Big journals between 1996 and 2015) extracted from

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


BD and BDA as a research
discipline are still evolving and not yet established, thus, a comprehensible
understanding of the phenomenon, its definition and classification is yet to be
fully established. The extant progress made in BD and BDA not only revealed a
lack of management research in the field but a distinct lack of theoretical
constructs and academic rigor – perhaps a function of an underlying
methodological rather than academic challenge. At large, there has also been a
lack of research studies that comprehensively addresses the key challenges of
BD, or which investigates opportunities for new theories or emerging practices

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there exists the need to culminate the BD challenges and associated BDA methods
to allow signposting to take place. Following the earlier limited normative
research studies conducted by Polato,
Ré, Goldman, and Kon (2014) – mainly focusing on Apache Hadoop; Frehe,
Kleinschmidt, and Teuteberg (2014) – BD logistics; Eembi,
Ishak, Sidi, Affendey, and Mamat (2015) – on data veracity research
for profiling digital news portal, and Abdellatif,
Capretz, and Ho (2015) – on software analytics (a distinct branch of
BDA), this paper attempts to broaden the scope of their reviews by
further investigating and assessing the different types of BD challenges and
the analytical methods employed to overcome the challenges. Although these
research studies provide worthy understanding on some aspects of BD and BDA
area, there seems to be a lack of comprehensive and methodical approaches to
understand the phenomenon of BD – more precisely the types of BDA methods thus
an aide memoir will act as a suitable frame of reference. Moreover, explicitly
in respect of the conclusions offered by these existing review articles, this
research specifically aims to:

analyze, synthesize and present a state-of-the-art
structured analysis of the normative literature on big data and big data
analytics to support the signposting of future research directions.


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