# Effective Data Utilization in the Context of Industry 4.0 Technology Integration
## 4 Discussion and Conclusions  
Data are an important internal resource for any industrial organization. Proper use of high-quality data can assist organizations in analysis, decision making and forecasting to meet strategic objectives. However, the prerequisite is that is the data are properly collected, adequately evaluated and processed into the resulting information that will benefit and add value to smart organizations. Their utilization is not limited, they can help in the field of decision making, optimization, or process improvement, as well as in the application of various lean methods and industrial engineering practices in practice. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Improving Production**} & \multicolumn{2}{c}{**Improving Logistics**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Reducting the** \\ **Consumption of** \\ **Materials** \\ \end{tabular} } & \multicolumn{2}{c}{**Predictive Maintenance**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Ensuring and** \\ **Improving Quality** \\ \end{tabular} } \\  & \multicolumn{2}{c}{**Replicative**} & \multicolumn{2}{c}{**Absolute**} & \multicolumn{2}{c}{**Relative**} & \multicolumn{2}{c}{**Absolute**} & \multicolumn{2}{c}{**Relative**} & \multicolumn{2}{c}{**Absolute**} & \multicolumn{2}{c}{**Relative**} \\  & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Frequency**} \\ \hline Big data & 71 & 21\% & 48 & 14\% & 51 & 15\% & 36 & 11\% & 57 & 17\% \\ \hline Cloud computing & 112 & 33\% & 66 & 20\% & 71 & 21\% & 46 & 14\% & 85 & 25\% \\ \hline Internet of things & 37 & 11\% & 24 & 7\% & 22 & 6\% & 12 & 4\% & 29 & 8\% \\ \hline Cybersecurity & 87 & 25\% & 55 & 16\% & 56 & 17\% & 46 & 14\% & 71 & 21\% \\ \hline Cybers–Physical systems (e.g.,  automatic conveyors) & 74 & 22\% & 47 & 14\% & 48 & 14\% & 34 & 11\% & 55 & 16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Impact of collected data from selected Industry 4.0 attributes on processes. Figure 6: Areas of use of data obtained from processes. Big Data is perceived as a technology for a rigorous and intelligent system for evaluating the data collected. In this system, it is a new more sophisticated data source that can be used to better identify the state inside the processes and understand the value of data that can be obtained through Big Data technologies. The use of Big Data in this context will have a significant impact on creating a competitive advantage for businesses [4]. The importance of Big Data, its collection and use for effective process management was confirmed by the data in Tables 6 and 7 (maintained by the authors of the publication), on the basis of which it is possible to fully agree with [PERSON] et al. The Internet of Things will provide industrial organizations with interconnected manufacturing Cyber-Physical systems, sensors will collect data from processes across the entire product lifecycle, which will be stored on Cloud Computing platforms and evaluated through Big Data analysis to add value to the organizations. This process supports the transformation of traditional organizations into smart ones. Several barriers can hinder the full implementation of Industry 4.0, such as high costs, financial limitations, lack of management support, resistance to change, poor quality infrastructure, poor quality implementation data and many other negative influences on the concept. Thus, this paradigm relies on digitizing manufacturing processes and entire systems to manage production in real-time with minimal cost. This implies that digitalization will also have a significant impact on manufacturing systems and their sub-systems to be able to respond quickly to customer demands [33; 36].