The analysis by the consulting firm Staufen and the TU Darmstadt states that almost three-quarters of companies in the electrical industry now use big data to control product quality. Although the technology is also widespread in machine engineering (59%) and product development (51%) sectors, companies have made very little use of the collected information to date. The study's findings indicate that although companies are indeed using big data to create transparency in the production process, they cannot not make any statements about cause and effect. This knowledge is then lacking when it comes to anticipating potential faults or automating the trouble-shooting process. The necessary analysis is "still rarely found in the companies". In addition, apparently not all measured data is transferred to memory systems, from where it can then be read and analyzed. It appears that traditional QM methods are slowly surely reaching their limits.

"Quality assurance will become more of a data science through the opportunities offered by the collection data and its smart evaluation and use," says Malte Fiegler of the German Society for Quality (DGQ). In an interview with the software provider Böhme & Weihs , he explains that various quality features can be generated in the production process. There is also an opportunity to detect deviations and eliminate errors during monitored product usage.