The evolution of Information and Communication Technologies (ICTs) over the past few decades has significantly contributed to global socio-economic progress. Countries with higher ICT adoption rates tend to experience improved economic outcomes. However, the aspiration for a digital society remains elusive for some countries, leading to a digital divide at both individual and enterprise levels. This divide, addressed at the 2003 World Summit in Geneva, refers to the disparity in access to specific technological infrastructures [1]. The digital divide can also represent the psychosocial gap between those who embrace the digital revolution and those who reject it due to various personal and demographic reasons. While the digital divide has decreased for some technologies, new and emerging technologies contribute to a divide among enterprises. This is concerning as enterprises heavily rely on ICTs to enhance their competitiveness. One such technology is big data, primarily driven by the emergence of Industry 4.0. The concept of Industry 4.0 was initially proposed at the 2011 Hannover Fair and became a German strategic initiative in 2013. The fourth industrial revolution, facilitated by the development of the Internet of Things (IoT) and big data, has enabled the implementation of automation and artificial intelligence in industrial environments, making them “smart” [2]. Big data plays a crucial role in Industry 4.0 enterprises. Big data algorithms and technologies enable the discovery of new business insights and informed data-driven decisions, improving organizational performance and competitive advantage. Consequently, it is projected that 40% of ICT investment growth from 2012 to 2020 would be devoted to big data [3]. Big data refers to large amounts of structured and unstructured data, usually collected in real-time. Its complexity can be summarized by the 3V model: Volume, Variety, and Velocity. Machine learning or deep learning is an integral part of big data systems because they can learn from big data. This is crucial as it is nearly impossible for humans to generate any relevant insight from big data without machine learning [4]. Machine learning on big data can help businesses detect and prevent various types of fraud, thereby increasing their security and reducing costs associated with computer crime. Advances have been made in various fields, such as weather forecasting, natural disaster management, medicine, biology, and physics. The benefits of machine learning and big data have been demonstrated in various industries, including insurance, chemistry, and energy [5]. Big data is also used in the public services domain, where insights can foster innovations. Some additional implementations include public safety, smart health, smart grids, and eGovernment. In summary, the rise of ICTs and big data has significantly impacted various sectors, driving socio-economic progress, and shaping the future of industries and public services [6].

This research uses a dataset from Eurostat on big data usage in enterprises, focusing on the sources of big data and the expertise employed in these enterprises. The data, collected in 2018, covers 28 European countries and Norway, excluding the UK, and categorizes enterprises by size (small, medium, large). The study investigates the level of big data digital divide among these countries, the impact of using internal or external big data experts, and the level of big data usage in various industries. The study employs cluster analysis to address these research questions, which identifies homogeneous groups within a dataset. This process involves determining the variables for data segmentation, selecting the clustering method (in this case, non-hierarchical k-means clustering), choosing the number of clusters, and interpreting the results. The analysis used 12 observed variables on big data utilization and validated the optimal number of clusters through v-fold cross-validation. The findings from this research provide valuable insights into the utilization and acceptance of big data across different industries and countries in Europe. The research aimed to examine the digital divide in big data usage among European countries and different industries. It found that big data, which enhances competitiveness by enabling customer intelligence, competitive intelligence, and process intelligence, is used differently across countries and enterprise sizes. European countries were divided into three clusters based on big data usage, with the most developed countries showing the highest usage. The study also revealed that enterprises using big data more frequently rely more on internal than external experts, especially in large enterprises. In terms of industry, enterprises in the Information and Communication industry and the Electricity, Gas, Steam, Air Conditioning, and Water Supply industry showed the highest big data usage. The research confirmed Northern European countries’ leadership in technological innovations, highlighted substantial differences in big data usage across industries, and underscored the challenges small companies face in implementing big data. These findings contribute to understanding the digital divide in big data usage and can guide future research and policy-making. The research confirms Northern European countries’ leadership in innovative industries like big data, despite a digital divide among EU member states. This divide is slowly decreasing, but Industry 4.0 will play a significant role in the new digital divide, affecting those with low education levels the most. The study reveals that industries like information technology lead in big data utilization, largely due to their employees’ technical competence. However, European manufacturing enterprises lag in big data usage. Large companies lead in implementing innovative technologies like big data, but small enterprises with groundbreaking ideas could also compete effectively. The research suggests the need for educational interventions, including a strong curriculum focusing on big data and nationally introduced massive open online courses. These programs should be tailored for open-source big data software usage, benefiting small enterprises by increasing their efficiency in acquiring big data expertise and reducing costs.