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).