Disaster recovery site evaluations and selections for information systems of academic Big Data

The most dramatic factor shaping the future of higher education is Big Data and analytics. In the Big Data era, the explosive growth of massive data manipulations imposes a heavy burden on computation, storage, and communication in data centers. Increasing uncertainties in information system availability have become a daily serious problem. An appropriate evaluation and selection of the right information system disaster recovery (DR) site can ensure business continuity and investment optimization. However, most academic institutes always neglect the importance of DR. Not to mention the DR sites in the era of Big Data. Existing research results do not evaluate or select DR sites in general or those for academic Big Data applications in particular. Therefore, this research aims to establish an analytic framework for evaluating, selecting DR sites for academic Big Data. The proposed analytic framework is consisting of the Decision-Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based network process (DNP) and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. An empirical study based on a real Big Data DR application of an Asian high-performance computer center's evaluation and selection of DR sites for academic Big Data is used to illustrate the feasibility of the proposed framework. The analytic results can serve as a foundation for information technology administrators' strategies to reduce the performance gaps of a DR site for Big Data manipulations in general, and academic Big Data manipulations in special.