Many forms of BDA exist to meet specific decision-support demands of different organizations. Three BDA analytical classes exist: 1) descriptive, dealing with straightforward questions regarding what is or has happened and why—with ‘opportunities and problems’ using descriptive statistics such as historical insights; 2) predictive, dealing with questions such as what will or is likely to happen, by exploring data patterns with relatively complex statistics, simulation, and machine-learning algorithms (e.g., to identify trends in sales activities, or forecast customer behavior and purchasing patterns); and 3) prescriptive, dealing with questions regarding what should be happening and how to influence it, using complex descriptive and predictive analytics with mathematical optimization, simulation, and machine-learning algorithms (e.g., many large-scale companies have adopted prescriptive analytics to optimize production or solve schedule and inventory management issues) [18]. Regardless of the type of BDA analysis performed, its application significantly impacts tangible and intangible resources within an organization.
2.1 Previous studies on BDA
BDA tools or techniques are used to analyze Big Data (such as social media or substantial transactional data) to support strategic decision-making [19] in different domains (e.g., tourism, supply chain, healthcare), and numerous studies have developed and evaluated BDA solutions to improve organization decision support. We categorize previous studies into two main groups based on non-technical aspects: those which relate to development of new BDA requirements and functionalities in a specific problem domain, and those which focus on more intrinsic aspects such as BDAC development or value-adding because of their impact on particular aspects of business. Examples of reviews focusing on technical or problem-solving aspects are detailed in Table 1.
Table 1: Example studies that focus technical or problem-solving aspects of BDA.
Source
|
Review method (# articles)
|
Key Results
|
[3]
|
(47) content analysis to discover issues
|
Importance of designing streaming analytics for big data found scalability, privacy, and load-balancing issues of big data technologies
|
[20]
|
(84) systematic literature review
|
Existing BDA mechanisms lead to competitive performance gains for building theory, aligning to resource-based and dynamic capabilities
|
[21]
|
(413) content analysis
|
A framework identifying supply chain functions with BDA models is developed
|
[22]
|
(67) systematic review
|
Organizations may realize values of Big Data, by analysis of two socio-technical features: portability and interconnectivity influence
|
[23]
|
(170) bibliometric analysis and systematic literature review
|
Created 4 clusters - big data and dynamic capabilities: big data and supply chain management, knowledge management, decision making, business process management and BDA, determined BDAC and organizational objectives to be aligned so organizations should develop new strategies for dynamic BDAC
|
[24]
|
(49) bibliometric and network analysis review
|
Identified clusters of Big Data to improve business processes in an organization
|
[25]
|
(109) descriptive review
|
Revealed how to establish BDAC for business transformation
|
[18]
|
(100) content analysis
|
Addressed Big Data issues, trends and views in Supply Chain Management (SCM) to spread Big Data value-adding perspective
|
The second literature group examines BDA in an organizational context, such as improving firm performance using big data analytics capability in specific business domains [26]. Studies that supports BDA leading to different aspects of organizational performance [20,25,27-30] (Table 2). Another research on BDA to improve data utilization and decision-support qualities. For example, [31] explained how BDAC may be developed to improve managerial decision-making processes, and [4] conducted a thematic analysis of 15 firms to identify the factors related to the success of BDA capability development in SCM.
Table 2: Examples of BDAC review studies
Source
|
Method (# online surveys)
|
Results
|
[4]
|
Thematic analysis: 14 firms,
|
Identified factors inhibiting organizational BDAC and maximizing its gains with BDA applications
|
[16]
|
Quantitative analysis (108) from 108 executive-level technology leaders
|
BDAC leads to organizational performance
|
[20]
|
Quantitative—202 technology leaders in Norwegian firms
|
Explained the advantages of BDAC to enable and support organization capability
|
[24]
|
Quantitative— (297) from Chinese IT managers
|
Determined BDAC to directly and indirectly impact firm performance
|
[25]
|
Quantitative—109 case description analysis
|
Revealed how to establish BDAC for business transformation
|
[26]
|
Quantitative (152)
|
Advances BDAC conceptualization and the role of Analytics Capability Business Strategy Alignment in enhancing organization’s performance
|
[27]
|
Quantitative analysis (306)
|
An organization’s intention for BDA and its competence for maintaining the quality of corporate data and decision making
|
[28]
|
Quantitative analysis (161)
|
Organizational level BDA use has significant impacts on two types of supply chain value creation: asset productivity and business growth
|
[29]
|
Quantitative (30)
|
Data and organization domains have a greater impact than technology and support domains
|
[31]
|
Qualitative: 3 exploratory case studies
|
Examined how BDA use enhanced operations, and identified links with operations performance
|
Potential applications of BDA
Many retail organizations use analytical approaches to gain a commercial advantage and organizational success [32]. Modern organizations increasingly invest in BDA projects to reduce costs, accurate decision making, and for future business planning. For example, Amazon was the first online retailer, and has maintained its position for innovative BDA improvement and use [32]. Examples of successful stories of BDA use in business sectors include.
- Retail: business organizations using BDA for dynamic (surge) pricing [33] to adjust product or service prices based on demand and supply. For instance, Amazon uses dynamic pricing to surge price in accordance with product demand.
- Hospitality: Marriott hotels—the largest hospitality agent with a rapidly increasing number of hotels and serviced customers—uses BDA to improve sales [34].
- Entertainment: Netflix uses BDA to retain clientele, and increase sales and profits [35,36].
- Transportation: Uber uses BDA [37] to capture Big Data from various consumers, and to identify the best routes to locations. ‘Uber eats,’ despite competing with other delivery companies, delivers foods in the shortest possible time.
- Food service: McDonalds continuously updates information with BDA, following a recent shift in food quality, now sells healthy food to consumers [38], and has adopted a dynamic menu [39].
- Finance: American Express has used BDA for a long time, and was one of the first companies to understand the benefits of using BDA to improve business performance [40]. Big Data are collected on the ways consumers make on- and offline purchases, and predictions are made as to how they will shop in the future.
- Manufacturing: General Electric manufactures and distributes products such as wind turbines, locomotives, airplane engines, and ship engines [41]. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., benefits can be brought to the existing power system including improving the customer service as well as the social welfare in the era of big data.
- Online business: music streaming websites are increasingly popular and continue to grow in size and scope because consumers want a customized streaming service [42]. Many streaming services (e.g., Apple Music, Spotify, Google Music) use various BDA applications to suggest new songs to consumers.
2.3 Organization value assessment with BDA
Specific performance measure must be established that rely on the number of organizational contextual factors such as - goal of the organization, external environment of the organization and organization itself. When looking at above contexts regarding the use of BDA to strengthen process innovation skills, it is important to note that the approach required to achieve positive results depends on the different combinations along with the area in which BDA deployed [43].
2.3.1 Organizational development and BDA
To assist organization decision making for growth, effective processes are required to perform operations such as continuous diagnosis, action planning, and the implementation and evaluation of BDA. Lewin’s Organizational Development (OD) theory regards processes as having a goal to transfer knowledge and skills to an organization, with the process being mainly to improve problem-solving capacity and to manage future change. Beckhard [44] defined OD as the internal dynamics of organization, which involve a collection of individuals working as a group to improve organizational effectiveness, capability, work performance, and the ability to adjust culture, policies, practices, and procedure requirements.
OD is ‘a system-wide application and transfer of behavioral science knowledge to the planned development, improvement, and reinforcement of the strategies, structures, and processes that lead to organization effectiveness’ [45], and has three concepts: organizational climate, culture and capability [46]. Organizational climate is ‘the mood or unique personality of an organization’ [46] which includes shared perceptions of policies, practices, and procedures; climate features also consist of leadership, communication, participative management, and role clarity. Organizational culture involves shared basic assumptions, values, norms, behavioral patterns, and artefacts, defined by [47] as ‘a pattern of shared basic assumptions that was learned by a group as it solved its problems of external adaptation and internal integration’ (p. 38). Organizational capacity (OC) implies the function of the organization, such as production of services or products, or maintenance of organizational operations, and has four components: resource acquisition, organization structure, production subsystem, and accomplishment [48]. Organizational culture and climate affect an organization’s capacity to operate adequately (Figure 1).
Research Methodology
Our systematic literature review presents a research process for analyzing and examining research, and to gather and evaluate it [49] in accordance with a PRISMA framework [50]. We use keywords to search for articles related to BDA application, following a five stage process.
Stage1: Design Development
We establish a research question to instruct the selection and search strategy, and analysis and synthesis process, defining the aim, scope and specific research goals following guidelines, procedures and policies of the Cochrane Handbook for Systematic Reviews of Intervention [51]. The design review process is directed by the research question: what are the consistent definitions of BDA, unique attributes, objections and business revolution including improve decision making process and organization performance with BDA? The below Table is created using the outcome of the search which performed using Keywords- Organizational BDAC, Big Data, BDA.
Table 3. Design Development stage
Science Direct
|
Web of Science
|
IEEE
|
Springer Link
|
Total
|
15,5518
|
8834
|
3235
|
63,000
|
230,587
|
Stage 2: Inclusion and elimination criteria
To maintain the nuances of a systematic review, we apply various inclusion and exclusion criteria to our search for research articles in four databases: Science Direct, Web of Science, IEEE (Institute of Electrical and Electronics Engineers), and Springer Link. Inclusion criteria include topics on ‘Big Data in Organization’ published between 2015 to 2021, in English. We use essential keywords to identify the most relevant articles, using truncation, wildcarding, and appropriate Boolean operators (Table 4).
Table 4. Inclusin and elimination criteria stage
Science Direct
|
Web of Science
|
IEEE
|
Springer Link
|
Total
|
107,067
|
7111
|
2471
|
30,000
|
146,649
|
Stage 3: Literature sources and search approach
Research articles are excluded on the bases of Keywords and Abstract, after which 8062 are retained (Table 5). The articles only selected which has Keywords such as Big Data, BDA, BDAC, and the Abstract only focused in Organizational domain.
Table 5. Literature sources and search approach stage
Science Direct
|
Web of Science
|
IEEE
|
Springer Link
|
Total
|
7735
|
46
|
22
|
259
|
8062
|
Stage 4: Assess quality of full papers
At this stage, for each of the 161 research articles that remained after stage 3 presented in Table 6, which was assessed independently by authors in terms of several quality criteria such as credibility, to assess whether the articles were well presented, relevance which was assess based on whether the articles were used in organizational domain.
Table 6. Quality examination stage
Science Direct
|
Web of Science
|
IEEE
|
Springer Link
|
Total
|
63
|
43
|
20
|
35
|
161
|
Stage 5: Literature extraction and synthesis process
At this stage, only journal articles and conference papers are selected. Articles for which full texts were not open access were excluded, reducing our references to 70 papers[1] (Table 7).
Table 7. Literature extraction and synthesis process stage
Science Direct
|
Web of Science
|
IEEE
|
Springer Link
|
Total
|
34
|
10
|
17
|
9
|
70
|
3.1 Meta-analysis of selected papers
Of the 70 papers satisfying our selection criteria, publication year and type (journal or conference paper) reveals an increasing trend in big data analytics over a last 6 years (Table 6). Additionally, journals produced more BDA papers than Conference proceedings (Figure 2) which may be effected during year 2020 -2021 because of COVID and would be fewer conference proceedings or publication were cancelled.
Of the 70 research articles, 6% were published in 2015, 13% (2016), 14% (2017), 16% (2018), 20% (2019), 21% (2020), and 10% (untill May 2021).