Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.
The magnitude of data generated and shared by businesses, public administrations numerous industrial and not-to-profit sectors, and scientific research, has increased immeasurably (Agarwal & Dhar, 2014). These data include textual content (i.e. structured, semistructured as well as unstructured), to multimedia content (e.g. videos, images, audio) on a multiplicity of platforms (e.g. machine-to-machine communications, social media sites, sensors networks, cyber-physical systems, and Internet of Things [IoT]). Dobre and Xhafa (2014) report that every day the world produces around 2.5 quintillion bytes of data (i.e. 1 exabyte equals 1 quintillion bytes or 1 exabyte equals 1 billion gigabytes), with 90% of these data generated in the world being unstructured. Gantz and Reinsel (2012) assert that by 2020, over 40 Zettabytes (or 40 trillion gigabytes) of data will have been generated, imitated, and consumed. With this overwhelming amount of complex and heterogeneous data pouring from any-where, any-time, and any-device, there is undeniably an era of Big Data – a phenomenon also referred to as the Data Deluge. The potential of BD is evident as it has been included in Gartner's Top 10 Strategic Technology Trends for 2013 (Savitz, 2012a) and Top 10 Critical Tech Trends for the Next Five Years (Savitz, 2012b). It is as vital as nanotechnology and quantum computing in the present era. In essence, BD is the artefact of human individual as well as collective intelligence generated and shared mainly through the technological environment, where virtually anything and everything can be documented, measured, and captured digitally, and in so doing transformed into data – a process that Mayer-Schönberger and Cukier (2013) also referred to as datafication.