Growing demand and Internet development for things and big data encourage a scientific measurement concept and discovery. Big data is a collection of strategies that could assist with the continuous assessment and transformation of the higher education system to meet the rapid speed of evolving developments in various sectors of the economy, which in turn generates a range of employee skills. Technology is an essential component that has radically altered how education is delivered. For example, mobile equipment, remote access systems and conference calling, academic platforms and services, etc. Teachers, students, academics evaluation experts, decision-makers interact, and researchers, including different types of technology used in education. These are aimed at affecting and enhancing learning and teaching and reflecting the use of modern technologies in real learning environments realistically. Large quantities of data are created through contact with these technologies, ranging from a single access log file to institutional-level operations. Educational systems, however, are not yet fully prepared to address them and use them to improve their continued quality (Qin, 2015). It is more pressing than ever that challenges be regulated within the education system. During the duration of the investigation and use of knowledge discovery, various options such as big data and analytics were indeed considered.
Big Data and Education
To explain and characterize the recent development and nature of massive data sets, Big Data (Bedi et al 2021) is now commonly used. In many sectors, it is found. There is constant receipt of large volumes of data from diverse sources and in different formats generated by the public, corporate, and social sectors. In certain situations, the information is exceptionally massive, such as petabytes that surpass the warehouse's structural or material capital, are handled and processed and are thus referred to as deep learning. Sector by sector or, in general, within the same sector, department by department (Qin, 2015). Because of its attribute of becoming huge, big data is referred to as such. Big data is therefore distinguished by additional features, including the various forms and ways and the distinct information streams, and the pace at which they are generated and, quite significantly, either by the intensity at which it is gathered. They are commonly extracted or often handled in actuality. They all have volume (size) and a wide range (sources, ways, and types) as well as pace (speed and frequency), illustrating and presenting challenges to data that would be another issue (Xuejuan, 2015).
When the number, diversity, and speed are high simultaneously, data in a particular system or domain is called big data, irrespective of whether all three of such features may be deemed 'tiny' for the text field. The data can vary between megabytes and petabytes, depending on the domain. Therefore, large data is context-specific and could refer to different types and sizes from one domain to the next. This is sufficient throughout this situation to question the limitations on how the information could be interpreted and evaluated so that it could be used only for multiple purposes. However, interpreting the data by analyzing it at a higher predictive rate and facilitating the development of data-driven systems and practices is the challenge common to all these areas (Abu et al., 2019).
Large amounts of data and analytics have brought benefits to results in various situations, thus providing an immensely helpful strategy in their possible impact on education, analysis methods in the area of business intelligence and analysis (Toivonen et al., 2019), or the field of professional data mining technologies (Miah et al., 2020). Due to the small analysis towards the use of Big Data and analysis in educational standards, we are looking to launch the user to the different territory of Big Data in academia where Data Analysis is integrated into technology, and also how information curriculum could be presented to different teams, such as politicians, assessment specialists, faculty, in different aspects and perspectives.