## 2 Materials and Methods
### Big Data and Perspective in Industry 4.0  
Big Data is one of the most important technologies in Industry 4.0. It includes the collection of data from all possible devices in companies as well as data on customers or processes. They contribute to increasing the quality and speed of production, predicting machine failures (predictive maintenance) and focusing production more on customer needs. The term Big Data does not only apply to the manufacturing sector, but it is also possible to collect and evaluate data in the business sector or in transport.  
Big Data is characterised by the three Vs (Volume, Velocity and Variety). Volume, as the name suggests, predicts huge volumes of processed data, on the order of terabytes to tens of petabytes. Velocity characterises the speed of data recording and processing. Data are coming in at a great pace, so it is important to be able to process it continuously. The data are not only structured--some are semi-structured and some unstructured--this is characterised by the name Variety. In [3], the authors point out another important characteristic of data: the criticality and non-criticality of the collected data. Such data are particularly interesting for small- and medium-sized enterprises, as they usually have difficulty deploying Industry 4.0 technologies. Then, the question arises: Which data have a higher value when collected? The answer depends precisely on the criticality and non-criticality of the collected data, which contributes to an increase in the quality or speed of production.  
For enterprises lacking sufficient capital to incorporate more technologies to enable the transition to Industry 4.0, Big Data is still a key technology to implement. The need to collect and analyse data is essential to ensure the competitiveness of businesses in their industries. In the case of small- and medium-sized businesses, Cloud Computing can also be a key in evaluating their collected data [4].  
In this case, it is also necessary to be more concerned about the security of the data sent and evaluated in this way. To the same extent, key personnel should be trained in data collection and analysis [5]. Another problem for small and medium-sized enterprises can be the extensive data collection required for predictive maintenance. Predictive maintenance requires large amounts of time-consuming data for proper evaluation. This may not be a problem for large companies that own a higher number of machines. Small- and medium-sized enterprises, often having only a few, sometimes just one piece of machinery, may have difficulty accumulating enough data to properly evaluate predictive maintenance--in some cases it may take years [6]. Many companies collect data but cannot process and evaluate it efficiently. There is often a lack of departmental connectivity, where data from one department that could be used in other departments consequently remains unused because of the lack of an integrated system. The solution is Enterprise Resource Planning(ERP) systems that work with all the data collected in the enterprise [7]. There are some of the challenges in deploying and operating Big Data in businesses.