Impact of Big Data Analytics on Business

This assignment delves into the profound impact of big data analytics on contemporary businesses. It requires a comprehensive analysis of how big data is leveraged to make informed decisions, improve operational efficiency, enhance customer experiences, and gain a competitive edge. The assignment also examines the challenges associated with big data analytics, such as data security, privacy concerns, and the need for skilled professionals.

Running head: RESEARCH PROCESS
Research Process
Name of the Student
Name of the University
Author’s Note

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Executive Summary
This particular study has focused to make an in-depth overview about the importance of big data
and business intelligence in rendering organizational efficiency. After the emergence of
advanced technology business organizations are successfully able to maintain database
systematically. This particular study has focused to make a critical evaluation on how big data as
well as BI system plays a major significance in rendering the success of business. After identify
the research issue the study has made an in-depth theoretical analysis by involving the point of
views of eminent research scholars. An effective research methodology has also been conducted.
With the help of primary source of data collection technique the researcher has focused to make
an effective survey where 50 respondents have been involved. In addition, based on the result
and data analysis some of the relevant recommendations have also been provided.

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Table of Contents
1. Introduction..................................................................................................................................4
1.1 Purpose of the study...................................................................................................................4
1.2 Research scope:.........................................................................................................................5
1.3 Research background:................................................................................................................5
1.4 Research objectives:..................................................................................................................6
1.5 Key definitions:.........................................................................................................................6
1.6 Theories models and hypothesis:...............................................................................................7
2. Methodology:.............................................................................................................................10
2.1 Research design:......................................................................................................................10
2.2 Research project performed.....................................................................................................11
2.3 Data preparation.......................................................................................................................11
2.4 Models and concepts used.......................................................................................................12
2.5 Sequence of events..................................................................................................................12
2.6 Use of special tools..................................................................................................................13
2.7 Methods used for record data..................................................................................................13
2.8 Project procedure.....................................................................................................................13
2.9 Experimental procedure...........................................................................................................13
3. Result and discussion.................................................................................................................14
4. Conclusion:................................................................................................................................22
5. Recommendations:....................................................................................................................23
5.1 Operational recommendation:.................................................................................................24
5.2 Strategic recommendation:......................................................................................................24

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5.3 Future development in the research project:............................................................................24
5.4 How to improve the result:......................................................................................................25
Reference List:...............................................................................................................................26
Appendix: Gnatt Chart...................................................................................................................27

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1. Introduction
Business operations are going to change their mode of wings with the gradual progress of
advanced technology. At the very initial stage business experts intended to maintain their overall
data record with the help of advanced technology. This particular study has provided an in-depth
overview about the importance of big data and business intelligence in rendering
organizational efficiency. The primary function of big data is to store and possess data from
internally and externally. The concept of big data actually refers to the large volume of data that
is primarily available in online as well as in the cloud.
1.1 Purpose of the study
As per the point of view of Minelli, Chambers and Dhiraj (2012), sources of data are
diverse. Therefore, the data generally remains unstructured initially. With the help of big data the
individual experts can store the process the internal data resources as per the requirement. After
the emergence of big data the business organizations are able to maintain a dada record
systematically. Integrating advanced analytics for big data with the business intelligent system is
one of the most effective integration with the help of which the business executives are able to
run the entire business process systematically (Wamba et el., 2015). The overarching term
business intelligence implies generation, aggregation, analysis and visualizations. The primary
aim of business intelligence is to generate and gather data in order to store for maintaining proper
database (Zhou & Yang, 2016). However, the purpose of this particular research is to evaluate
the importance of big data and business intelligence for enhancing organizational efficiency.

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1.2 Research scope:
It has been observed that business organizations especially in the telecommunication
sectors are not able to maintain proper data record regarding the performance level of the
employees as well as on the profitability of business. Customers are not getting proper services
and response from the service providers (Zheng, Zhu & Lyu, 2013). In this kind of situation, the
purchasers intended to show their extreme level of reluctance in using the products and services.
Therefore, this particular topic has become a major issue for numerous practitioners based on
which they have conducted an effective research work.
After the emergence of big data and business intelligence people belonging to different
geographical markets are getting effective services. At the same time, the business executives are
successfully able to maintain internal data record systematically. Employees are getting justified
appreciation of their performance level (Gopalkrishnan et al., 2012). Consequently, the entire
process of business has become more systematic and rhythmic. Customers are getting services at
the proper time. The interpersonal communication between the business managers and the
employees has become effective due to transparency in overall data record (Edgeman, 2013).
1.3 Research background:
The technology is growing so fast in past few years that business experts do not have to
additional effort in maintaining overall database of organizational performance manually. In the
realm of data mining along with big data an individual is allowed to keep a constant control over
the entire process of business. Big data enables an individual in gathering, storing and visualizing
entire data record with the help of business intelligence (Kwon, Lee & Shin, 2014). In this kind
of situation, business executives do not have to put additional effort in maintaining the database.

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With the process of automation technology executives have to fill up a form. This particular
automation process automatically captures data and information. As a result, the business
executives do not have to apply their brain in maintaining the entire data record both internally as
well as externally (Hashem et al., 2016). Therefore, this particular research study has focused to
make an in-depth overview about the importance of big data and business intelligence for
enhancing business scope. At the same time, after the implementation of big data the
organizational executives are facing innumerable challenges as well. Therefore, this particular
study has focused to evaluate some of the major issues faced by employees after the
implementation of big data and business intelligence.
1.4 Research objectives:
This particular research aims to evaluate the importance of big data and business
intelligence in rendering organizational efficiency. The necessary objectives of this particular
study are as follows:
 To critically evaluate the importance of importance of big data and business intelligence
in rendering organizational efficiency
 To highlight the major factors that affect in implementing big data and business
intelligence within the organization process
 To provide some of the major recommendations on how to overcome of barriers of
implementing big data and BI system
1.5 Key definitions:
Big data:

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Big data is the technology based on which an individual can gather, store and process
internal as well as external data record of an organization. With the help of big data the business
experts tend to keep a constant record over the entire performance level of the employees (Ularu
et al., 2012). At the same time, the overall operational process is controlled with the help of big
data. Like the same way, the entire external record such as customers’ satisfaction feedback,
budget of marketing and promotion, business profitability is also maintained with the help of big
data analysis.
Business intelligence:
The overarching term business intelligence refers to the technology in broader way with
the help of which an efficient individual is able to analyze the captured data in different way.
With the help of this analysis the business managers tend to evaluate their organizational
performance (Chen & Zhang, 2014). Therefore, decision making process of the employees is
highly dependent on this analytical procedure.
Data mining:
Data mining is the systematic process of shifting necessary data and information with the
evidence of recognized patterns that is already captured earlier. While making a business
evaluating before taking strategic decision the role of data mining is highly significant.
1.6 Theories models and hypothesis:
The theory of value chain focuses on three major concept (Mahmood & Afzal, 2013).
Value chain is the systematic approach in examining the development of competitive advantages.
With the concept of business intelligence value chain focuses an in-depth evaluation from data to

Data
Information
Knowledge
Profit
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profit. Vossen (2014) emphasized that after gathering necessary data and information BI enables
to store that data in order to evaluate its major risk factors. If an individual business expert
intends to analyze the organizational profitability BI system helps that individual in evaluating
data by providing a clear analytical representation. In this case big data helps to gather and store
that particular information so that the executives get a proper scope of data analysis. As per the
opinion of Ohlhorst (2012), providing quality of product and services is not the primary role of a
particular business organization. In order to maintain business sustainability an individual has to
focus on collecting necessary information regarding customers’ feedback. Stakeholders’ analysis
and their current needs and demands are highly important for maintaining the performance level
of the employees. In this kind of situation, the importance of big data is undeniable. This
particular advanced technology helps an individual in collecting necessary data in order to store
and analyze.
Figure 1: External value chain process

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(Source: Created by the author)
In order to evaluate business profitability the organization has to follow a proper value
chain process. Géczy (2014) stated that after collecting necessary data and information regarding
the sales growth the information analysts intend to store that data. With the help of business
intelligence system the organization evaluate that particular data in comparison to the
competitors’ market scenario. As a result, the business executives get an in-depth overview about
the competitors’ market position. Like the same way, value chain theory helps to analyze the
internal performance level of the organization. Based on the data collected from performance
management system a particular data record is maintained. On the other hand, the information
analysts intend to gather a systematic report on the overall performance of other business
enterprises. Imran Zoha & Abu-Dayya (2014) opined that the performance record of other
business enterprises helps the business analysts to evaluate on how the organizational employees
should perform. The areas of improvement can be identified with the business performance level
of other organizations. Therefore, in order to enhance the performance level of the employees the
organization should focus on big data analysis by applying value chain theory.

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Figure 2: BI internal value chain Process
(Source: Dean, 2014)
2. Methodology:
Research methodology is the systematic process of collecting necessary data and
information from various reliable resources. For this specific study the researcher has used
several methodological tools for accomplishing the entire process of research work
systematically (Dhar, 2013). In this specific study the methodology has focused to discuss a
systematic research design for conducting the research issue from various perspectives.
2.1 Research design:
Research design is the overall representation on how the entire research work has been
performed. With the help of different methodological tools the research work has been critically
Business performance Enterprise Report Business Analysis

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evaluated. In the specific study the researcher has focused to use positivism research philosophy
with the help of deductive research approach along with descriptive research design. Positivism
research philosophy is effective for making a keen observation about the dependent and
independent variable of research issue (Calof, Richards & Smith, 2015). As per the opinion of
different positivists observation is repeatable but the phenomenon is isolated. With the help of
keen observation the research is able to analyze the data from various perspectives. Deductive
approach is the systematic process that helps to evaluate data from various case studies. In this
specific research data has been evaluated by using descriptive research design for explaining the
research variables from different point of view. These specific methodological tools are highly
beneficial for evaluating the necessary impacts of big data and business intelligence in rendering
organizational efficiency (Peppard & Ward, 2016). .
2.2 Research project performed
In order to perform the research project the researcher in this specific study has focused
to use those methodology tools in various ways. Use of deductive approach along with
descriptive research design is highly significant (Jagadish et al., 2014). With the help of this
specific research approach the researcher has collected necessary information on how big data is
possessed with a major impact on the overall performance level of the organization.
2.3 Data preparation
Before conducting the research work an individual researcher has to collect various data
from different authentic resources. Data collection resource is possessed with two major types
including primary source of research and secondary source of research work (Obeidat et al.,
2015). Primary source of research work enables in gathering necessary information from survey

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and interview process. On the other hand, secondary source of data is collected from books and
magazines, websites and so on. However, this particular study has focused to use primary source
of research with the help of which questions have been formed based on research issue.
Participants have taken part in providing necessary response regarding the research issue
(Bhimani & Willcocks, 2014). In order to use primary source of data collection procedure the
study has used 50 respondents from business organizations in order to collect their own opinion.
2.4 Models and concepts used
The primary models and concepts that have been used in this specific study for analyzing
the research issue include value chain process. In this particular model the researcher has
skillfully developed the entire procedure on how the BI system enables a business organization
in evaluating internal and external information from various perspectives (Clarke & Margetts,
2014). On one hand, the internal business performance data has been evaluated. On the other
hand, value chain process helps to analyze the external data as well.
2.5 Sequence of events
In order to conduct the research methodology the research has focused to make an in-
depth research design. In this specific research design the study has focused to evaluate the
usefulness of appropriate methodological tools (Zulkernine et al., 2013). With the help of this
methodological tools research issue has been depicted from different perspectives. After
designing the research tool the researcher has focused on data collection procedure for
accomplishing the entire research work. The methodology consists of analyzing the ethical
considerations as well along with highlighting research limitations.

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2.6 Use of special tools
In order to evaluate necessary data and information the researcher has focused to conduct
statistical tools (Garber, 2012). With the help of graphical representation the researcher has
provided a clear and in-depth result on research issue. The response of the participants has been
presented with graphical representation.
2.7 Methods used for record data
In order to conduct the entire research work the study has focused to select quantitative
method for analyzing data and information. Quantitative data analysis represents a direct
interaction with participants (Acito & Khatri, 2014). On the other hand, the researcher gets the
scope of getting an immediate response from the participants. People belonging to different
geographical boundaries associated with the business organization can take part in survey. The
response has been collected from those participants as well.
2.8 Project procedure
The entire project is conducted by maintaining a particular time horizon which is
otherwise named as gnat chart. (The time horizon is attached in appendix)
2.9 Experimental procedure
In order to conduct the entire methodology the researcher has focused some of the major
issues. While collecting necessary response from the participants the researcher has to face
challenges in gathering data. Large numbers of participants are there who are having linguistic
problem. In this kind of situation, they have filled up the survey from without understating its
essence (Olszak & Batko, 2012). On the other hand, it has also been observed that people are

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intending to provide biased response. As a result, it was difficult for the business executives for
evaluating accurate data.
3. Result and discussion
How far do you agree that big data is future technology in maintaining chronological data
record?
Responses Frequency Percentage
Strongly agree 31 62
Agree 10 20
Indifferent 2 4
Disagree 3 6
Strongly disagree 4 8
62
20
4 6 8
Percentage
Strongly agree
Agree
Indifferent
Disagree
Strongly disagree
Figure : Big data is future technology in maintaining chronological data record

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Findings and analysis:
This question has focused on highlighting the significance of big data in present business
environment. With time, several new technologies have been adopted by many business units for
achieving sustainable growth in the market. The outcome of this question will help to highlight
the superiority of big data analysis from other alternative techniques. The findings have
highlighted that 62% of the respondents strongly agreed that big data is an advance tool, which is
formulated considering future business needs and wants. Moreover, 20% of the respondents have
agreed with the same fact. On the contrary, only 8% of the respondents have strongly disagreed
with the fact that big data is not a future technology. Therefore, the analysis has highlighted big
data is likely to gain more popularity in near future within several organizations. In fact, both
small and large size organizations are likely to initiate big data technology for gaining success in
the market.
How far do you agree that big data implementation is useful for enhancing the quality of
decision-making process?
Responses Frequency Percentage
Strongly agree 28 56
Agree 8 16
Indifferent 3 6
Disagree 6 12
Strongly disagree 5 10

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Strongly agree
Agree
Indifferent
Disagree
Strongly disagree
0 10 20 30 40 50 60
56
16
6
12
10
Percentage
Figure : Big data implementation is useful for enhancing the quality of decision-making
process
Findings and analysis:
In this question, the focus is provided on evaluating the way big data has changed the
way leaders took different management decisions. In present time, every organization is heavily
depending on numeric value or fact before taking any critical decision about the business
procedure. Here, the analysis has highlighted the fact that 56% of the total respondents have
strongly agreed with the fact that big data enhances the overall quality of the decision-making
process. Moreover, 16% of the respondents have also expressed the same argument. On the other
hand, only 10% of the respondents have disagreed with the fact argument. It highlights the fact
that proper utilization of big data technique can allow managers to identify critical trends related
with business process. Therefore, it helps to identify several risk factors associated with the
operational procedure, which will directly create positive impact on the decision-making
procedure.

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How far do you agree that big data implementation required for securing the confidential
information?
Responses Frequency Percentage
Strongly agree 32 64
Agree 9 18
Indifferent 1 2
Disagree 5 10
Strongly disagree 3 6
64
18
2
10
6
Percentage
Strongly agree
Agree
Indifferent
Disagree
Strongly disagree
Figure : Big data implementation required for securing the confidential information
Findings and analysis:
This question has focused on evaluating one of the most contemporary issues of securing
confidential information. Now, majority of the big data use cloud based software for analyzing
all the provided information. Here, the evaluation has highlighted that 64% of the respondents

18RESEARCH PROCESS
have strongly agreed with the fact that big data secures all the confidential information in a major
way. On the contrary, only 6% of the respondents have strongly disagreed with the same
argument. Recently, several cases of hacking have been identified in the present market due to
lack of security. However, implementation of big data technique on cloud-based software limits
the threat of information breach in a major way. In fact, it provides different level of security for
the organizations to eliminate any possibility of information misuse. Thus, the outcome of the
analysis has established the fact that implementation of big data secure confidential information.
How far do you agree that initiation of big data reduces business related risks in a major
way?
Responses Frequency Percentage
Strongly agree 33 66
Agree 6 12
Indifferent 3 6
Disagree 5 10
Strongly disagree 3 6

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Strongly
agree Agree Indifferent Disagree Strongly
disagree
0
10
20
30
40
50
60
70 66
12
6 10 6
Percentage
Figure : Initiation of big data reduces business related risks in a major way
Findings and analysis:
This question focuses on evaluating the amount of impact implementation of big data has
created on the several business risk factors. The analysis has highlighted that 66% of the total
respondents have strongly agreed with the fact that proper utilization of big data reduces business
related risks. Moreover, 12% of the total respondents have agreed with the same argument. On
the contrary, only 16% of the total respondents have disagreed or disagreed with the argument.
This has established the fact that proper use of big data allows businesses to utilize the numbers
or trends from previous business practices. Moreover, it is also likely to help businesses in
evaluating the way all the previous risk factors has been countered. Therefore, it is likely to
provide a clear idea regarding the best possible way to counter all the risky factors associated
with the business procedure.
How far do you agree that introduction of big data enhances the quality of the business
intelligence practices?

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Responses Frequency Percentage
Strongly agree 35 70
Agree 6 12
Indifferent 2 4
Disagree 4 8
Strongly disagree 3 6
70
12
4 8 6
Percentage
Strongly agree
Agree
Indifferent
Disagree
Strongly disagree
Figure : Introduction of big data enhances the quality of the business intelligence practices
Findings and analysis:
This question focuses on evaluating the contribution of big data on several business
intelligence practices. Now, business intelligence includes all the applications and technologies
used for integrating and presenting business information. The analysis highlighted that 70% of
the total respondents strongly agreed with the fact that big data is the prime tool for business
intelligence practices. On the other hand, only 6% respondents have strongly disagreed with the

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same argument. Proper utilization of big data not only helps businesses to store all the important
business data but also helps to evaluate to make proper information. Big data analysis also help
businesses to identify yearly trends related to several business activities in a major way.
Therefore, it increases the efficiency level of the management decision. Moreover, several
businesses have used big data as a tool for analyzing future trends related with different market
related challenges in a comprehensive manner.
4. Conclusion:
The importance of big data and business intelligence is undeniable belonging to the
current scenario of business industry. Big data enables one to gather and restore necessary data
systematically as well as chronologically. In this kind of situation, the overall performance
record of the business organization has been maintained for evaluating organizational
profitability. This particular study has provided in-depth overview on how the business
executives have rendered business success after the implementation of business intelligence for
running an organization properly. While conducting an in-depth research work on the
importance of big data and business intelligence in rendering organizational efficiency the
study has focused to find out various research sources as well from where accurate and relevant
data has been collected. Apart from evaluating the importance of big data and business
intelligence in rendering organizational efficiency the primary objective of this study is to
highlight the major factors that affect in implementing big data and business intelligence within
the organization process as well. It has been observed that due to the lack of proper training the
organizational employees are unable to handle proper data and information with the help of big
data and business intelligence system.

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In this specific research work an effective theoretical concepts have been designed in the
form of value chain process. The researcher in this specific study has focused to make the value
chain procedure based on internal as well as external data of the organization. This particular
study has focused to make an in-depth overview on how methodological tools are having major
impact in evaluating necessary data and information. People belonging to different geographical
boundaries have participated in the data collection procedure. Therefore, the researcher has to
face barriers in communicating with those individuals. In the survey procedure an immediate
response from the participants has been collected. At the same time, while conducting the
research an individual intends to focus on some ethical considerations. As per the ethical
considerations researcher should never force an individual employee for providing information
regarding the research issue. Respondents should participate spontaneously in providing
necessary response regarding the various aspects of research work. This particular study has
focused to make an in-depth overview on how data from various respondents should be collected
without being biased. While conducting the entire research work it has been observed that only
primary source of data collection method is not sufficient enough for collecting necessary
information regarding the research issue.
5. Recommendations:
While conducting the entire research some of the major issues have been highlighted.
Employees are not efficient enough in dealing with the continuous process of big data. In order
to operate big data as well as business intelligence the employees need to have proper skill and
competency on the advancement of technology (Zhou & Yang, 2016). Otherwise the entire
process of business would become failure. In this kind of situation, the business experts should

23RESEARCH PROCESS
take some of the major initiatives with the help of which employees can be motivated towards
handling BI system along with big data. The recommendations are as follows:
5.1 Operational recommendation:
In order to make the entire business process stronger the employers are recommended to
recruit IT analysts within the operation process who will be able to keep a constant control over
the entire process of business (Sodenkamp, Kozlovskiy & Staake, 2015). On the other hand, if
the human resource managers tend to appoint IT analyst within the operation process the
organization would not have to invest money for training and development session in order to
enhance the technological skill of the employees. At the same time, IT analysts would be able to
handle any kind of crisis related technological error. Therefore, appointing IT analysts within the
operation process can be an effective solution.
5.2 Strategic recommendation:
It has been observed that due to the lack of proper technological equipments the
organizations fail to maintain big data properly (Picciano, 2012). Therefore, it can be
recommended that business experts should enhance technological equipments so that database
can be maintained properly. On the other hand, the IT analysts can be appointed in the role of a
trainer as well. With the help of this particular strategy employees having least competency in
operation process of technology can get proper help and support from those individuals
(Edgeman, 2013).

24RESEARCH PROCESS
5.3 Future development in the research project:
The entire research project has not focused on literature review by involving the opinion
of some of the eminent scholars. The researcher has evaluated the literature by focusing on a
particular theory. Therefore, this specific area can be developed in future. The importance of big
data and business intelligence in rendering organizational efficiency can be evaluated more
critically by presenting the opinion of different eminent scholars. Therefore, the literature review
part could have been presented in an in-depth way. At the same time, it has been also been
observed that the researcher has selected limited number of respondents for conducting the data
collection method.
5.4 How to improve the result:
In order to improve the result, the researcher could have focused on some of the major
areas for getting a better outcome. In the research methodology chapter, post positivism
philosophy can be used instead of only positivism approach. Post positivism philosophy is highly
dependent on observation along with proper evidence and data. Therefore, this particular
research philosophy could have used for accomplishing the entire research work more
effectively. At the same time, the researcher in this specific research work may focus on
collecting data from journals and articles as well. Only primary method of data collection
procedure is not effective enough in gathering sufficient data and information. Therefore, the
researcher can focus on secondary source of data collection procedure as well.

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Reference List:
Acito, F., & Khatri, V. (2014). Business analytics: Why now and what next?.
Ayankoya, K., Calitz, A., & Greyling, J. (2014, September). Intrinsic relations between data
science, big data, business analytics and datafication. In Proceedings of the Southern
African Institute for Computer Scientist and Information Technologists Annual
Conference 2014 on SAICSIT 2014 Empowered by Technology (p. 192). ACM.
Bhimani, A., & Willcocks, L. (2014). Digitisation,‘Big Data’and the transformation of
accounting information. Accounting and Business Research, 44(4), 469-490.
Calof, J., Richards, G., & Smith, J. (2015). Foresight, competitive intelligence and business
analytics—tools for making industrial programmes more efficient. Foresight-
Russia, 9(1), 68-81.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Clarke, A., & Margetts, H. (2014). Governments and citizens getting to know each other? Open,
closed, and big data in public management reform. Policy & Internet, 6(4), 393-417.
Cukier, K., & Mayer-Schoenberger, V. (2013). The rise of big data: How it's changing the way
we think about the world. Foreign Aff., 92, 28.
Dean, J. (2014). Big data, data mining, and machine learning: value creation for business leaders
and practitioners. John Wiley & Sons.
Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73.

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Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., & Papadopoulos, T. (2016). The
impact of big data on world-class sustainable manufacturing. The International Journal of
Advanced Manufacturing Technology, 84(1-4), 631-645.
Edgeman, R. (2013). Sustainable enterprise excellence: towards a framework for holistic data-
analytics. Corporate Governance, 13(5), 527-540.
Garber, L. (2012). Using in-memory analytics to quickly crunch big data. Computer, 45(10), 16-
18.
Géczy, P. (2014). Big data characteristics. The Macrotheme Review, 3(6), 94-104.
Gopalkrishnan, V., Steier, D., Lewis, H., & Guszcza, J. (2012, August). Big data, big business:
bridging the gap. In Proceedings of the 1st International Workshop on Big Data, Streams
and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and
Applications (pp. 7-11). ACM.
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., ... & Chiroma, H.
(2016). The role of big data in smart city. International Journal of Information
Management, 36(5), 748-758.
Imran, A., Zoha, A., & Abu-Dayya, A. (2014). Challenges in 5G: how to empower SON with big
data for enabling 5G. IEEE Network, 28(6), 27-33.
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R.,
& Shahabi, C. (2014). Big data and its technical challenges. Communications of the
ACM, 57(7), 86-94.

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Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and
acquisition intention of big data analytics. International Journal of Information
Management, 34(3), 387-394.
Mahmood, T., & Afzal, U. (2013, December). Security Analytics: Big Data Analytics for
cybersecurity: A review of trends, techniques and tools. In Information assurance (ncia),
2013 2nd national conference on (pp. 129-134). IEEE.
Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: emerging business
intelligence and analytic trends for today's businesses. John Wiley & Sons.
Obeidat, M., North, M., Richardson, R., Rattanak, V., & North, S. (2015). Business intelligence
technology, applications, and trends. International Management Review, 11(2), 47.
Ohlhorst, F. J. (2012). Big data analytics: turning big data into big money. John Wiley & Sons.
Olszak, C. M., & Batko, K. (2012, September). The use of business intelligence systems in
healthcare organizations in Poland. In Computer Science and Information Systems
(FedCSIS), 2012 Federated Conference on (pp. 969-976). IEEE.
Peppard, J., & Ward, J. (2016). The strategic management of information systems: Building a
digital strategy. John Wiley & Sons.
Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher
education. Journal of Asynchronous Learning Networks, 16(3), 9-20.
Sodenkamp, M., Kozlovskiy, I., & Staake, T. (2015). Gaining is business value through big data
analytics: a case study of the energy sector.

28RESEARCH PROCESS
Ularu, E. G., Puican, F. C., Apostu, A., & Velicanu, M. (2012). Perspectives on big data and big
data analytics. Database Systems Journal, 3(4), 3-14.
Vossen, G. (2014). Big data as the new enabler in business and other intelligence. Vietnam
Journal of Computer Science, 1(1), 3-14.
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.
Zheng, Z., Zhu, J., & Lyu, M. R. (2013, June). Service-generated big data and big data-as-a-
service: an overview. In Big Data (BigData Congress), 2013 IEEE International Congress
on (pp. 403-410). IEEE.
Zhou, K., & Yang, S. (2016). Understanding household energy consumption behavior: The
contribution of energy big data analytics. Renewable and Sustainable Energy
Reviews, 56, 810-819.
Zulkernine, F., Martin, P., Zou, Y., Bauer, M., Gwadry-Sridhar, F., & Aboulnaga, A. (2013,
June). Towards cloud-based analytics-as-a-service (claaas) for big data analytics in the
cloud. In Big Data (BigData Congress), 2013 IEEE International Congress on (pp. 62-
69). IEEE.

29RESEARCH PROCESS
Appendix: Gnatt Chart
Main activities/ stages Month
February
Month
March
Month
April
Month
May
Month
June
Month
July
Topic Selection 
Data collection from primary
sources
 
Framing layout of the research 
Theories and conceepts   
Formation of the research Plan  
Selection of the Appropriate
Research Techniques
 
Primary data collection  
Analysis & Interpretation of
Data Collection
 
Conclusion of the Study 
Formation of Rough Draft 
Submission of Final Work 