Critical analysis of Big Data challenges and analytical methods

doi:10.1016/J.JBUSRES.2016.08.001

Home
/
Papers
/
Critical analysis of Big Data challenges and analytical methods

Journal Article•DOI•

Critical analysis of Big Data challenges and analytical methods

Uthayasankar Sivarajah¹, Muhammad Kamal¹, Zahir Irani¹, Vishanth Weerakkody¹•Institutions (1)

Brunel University London¹

01 Jan 2017-Journal of Business Research (Elsevier)-Vol. 70, pp 263-286

TL;DR: In this article, the authors present 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.

read less

About: This article is published in Journal of Business Research.The article was published on 2017-01-01 and is currently open access. It has received 1267 citations till now. The article focuses on the topics: Analytics & Big data.

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices

[...]

Abdul-Nasser El-Kassar¹, Sanjay Kumar Singh²•Institutions (2)

Lebanese American University¹, Abu Dhabi University²

01 Jan 2018-Technological Forecasting and Social Change

TL;DR: In this article, the authors developed and tested a holistic model that depicts and examines the relationships among green innovation, its drivers, as well as factors that help overcome the technological challenges and influence the performance and competitive advantage of the firm.

...read moreread less

515 citations

Journal Article•DOI•

A survey towards an integration of big data analytics to big insights for value-creation

[...]

Mandeep Kaur Saggi¹, Sushma Jain¹•Institutions (1)

Thapar University¹

01 Sep 2018-Information Processing and Management

TL;DR: This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies and presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city.

...read moreread less

Abstract: Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

...read moreread less

274 citations

Journal Article•DOI•

Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review

[...]

Assunta Di Vaio¹, Rosa Palladino¹, Rohail Hassan², Octavio Escobar•Institutions (2)

University of Naples Federico II¹, Universiti Utara Malaysia²

01 Dec 2020-Journal of Business Research

TL;DR: In this article, the authors investigated the role of Artificial Intelligence (AI) in the construction of sustainable business models (SBMs) and provided a quantitative overview of the academic literature that constitutes the field.

...read moreread less

269 citations

Journal Article•DOI•

Big data analytics as an operational excellence approach to enhance sustainable supply chain performance

[...]

Surajit Bag¹, Lincoln C. Wood², Lincoln C. Wood³, Lei Xu⁴, Pavitra Dhamija¹, Yasanur Kayikci - Show less +2 more•Institutions (4)

University of Johannesburg¹, University of Otago², University of Western Australia³, Civil Aviation University of China⁴

01 Feb 2020-Resources Conservation and Recycling

TL;DR: In this paper, the authors used dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance, and identified two pathways that managers can use to improve sustainable supply-chain outcomes in the mining industry based on big data analytics capabilities.

...read moreread less

Abstract: Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.

...read moreread less

255 citations

Journal Article•DOI•

Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities

[...]

Surajit Bag¹, Jan Ham Christiaan Pretorius¹, Shivam Gupta², Yogesh K. Dwivedi³•Institutions (3)

University of Johannesburg¹, NEOMA Business School², Swansea University³

01 Feb 2021-Technological Forecasting and Social Change

TL;DR: In this article, the authors employed institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities.

...read moreread less

236 citations

1
2
3
4
…
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200

Collapse

References

PDF

Open Access

More filters

Posted Content•

Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review

[...]

David Tranfield¹, David Denyer¹, Palminder Smart¹•Institutions (1)

Cranfield University¹

19 Oct 2003-Social Science Research Network

TL;DR: The extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research is evaluated.

...read moreread less

Abstract: Undertaking a review of the literature is an important part of any research project. The researcher both maps and assesses the relevant intellectual territory in order to specify a research question which will further develop the knowledge base. However, traditional 'narrative' reviews frequently lack thoroughness, and in many cases are not undertaken as genuine pieces of investigatory science. Consequently they can lack a means for making sense of what the collection of studies is saying. These reviews can be biased by the researcher and often lack rigour. Furthermore, the use of reviews of the available evidence to provide insights and guidance for intervention into operational needs of practitioners and policymakers has largely been of secondary importance. For practitioners, making sense of a mass of often-contradictory evidence has become progressively harder. The quality of evidence underpinning decision-making and action has been questioned, for inadequate or incomplete evidence seriously impedes policy formulation and implementation. In exploring ways in which evidence-informed management reviews might be achieved, the authors evaluate the process of systematic review used in the medical sciences. Over the last fifteen years, medical science has attempted to improve the review process by synthesizing research in a systematic, transparent, and reproducible manner with the twin aims of enhancing the knowledge base and informing policymaking and practice. This paper evaluates the extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research. The paper highlights the challenges in developing an appropriate methodology.

...read moreread less

7,368 citations

"Critical analysis of Big Data chall..." refers methods in this paper

...In this paper, the authors commenced this systematic search by using an established detailed reviewprotocol based on the guidingprinciples and procedures of the SLR (Tranfield et al., 2003; Kitchenham & Charters, 2007)....
[...]
...The research protocol (phase I.3) In this paper, the authors commenced this systematic search by using an established detailed reviewprotocol based on the guidingprinciples and procedures of the SLR (Tranfield et al., 2003; Kitchenham & Charters, 2007)....
[...]
...Following Tranfield et al. (2003) and Kitchenham and Charters (2007) Systematic Review Approach, this paper extracted and reviewed 227 journal articles from 1996 to 2015 from the Scopus database – as a result fulfilling the aim of this literature review paper (as indicated in Section 1.1)....
[...]
...In the interest of parsimony, a meticulous though not exhaustive SLR was carried out through following a three-phase approach as described by Tranfield et al. (2003) and Kitchenham and Charters (2007) and diagrammatically illustrated in Fig....
[...]

Journal Article•DOI•

Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review

[...]

David Tranfield¹, David Denyer¹, Palminder Smart•Institutions (1)

Cranfield University¹

01 Sep 2003-British Journal of Management

TL;DR: In this article, the authors evaluate the process of systematic review used in the medical sciences to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research and highlight the challenges in developing an appropriate methodology.

...read moreread less

Abstract: Undertaking a review of the literature is an important part of any research project. The researcher both maps and assesses the relevant intellectual territory in order to specify a research question which will further develop the knowledge hase. However, traditional 'narrative' reviews frequently lack thoroughness, and in many cases are not undertaken as genuine pieces of investigatory science. Consequently they can lack a means for making sense of what the collection of studies is saying. These reviews can he hiased by the researcher and often lack rigour. Furthermore, the use of reviews of the available evidence to provide insights and guidance for intervention into operational needs of practitioners and policymakers has largely been of secondary importance. For practitioners, making sense of a mass of often-contrad ictory evidence has hecome progressively harder. The quality of evidence underpinning decision-making and action has heen questioned, for inadequate or incomplete evidence seriously impedes policy formulation and implementation. In exploring ways in which evidence-informed management reviews might be achieved, the authors evaluate the process of systematic review used in the medical sciences. Over the last fifteen years, medical science has attempted to improve the review process hy synthesizing research in a systematic, transparent, and reproducihie manner with the twin aims of enhancing the knowledge hase and informing policymaking and practice. This paper evaluates the extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research. The paper highlights the challenges in developing an appropriate methodology.

...read moreread less

7,020 citations

Journal Article•DOI•

Business intelligence and analytics: from big data to big impact

[...]

Hsinchun Chen¹, Roger H. L. Chiang², Veda C. Storey³•Institutions (3)

University of Arizona¹, University of Cincinnati², J. Mack Robinson College of Business³

01 Dec 2012-Management Information Systems Quarterly

TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.

...read moreread less

Abstract: Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

...read moreread less

4,610 citations

"Critical analysis of Big Data chall..." refers background or methods in this paper

...…various BD analytics methods, the SLR highlights that there are a number of off the shelf software tools [e.g. Hadoop, MapRecuce, Dyrad] (Chen, Chen et al., 2012; Chen, Chiang et al., 2012; Jiang et al., 2015), that have been built using and extending off-theshelf existing software [e.g.…...
[...]
...Advocates of BD and BDA perceive that in identifying a better way to mine and clean the BD can result in big impact and value (Chen, Chen et al., 2012)....
[...]
...Regardless, the rapid increase in the articles highlights the awareness and importance of this area among the academic community, practitioners, and even governments worldwide (see e.g. Chen, Chen et al., 2012; Chen, Chiang et al., 2012; Joseph & Johnson, 2013)....
[...]
...…articles clearly indicate that conducting experiments and simulations and or proposing algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications (Chen, Chen et al., 2012; Chen, Chiang et al., 2012)....
[...]

Journal Article•DOI•

Critical questions for big data

[...]

danah boyd¹, Kate Crawford¹•Institutions (1)

Microsoft¹

25 May 2012-Information, Communication & Society

TL;DR: The era of Big Data has begun as discussed by the authors, where diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people.

...read moreread less

Abstract: The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-tech...

...read moreread less

3,955 citations

"Critical analysis of Big Data chall..." refers background in this paper

...…challenges have paid attention to the difficulties of understanding the notion of BD (Hargittai, 2015), decision-making of what data are generated and collected (Crawford, 2013), issues of privacy (Lazer et al., 2009) and ethical considerations relevant to mining such data (Boyd & Crawford, 2012)....
[...]

Journal Article•DOI•

Beyond the hype

[...]

Amir H. Gandomi¹, Murtaza Haider¹•Institutions (1)

Ryerson University¹

01 Apr 2015-International Journal of Information Management

TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.

...read moreread less

2,962 citations

"Critical analysis of Big Data chall..." refers background or methods in this paper

...On the other hand, the challenges are significant such as data integration complexities (Gandomi & Haider, 2015), lack of skilled personal and sufficient resources (Kim, Trimi, & Chung, 2014), data security and privacy issues (Barnaghi, Sheth, & Henson, 2013), inadequate infrastructure and…...
[...]
...…say 3Vs [volume, velocity and variety] of data (e.g. Shah, Rabhi, & Ray, 2015), others reported 4Vs [volume, velocity, variety, and variability] of data (e.g. Liao, Yin, Huang, & Sheng, 2014) and 6Vs [volume, velocity, variety, veracity, variability, and value] of data (Gandomi & Haider, 2015)....
[...]
...Gandomi and Haider (2015) asserts the need to develop new solutions for predictive analytics for structured BD. Predictive analytics are principally based on statistical methods and seeks to uncover patterns and capture relationships in data....
[...]
...Big Data analytical methods – related to Q2 To facilitate evidence-based decision-making, organizations need efficient methods to process large volumes of assorted data into meaningful comprehensions (Gandomi & Haider, 2015)....
[...]
...Thus, the necessity to deal with inaccurate and ambiguous data is another facet of BD, which is addressed using tools and analytics developed for management and mining of unreliable data (Gandomi & Haider, 2015)....
[...]