###### Abstract  
With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this. data quality assurance; data mining; machine learning; manufacturing data management;  
out, disturbance effects in the data must be recognized and eliminated. For this purpose, there are extensive methods with which simple effects [13] can be identified. There are also methods, e.g., based on fuzzy models, with which complex interactions between linked signals can be detected to be able to evaluate the uncertainty of the recording of parameters [14]. Meanwhile, there is preliminary work using AI methods such as deep learning to assess the trustworthiness of complex data sets from machine systems [15; 16]. For this, a holistic method is required that thinks from the essence of the CPPS and places individual methods for signal processing in an overall context. The existing state-of-the-art of data quality improvement solutions \"lack general means to detect, validate and correct the gathered sensory data\" [17]. Many known solutions deal with the quality assurance of enterprise-level data (e.g., master data [18; 19]), where usually the customer, production planning, and facility data are analyzed and maintained. For the analysis of the production processes, the primary sensory (or signal) data are essential [20], for which further methods for quality assurance are necessary [21]. There are already different metrics for the master data quality assurance and procedures for their systematic improvement. For the data-driven analysis algorithms in production engineering systems, there are particularly complex challenges regarding data quality assurance. These challenges result from the use of data from different sources with different data types and a large number of relevant parameters (often several hundred), which must be combined into a data set. Moreover, the structure of technical production systems, as well as the product life cycle and the analysis workflow, produce numerous possibilities for influencing data quality, which must also be taken into account. The product life cycle includes the design, assembly, commissioning, operation, and withdrawal from service phases. In the design phase of a production system, data quality is already influenced by the choice of suitable measurement principles and the selection of the appropriate sensors for data acquisition. During the assembly and commissioning phase, failure detection methods are implemented during machine or tool programming. In the operation phase, faults can occur either in the measuring system or in the communication system that has nothing to do with the desired system behavior, such as signal noise, cable breakage, etc. The fault detection methods are usually implemented during installation and commissioning of the equipment, or later during the operation phase. The strategy and algorithms of the data processing must realize the \"correct synchronization\" of data from different sources and the context-related extraction of effects from the total data with \"mixed content\".