BSC203 Introduction to ICT Research: Big Data Literature Review

This literature review examines the application and impact of Big Data Analytics in business, drawing on various research articles. It explores how different authors describe big data and its influence on strategic decision-making. The review highlights the importance of data analysis in today's business and technology landscape, focusing on the link between operational and marketing capabilities within the big data analysis approach. The report also discusses the difference between existing and proposed systems, aiming to create a better impact at the business level. Key areas covered include the role of big data in market benefits, the challenges of managing large datasets, and the use of big data in supply chain management. Furthermore, the review analyzes the evolution of marketing mix factors, the costs and benefits associated with big data, and the fusion of marketing analytics and big data analytics, the impact on consumer welfare and security. The report synthesizes diverse concepts within the literature to provide deeper insights for achieving values through big data strategy and implementation.

Running head: LITERATURE REVIEW ON BIG DATA
Literature Review on Big Data
Name of the Student
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Table of Contents
Introduction....................................................................................................................2
Discussion......................................................................................................................2
Literature review........................................................................................................2
Conclusion:....................................................................................................................7
References......................................................................................................................8

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Introduction
This literature review has evaluated the usefulness of Big Data Analytics in business
and reviewed on how it is described in different articles by different authors. The review has
also stated the difference between the existing system and the proposed system which shall
create a better impact in the business level. Big data analytics is the process of citing
important and useful information or data after analysing different types of big data sets. Data
is very essential in today’s business and technology world (Michael & Miller, 2013).This
literature review shall deal with the initiatives in analysing the data in order to gain hold in
the strategic decisions. The purpose and aim of the research is to understand the regulation
and processing of the big data analytic tools and the need or requirement of new
improvement in the data analytical tools that enhances the business structure in the
organization. The review focuses on the linking operations and marketing capabilities on the
big data analysis approach.
Discussion
Literature review
According to Wang et al. (2016), in the article published by them, the big data plays a
very crucial role in getting benefits from the market in a very short time. The data amount
that is produced and is connected or communicated over the internet is gradually increasing.
This significant increase is developing challenges for all organizations related to business that
tend to reap the profits from analyzing the huge big data. According to the report results the
big data has successfully provided unique understanding from the general survey on the mass
of people over the internet, among other things, patterns bought by the customer, market
trends and the cycles of maintenance to enable more targeted business decisions by reducing
the costs. After knowing Big Data Business Analytics (BDBA) and the importance of the

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subject, the author reviewed and classified the results in logistics area and supply chain
management (LSCM) application part of BDBA. The author has assessed the article to the
extent at which the SCA is applied between the LSCM. The highlighted role of
methodologies and techniques denotes the usage of Big Data in an organization.
According to George, Haas and Pentlands (2014), Big data is a term for large
and complex data sets with the problems of storing, analyzing and visualizing for
the further processes or outcomes. The big data are the process of researching through data in
greater amounts which enables the organization to receive and reveal hidden patterns and
correlations. The results are the business of the organization is having a positive effect by the
big data analysis as the information is useful and the business is gaining richer and deeper
insights. This creates a scope of gaining advantage in the competition. This is the primary
reason why the big data needs to be executed as accurately as possible. The data sets
comprises of the data that are sized more than the ability of manual calculating and
commonly used software tools.
According to the author Abello (2015), the data collection method is the process of
collecting and analysing the study. This depends on the collection of primary and secondary
data sources. The survey and interview sources are the methods of primary data sources.
However the secondary data refers to the data that is related to the information collected from
the books, articles or journals. Secondary data contains the data or information that are
already been researched. To research an organization’s need, both the primary data and the
secondary data are collected to conduct the research. The primary data will allow the
researcher to examine and survey on which he will analyse his data and the secondary data
allows the researcher to research and analyse the books and journals which are already
published. This results in accurate observation that helps in gaining correct information and

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applying the resources in the business of the organization proves to be more successful for
the business growth of the organization.
The author Wamba (2015), has investigated both strategic and operational impacts of
the Big Data. A systematic review has been drawn by the author for proceeding along with
the case study findings. The applications used for Big Data have been analyzed by the
interpretive framework presented by the author. They synthesized the very different variety
of concepts of the big data in this literature review. The potential of Big data is to
revolutionize management art. There is a lack of experimental research that evaluates the big
data’s business value, despite the high operational and strategic impacts. This paper presents
an interpretative structure that helps to analyze the discretional and definitional perspectives
of big data applications which will be done on the basis of a systematic review and case study
findings. A general taxonomy is also provided by the paper that helps to broaden the
understanding and role that is been played by big data that captures business value. Within
the literature on big data, the synthesis of the different concepts helps to provide deeper
insights for achieving the values through big data strategy and implementation.
Saboo and Park (2016), States the resources of marketing to allocate which has been a
subject of extreme examination, but the literature topic has not been paid sufficient attention
to the marketing mix factors and elements that vary in an effective way over a period of time.
Although, the companies are collecting the huge numbers of data. It is depended on the
consumers, where the estimation that exists and approaches do not lend themselves for the
purpose of modelling the temporary variations for big data. They lend their managers with
very less guidance on their decisions for allocating the resource. We have identified this gap
of the marketing mix efficiency which varies in evolution of the organization’s brand and
customer relationship and also the explicitly model. These temporary variations that uses a

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time varying effects model (TVEM), narrates the selection of customer’s self based in getting
marketing based communications on the number of this type of communications.
Kshetri (2014), has described in the article about the various costs, externalities and
benefits associated with the organization using big data. The different big data characteristics
are very closely related to the effects of security, security in privacy and welfare. The
security, privacy and welfare of the Big data effects varies across consumers with different
levels of sophistication, technological expertise and vulnerability and is very well described
by the author. This paper has intended to show the benefits of costs and externalities that are
related to the utilization of big data in the organizations. Particularly, it examines the
relationship between the different characteristics of big data that are inherent and the security
in privacy welfare and the consumer welfare. To evaluate from the point of view of data
sharing, data collection, data storage and accessibility, the relationship that has been
established between the characteristics of privacy and big data, consumer and security
welfare creating issues and are examined. The paper also discusses how the effects of big
data on privacy, security and welfare differ among consumers with different levels in
sophistication, technological knowledge and vulnerability.
Xu, Frankwick and Ramirez (2016), have introduced the study of fusion taxonomy to
relate and understand the relationships that are established between the old marketing
analytics (TMA), New Product Success (NPS), and Big Data Analytics (BDA). With a very
high speed of information that is carried in the digital economy by different stakeholders and
it is also shown that the taxonomy objectifies to help the companies that will build strategies
so that it can combine marketing and big data knowledge together. This study has suggested
that the fusion of knowledge is non-automatic in virtue of improving NPS and requires
strategic choices to achieve its benefits.

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Erevelles, Fukawa and Swayne (2016), has highlighted the firms which will attract
benefits from the big data that uses inductive reasoning than deductive reasoning for more
benefits. The authors have described about the RBT that provides overview of the
competitive advantage impact of Big Data. The added Ignorance helps to motivate the Big
Data’s desire to accumulate new information. The companies are enabled by radical
innovation through Big Data to create greater value. The authors have mentioned about the
Creative intensity that is essential to exploit Big Data's benefits.
Authors Akter et al. (2016), presented an analytics on big data capability model. The
improvement in the business strategy is done by the frame on firm performance using the the
entanglement view of the RBV. The author shows BDAC as the way of a hierarchical model
that contatins the three primary dimensions that is the management, technology and capacity
for eleven sub-dimensions that is the investment, planning, coordination, connectivity,
control, modularity, compatibility, knowledge of management on technology, business
knowledge and relational knowledge.
Gunasekaran et al. (2017), stated the importance of the predictive analysis of big data
in order to achieve business values and smooth performance. The author has made research
based views to address the objectives and results of big data on the society of commerce. It
conceptualizes the assimilation process which consists of three stages of routineisation that
includes, acceptance along with assimilation and the identification of a resource. This feature
influences Data Assimilation Capacity connectivity and information sharing within the
mediation effect of the top management engagement on big data and the capacity of the
assimilation of the big data. As a result the findings under the mediation effect of BDPA
assimilation, the routineization has suggested about the BDPA acceptance, which is related
positively to BDPA assimilation which is related to information sharing under the Top
Management Engagement mediation effect along with the connectivity features..

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Conclusion:
The literature review has provided information on the significance of the Big Data
Analytics in business enterprises and how it is describes by the authors. The research has
discussed the authors have described the analytical tools and their methods and significance
of how it can benefit small and medium sized enterprises in business. The review focuses on
the Big Data Analysis approach to linking operations and marketing capabilities.

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References
Abelló, A. (2015, October). Big data design. In Proceedings of the ACM Eighteenth
International Workshop on Data Warehousing and OLAP (pp. 35-38). ACM.
Authors Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to
improve firm performance using big data analytics capability and business strategy
alignment?. International Journal of Production Economics, 182, 113-131.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the
transformation of marketing. Journal of Business Research, 69(2), 897-904
George, G., Haas, M. R., & Pentlands, A. (2014). Big data and management.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., &
Akter, S. (2017). Big data and predictive analytics for supply chain and organizational
performance. Journal of Business Research, 70, 308-317.
Kshetri, N. (2014). Big data׳ s impact on privacy, security and consumer
welfare. Telecommunications Policy, 38(11), 1134-1145
Michael, K., & Miller, K. W. (2013). Big data: New opportunities and new challenges [guest
editors' introduction]. Computer, 46(6), 22-24.
Patil, H. K., & Seshadri, R. (2014, June). Big data security and privacy issues in healthcare.
In 2014 IEEE international congress on big data (pp. 762-765). IEEE.
Saboo, A. R., Kumar, V., & Park, I. (2016). Using Big Data to Model Time-Varying Effects
for Marketing Resource (Re) Allocation. MIS Quarterly, 40(4).

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Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can
make big impact: Findings from a systematic review and a longitudinal case
study. International Journal of Production Economics, 165, 234-246.
Wang, Gang, et al. "Big data analytics in logistics and supply chain management: Certain
investigations for research and applications." International Journal of Production
Economics176 (2016): 98-110.
Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional
marketing analytics on new product success: A knowledge fusion perspective. Journal
of Business Research, 69(5), 1562-1566.