important source of big data. Initially in a pilot project and then also in a subsequent second project, analyses were to be carried out for various production technologies in order to assess and, if necessary, improve the status of data quality. After assessing the status quo of data quality, it was jointly decided that suitable metrics for measuring the data quality dimensions completeness and consistency should be developed to ensure good data quality management. The goal is to gain knowledge about the current data quality through the specially developed methods as well as procedures and, if necessary, to derive improvements in decisions for action and reduce waste. Furthermore, the results of this project are to be extended and transferred to many other application fields and production technologies in the future.

Project periods: December 2020 – April 2021 & February 2022 – September 2022


Both the pilot and the follow-up project pursued the goal of soundly assessing the quality of the large amount of data ("Big Data") collected within the automotive production environments, i.e., quantifying it with meaningful and interpretable metrics. Our developed metrics for completeness and consistency extend the se of scientific methods in the area of data quality, with which the Institute for Business Analytics significantly advances this research area. The improved assessment of data quality in the production environment makes it possible to establish a comprehensive data quality management and to preventively reduce losses, e.g. through reduced scrap. The cooperation and transfer in this project thus results in direct added value for science and practice.