An Exploration of Big Data and Analytics Software

Abstract

Big data has become a significant part of the modern society and influences all aspects of life ranging from society, economy, to science. Large organizations have implemented big data analytics for management, maintaining production processes, developing products, marketing, avoiding risks, etc. Nevertheless, the degree to which small and medium sized entrepreneurs (SMEs) utilise data or big data analytics is surprisingly limited. This thesis thus sets out to study, among others, the many issues that create barriers that prevents SMEs from implementing big data analytics. The issues included lack of understanding, dominance of domain specialist, cultural barriers and intrinsic conservatism, shortage on in-house data analytic expertise, shortage of useful and affordable consulting, non-transparent software market, lack of intuitive software, concerns on data security & concerns about data protection and data privacy.

This study aimed to describe the state-of-the-art of the Big Data management with a special focus on the methods and tools of (big) data analytics. The theory part discussed big data definition, described five selected data analytics methods and introduced three popular analytics software programs. The practical part of the study explored the features of different Big Data management/ analysis software programs and identified the challenges faced by SMEs when implementing Big Data analytics. Subsequently, a comparison study of these different features was conducted. Based on the findings, this study proposed a recommendation to Centria University of Applied Sciences regarding the most suitable analytics software programs for the university’s corporate partners.