## 2 State of the Art
### Quality Assurance on Data
#### 2.3.2 \"Quality Dimensions\" for Data  
In our work, we understand data as a reinterpretable representation of information in a formalized manner, suitable for communication, interpretation, or processing (see also ISO/IEC 2382-1). Quality is commonly defined as the degree according to which inherent characteristics of an object meet requirements (ISO 9001). In the case of data, this is the degree to which dimensions of data quality meet requirements. The German Society for Information and Data Quality e.V. (DGIQ) defines 15 dimensions of data quality [35], which are shown in Figure 2. These are divided into four categories. The DGIQ data quality model is based on a study by the Massachusetts Institute of Technology (MIT) [36]. Other works propose further definitions of data quality criteria [37; 38; 39; 40]. In 2021, the Data Quality Working Group (Data Quality of DAMA Netherlands) conducted a general survey on definitions of data quality dimensions. It collected definitions from different sources and compared them. The working group also tested the definitions against criteria derived from a standard for concepts and definitions (ISO 704). The result represents a list of 60 dimensions of data quality and their definitions [41]. The resulting dimensions were thereby assigned to the corresponding data concept (such as \"Attributes\", \"Data\", \"Dataset\", \"Format\", \"Metadata\", etc.). The most important dimensions of data quality correspond to those defined by DGIQ. Experience shows that in the production engineering field, no more than \"only\" 11 relevant data quality criteria are actually pursued [42]. In rare cases, they are all considered simultaneously. These 11 criteria are usually selected according to their meaningfulness and purpose [43]. The following 6 criteria are the most important ones [42]:  
* **Completeness**: a data set must contain all necessary attributes. Attributes must contain all necessary data. * **Uniqueness**: each data set must be uniquely interpretable. * **Correctness**: the data must correspond to reality. * **Timeliness**: all data sets must correspond to the current state of the depicted reality. * **Accuracy**: the data must be available with the required accuracy. * **Consistency**: a data set must not show any contradictions within itself or with other data sets. The other 5 criteria can be added over time--to secure and further improve the achieved (basic) data quality:  
* **Non-redundancy**: there must be no duplicates within the data records. * **Relevancy**: the information content of data records must meet the respective information requirements. * **Uniformity**: the information in a data set must be structured uniformly. That is, a set of data is continuously presented uniformly. * **Reliability**: the origin of the data must be traceable. Figure 2: Definition of data quality criteria according to the proposal of the German Society for Information and Data Quality ref. to [35]. * **Understandability**: the terminology and structure of the data sets must be consistent with the ideas of the recipients of the information (e.g., departments). As already mentioned, all data must meet the set quality requirements. However, there are specific (adapted) quality assurance methods for each type of data. Up to now, data quality has been viewed predominantly from a database perspective. That is, the focus was on the master data (context data) and not on the production-relevant transaction data (especially--signal data). In Table 1, the authors of this paper have attempted to summarize the status of quality assurance methods for the most frequently occurring data types \"signal data\".