## 2 Theoretical Fundamentals and State of the Art  
To successfully support a digital transformation and to achieve a better comprehension of the production processes, the first step is to assemble a large amount of data from the production sites to understand the operational sequences and to initiate the digital transformation within a company [15]. One of the key factors in this context promoting this transformation is the utilization of the Big Data concept [16]. According to [17; 18; 19; 20; 21; 22; 23], Big Data technologies have to fulfill the three criteria of Volume, Variety, and Velocity. These three requirements can be extended by two further criteria, presented by [24; 25] to five characteristics, which are referred to as the 5 Vs: volume, velocity, variety, veracity, and value. As new technologies become established, new opportunities, challenges, and threats arise. The opportunities originated up by Big Data initially lie in operational efficiency, leading to a variety of advantages for businesses, and therefore, manufacturing operations [26]. At the production level, this might lead to an improvement in production planning. At the executive level, targeted data integration can support decision-making, strategy development and execution as well as supply chain management [27; 28]. All these improvements can subsequently be used to augment customer service [26]. Difficulties occur in the acquisition, transmission, storage, management, analysis, visualization, integration, privacy, and security of data as well as risk management [26; 29; 30]. These difficulties can be traced back to the 5 Vs. The first V, volume, presents a major obstacle in two respects. According to [PERSON] [21], on the one hand, a large amount of data of at least one petabyte must be processed. On the other hand, the corresponding infrastructure has to be available for the intention to be able to process these data volumes in a reasonable amount of time, leading to the next characteristic, velocity. Velocity is defined as the ability to generate, process, analyze, and store data at high speeds continuously or discretely. The velocity of this refers to the time it takes to get from source to destination including all necessary operations. Variety arises from the different file structures that can be distinguished in structured, semi-structured, and unstructured data sets. In more than 70% of cases, data is present in the unstructured form [26]. Veracity arises from the failure to provide data of sufficient quality that cannot be used due to a lack of meaningfulness or uncertainty. Since data analysis inevitably depends on the quality of data, low-quality data can lead to an unintentional distortion of the result. Value describes the added value generated by analyzing and linking data [24; 25]. An additional threat is the privacy of data that could be leaked through cyberattacks due to a lack of security. Other concerns arise from the data itself, should one criterion of the 5 Vs not be met [26; 31; 32; 33; 34; 35]. One of the major challenges of Big Data analysis in the metal processing environment is the high variety of processes regarding the geometry as well as material and the processing steps concerning the application of the respective workpiece, especially for specialized SMEs and in the metal forming sector. Considering the supply chain of a metal-based product from casting to forming of semi-finished products up to component manufacturing, this variety increases even further. A typical supply chain for high-quality metal components, e.g., the aerospace industry includes multiple specialized SMEs, resulting in a processing chain consisting of a large number of different companies involved [2]. In this case, digitalization solutions are often planned and implemented as stand-alone solutions, especially taking into account the internal restrictions of these companies concerning their confidentiality regulations.