# Effective Data Utilization in the Context of Industry 4.0 Technology Integration
## 1 Introduction
### Big Data  
Through the rapid development of the Internet and Industry 4.0 technologies, vast amounts of data and information are being generated and collected. Their processing and analysis go beyond the capabilities of traditional tools. Big Data allows the quick and efficient management and use a of constantly growing database. Big Data technology enables the analysis and separation of important information, and data from less important ones--it helps to draw conclusions and supports efficient knowledge transfer to achieve strategic goals. With this technology, data that have been collected in various incompatible systems, databases and websites are also processed and combined to provide a clear picture of the situation [29]. Big Data enables businesses to optimize processes, improve customer service, and offer a starting point for developing new business models [30]. Big Data refers to a set that collects a large amount of data. This amount of data is difficult to process using traditional storage, applications, and processing methods. By introducing Big Data technology, it is possible to perform tasks that involve a huge amount of data and manage, control, and implement improvements more efficiently and in real-time. Big Data technology is able to analyse a huge amount of different data at an advanced level that flows in the system at a high speed, and thus Big Data is a pillar of the Industry 4.0 concept because the functionality of the Fourth Industrial Revolution concepts that are based on data are derived from the rapid analysis of data [31].  
A Big Data solution package can consist of several critical components, from the hardware layer (such as storage, computing and networking--IoT and cloud computing) to the analysis layer (the actual processing of the data--Big Data analysis), where business statistics are generated using improved statistical and computational methods, algorithms and data visualization [32]. In addition to big data, many other features define the difference between Big Data and terms such as \"massive data\" or \"very big data\". These differences and different concerns in science and technology aspects cause different definitions of Big Data.  
Big Data can be simplistically described as a set of data that could not be collected, managed, analysed and processed by conventional computers within an acceptable scale and capacity. This means that the volumes of data sets conforming to the Big Data standard change and increase over time or with technological improvements. According to [33], in line with this definition, Big Data represents another possible development for innovation, competitiveness and productivity of industrial organizations.  
At the core of the Big Data paradigm is the extraction of knowledge from data as the basis for intelligent services and decision-making systems. This idea covers many scientific disciplines and explores various techniques and theories from different fields including data mining, machine learning, information discovery, analysis generation, etc. Big Data is undoubtedly becoming an important trend in process optimization. The results of optimization depend on the accuracy and availability of information. The concept of Big Data provides analytics to enable insights into the customer's experience in the process, their requirements, and the quality of the products [33; 34].