## 3 The New Methodology: Goals and Requirements  
Data quality assurance is a complex challenge that must include all steps in the data processing method--from sensor selection to configuration of the measurement or control software to context-related analysis and interpretation of the data. Thus, the approach must describe a principal procedure for the holistic data quality assurance for the ML to production engineering plants and processes. On one hand, the approach is intended to improve the accuracy of results of ML procedures, thus increasing their usefulness and ultimately their acceptance. On the other hand, the methods integrated into the procedure model enable the introduction of quality assurance in ML projects. At last, but not least, it enables the implementation of software for data management which would offer the best conditions for a broad and simple subsequent use. Thus, three user groups are addressed: production engineers, data analysts, and developers of relevant software systems.  
In the production engineering environment, the following engineering tasks offer particularly high potential for the application of ML projects and generate demand for a data quality assurance approach:  
* development of measurement methods as a basis for process control and quality control,
* commissioning from the component to the plant,
* commissioning (also sampling or tryout) and ramp-up of processes,
* condition monitoring (CM) and predictive maintenance (PM),
* machine optimization based on field tests on many machines of the same type,
* optimization of manufacturing processes regarding quality, time, energy, and resource consumption as well as costs.  
These engineering tasks must be the focus of the Quality Assurance procedure. Thus, the Quality Assurance Approach will make a sustainable contribution to increasing the productivity and quality of manufacturing processes and ultimately to conserving resources in manufacturing--based on the increased performance of data-driven methods in the sense of Industry 4.0. The above-mentioned engineering tasks must be analyzed from the perspective of influencing data quality and developed as task-specific workflows subdividing the process model. Although the engineering tasks each have their own target variables and methodical workflows, they are potentially based on the same data-handling principle. Based on the state of the art and the summarized problem statement, the following requirements are placed on the Holistic Quality Assurance Approach. The approach must:* cover the whole chain of data processing (from the acquisition of raw data from the respective process or plant to the presentation of the analysis results of a data-driven application);
* cover the whole development chain of the (measurement) process or plant (from the goal-setting for an ML application to the implementation of the data acquisition);
* shall assign the individual data quality criteria to the corresponding areas of influence;
* support the involvement of all required competencies of the responsible personnel or system development experience in all phases of plant design and subsequent data processing;
* establish visibility and assessability of the data quality in all phases, so that the responsible person can assess and decide in time;
* have template capability and allow for automation so that as many steps as possible to improve data quality can be automated;
* support the basic procedure as a guideline for action and assistance.