## 1 Introduction  
One aspect of industry 4.0 aims towards the interconnecting of devices and sensors (cf. Industrial Internet of Things, IIoT) to, e.g., increase efficiency by enhancing business models and identifying bottlenecks [1]. Therefore, data are a key factor. Using data-driven applications enables companies to optimize internal and external processes to decrease resource consumption or to gain competitive advantage [2]. However, to exploit the full potential, analyst require a high data quality which has to be ensured during acquisition and storing process-related information [2; 3]. Simultaneously, information and communication technologies (ICT) lead to steadily increasing energy usage (between 1% and 3.2% of global consumption in 2020) and are prognosticated to represent up to 23% by 2030 [4]. In order to reduce climate-damaging emissions and become climate-neutral, the IT landscape is therefore an important factor to consider. In our work, we aim to combine the aspects of data quality in combination with green computing approaches to archive improvements in data pipelines for IoT environments. Since our work is in cooperation with Robert Bosch Elektronik GmbH, we mainly focus on smart manufacturing scenarios, but our results can be adapted to other areas like smart healthcare as well. Figure 1 shows an example of a smart surface-mount technology (SMT) line. Such lines are used at Bosch among others to manufacture printed circuit boards (PCBs) for control units (e.g., for vehicles and e-bikes). SMT basically includes five processes: (1) solder paste printing (SPP) for printing solder paste on the circuit board, (2) solder paste inspection (SPI) to inspect the paste, (3) surface-mounted device (SMD) for placing components on the board, (4) reflow soldering (RFL), which is the actual soldering process, and (5) solder joint inspection (SJI) to inspect and test the final product. From a data point of view, SPL, SMD, and SJI provide the most important information; thus, the highest effort has to be on these processes. During manufacturing, we noticed that in some cases, data and final product did not match. On a closer inspection, we observed that these mismatches are due to erroneous data, which are the result of a heterogeneous production landscape with various machines and software versions. We called these mismatches inconsistencies. Inconsistencies usually refer to a data set, which corresponds to all messages and information related to one manufactured product. To handle erroneous data, we categorized our inconsistencies into four main classes (cf. [5; 6]):  
1. Missing message. A missing message leads to incomplete data and thus, an information gap. 2. Multiple message. Multiple messages from one process may indicate problems with a machine. 3. Incorrect content. This category refers to the content of a single message. 4. With contradictions. By comparing the data of a data set, the matching has to be conflict-free. The classification resulted from the study of our internal data. Categories 1 and 2 refer to entire messages that are either missing or duplicated (cf. [PERSON] et al. [7]). Since we validate complex JSON files, the content is of major interest in our use case. Figure 1: Run through a smart SMT line with data from SPP, SPI, SMD, and SJI. The machine pictures are provided by AE/MFT1 department. Our examinations have shown that inconsistencies either concern the content of a single message (incorrect content) or constitute a mismatch between the information content of two or more messages (with contradictions). Although the categories (especially 3 and 4) evolved from a smart manufacturing use case, the manifestations of inconsistencies are similar in related IIoT and IoT domains. In smart healthcare, we can, for example, track the completeness of health records and identify discrepancies between two related parameters. In order to ensure a high quality, in the past we presented a concept for a consistency checker (CC) [5].