We are living in a “data-driven era” where new data is continuously being generated by people, businesses, organisations, communities and society each and every moment. This rapidly growing and huge amount of data must be managed through effective way for producing useful information and human decision support for current and future problems solving, planning and practices improvements as well as to generate new knowledge for future generation. As a result, organizations globally are making significant investments to explore how to better utilise the huge data and its diversification to create value and actionable insights (for example, Pardos
2017; Miah et al.
2017,
2019a,
b). Big data has been a popular research problem across different academic disciplines. Although this problem has been treated mainly for advancing and innovating technological development (Wang et al.
2017), organisations and business communities are continuously exploring different aspects, perspectives and contextual specifics to find or explore benefits and value adding for improving practices. A lot of existing studies have defined the big data considering large volumes of broadly varied and complexity of datasets that are continuously being generated. The consideration for defining this goes to velocity, volume, value, variety, and veracity, so-called the "five V's of Big Data" (Gandomi and Haider
2015). Organizations such as education institutes have started to treat the issues of big data for reinforcing traditional electronic learning and teaching methods and other relevant products, and services (McAfee et al.
2012). For doing this, opportunity of adopting latest analytics, predictive algorithms and other disruptive technologies are rapidly developed for advancing the traditional e-learning approaches, for example in improving learning management systems (LMS). …
