In the course of digitisation, organisations have access to very extensive and ever-growing volumes of data ("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 structured analysis and use of these data enables improved, data-driven decision support and process management. Particularly since the start of Industry 4.0, the importance of data in the production environment and the associated knowledge and efficiency gains have been steadily increasing. Ensuring adequate data quality is foundational for the profitable use of data. Lacking data quality leads to erroneous analysis results and wrong decisions, which cause more harm than good ("garbage in, garbage out"). However, empirical evidence shows that the data used by companies are often characterised by low data quality. The main reasons for this are insufficient awareness of data quality and data quality problems, insufficient transparency of the data quality level (data quality measurement), and a grave lack of systematic quality improvement and control.

In this context, our partner from the automotive industry has identified the need to create a strong foundation for methodical data quality management. In the production environment, data from the various production technologies and controls in particular are a central asset and an important source of Big Data. In a joint pilot project, we are analysing a standard technology in production in order to assess and, if necessary, improve the status quo of data quality. For this purpose, the team defines meaningful KPIs (Key Performance Indicators) for the quality of the time series data of the production process, develops suitable metrics for measurement, and quantifies the data quality. The goal is to gain insights into the current data quality through the specially developed methods and procedures and to conclude how to improve decisions and reduce wastage. We hope to expand and transfer the results of this project to many other fields of application in the future.

Cooperation partners: Premium manufacturers from the German automotive industry

Project period: December 2020 - April 2021