From Big Data to Big Impact: analytics for teaching and learning in higher education

Sushil S. Chaurasia (Symbiosis Institute of International Business, Pune, India)
Anna Frieda Rosin (HHL Leipzig Graduate School of Management, Leipzig, Germany)

Industrial and Commercial Training

ISSN: 0019-7858

Publication date: 4 September 2017



The purpose of this paper is to examine the applicability of Big Data in higher education institutions.


A qualitative research approach using semi-structured interviews was employed to get insights from 23 experts from the Indian higher education sector. Respondents included higher education specialists from information technology, administration and academicians from public and private funded institutions.


Based on competitive advantage and data complexity, four major application areas were identified for the use of Big Data in higher education. These application areas are reporting and compliance; analysis and visualization; security and risk mitigation; and predictive analytics.

Research limitations/implications

Qualitative methodology is suitable to explain constructs and relationships between constructs, but it does not explain the magnitude of the relationships. The lack of Big Data experts in higher education constrained the ability of this research by leading to repeated themes. Finally, including participants from other countries would have assisted further in generalizing the findings.


As both interest and reluctance persists about Big Data, it calls for the application across industries and cost-benefit analyses. A number of researchers have studied the use of Big Data in various fields associated with the applicability, the data availability, the cost, the competence, the privacy, the relevance and the ownership. Very few publications explicitly address the integrative use of Big Data in higher education. So the current study examines the applicability of Big Data analytics in higher education institutions.



Sushil S. Chaurasia and Anna Frieda Rosin (2017) "From Big Data to Big Impact: analytics for teaching and learning in higher education", Industrial and Commercial Training, Vol. 49 No. 7/8, pp. 321-328

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