## 4 Discussion  
Sentences such as \"data are the new oil\" or concepts such as \"data capitalism\" [16; 70] highlight the importance of data as drivers of our knowledge-based society. From time immemorial, data has been a source of power and knowledge. That is why the first digitization of information and subsequent automation were so important in the context of businesses and in the beginning of the conception of computers [71]. In the business world, extracting or discovering patterns from data is useful to increase productivity and as a competitive advantage. Business intelligence [72] was born from these first computerized data analyses, which evolved as a series of systematized processes to exploit data both analytically and visually in search of relevant information. [PERSON]'s \"Information highways\" [73], the digital interconnection of all continents [74] and the technological evolution of telecommunications have allowed business intelligence since the 50s [72] to take one significant step forward. These early technological revolutions in telecommunication together with the adoption of Internet-web technology at all levels systematically generate enormous amounts of data, thus pushing the emergence of big data technologies [1; 9]. Big data is emerging in society and consolidating a fourth revolution where the Internet of Things functions as a gateway for the interconnection of virtual and physical space, blurring both realities. There is little or no differences between real and digital identity. Regarding previous innovations, entities in the educational context are taking part in this revolution by following the same evolutionary pattern. First, Internet-web technology  
Figure 7: (**a**) EIStudy’s visualization toolL once the user creates a request to the educational warehouse modules, it offers data related to the specified content and its role. If the user is a teacher, he or she will be able to see “individual” values and “group” values, whereas students will only see “individual” values since they are not allowed to see data from other people in that context (**b**). is being adopted when virtual learning environments or learning management systems become relevant, and then, analytical approaches such as educational data mining [75] or learning analytics [76] and their intersection [77; 78] are adopted. Finally, big data and artificial intelligence technologies [79], such as facial recognition or brainwave analysis to know students' attention or emotions are implemented. The Internet of Things is an integration, and we already see functional solutions that combine big data, machine learning, artificial intelligence and cloud computing [6]. However, the adoption of Internet-web technology as a means of communication has also set out exponentially, reflecting what [PERSON] stated in his 1996 Request For Comments (RFC) [80]: \"The Referer field allows reading patterns to be studied and reverse links drawn. Although it can be beneficial, its power can be abused if user details are not separated from the information contained in the Referer.\" Nowadays, lots of data types are collected to improve services as commodities for society and the educational context, risking the privacy and security of people's data and, consequently, both their real and digital identity. This has been the case in recent misuses, data leaks and improper access due to the high value of data in data capitalism platforms [70] where privacy and security are at risk because of daily surveillance [15]. In terms of the educational context, maintaining the privacy, security and digital identity of students and their data is even more critical. In many cases, data related to minors are also involved [81]. Although data analysis could bring benefits, there is an intense fragility in managing privacy and security of students' data in interconnected virtual environments and in exposing their real identity to different hazards, such as cyber-bulling or virtual harassment [32].