Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)
Abstract
:1. Introduction
2. Methodology
2.1. Data Source
2.2. Analysis Tools
3. Results and Discussions
3.1. Trends in the Literature
3.2. Analysis of Countries and Institutions
3.3. Analysis of Cited Journals, Cited Authors, and Cited References
3.4. Analysis of Categories, Hotspots, and Burst Topic Historic Trends
3.5. Discussions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ranking | Country | Count | Centrality |
---|---|---|---|
1 | USA/The United States | 93/39 | 0.1/0.09 |
2 | India | 76 | 0.08 |
3 | China/Republic of China | 71/55 | 0.02/0.03 |
4 | England | 54 | 0.11 |
5 | Germany | 38 | 0.04 |
Ranking | Institutions | Count | Centrality |
---|---|---|---|
1 | Department of Computer Science, University of Nevada, Las Vegas, NV, the United States. | 16 | 0 |
2 | Swansea University, UK | 6 | 0 |
3 | University College London (UCL) | 6 | 0 |
4 | Nanjing University, China | 5 | 0 |
5 | National Institute of Industrial Engineering (NITIE), Mumbai, Maharastra | 4 | 0 |
Ranking | Journals | Count | Centrality | Impact Factor (2021) |
---|---|---|---|---|
1 | Management Science | 82 | 0.12 | 5.04 |
2 | MIS Quarterly | 130 | 0.09 | 7.198 |
3 | Harvard Business Review | 103 | 0.09 | 1.66 |
4 | Decision Support Systems | 124 | 0.08 | 7.04 |
5 | European Journal of Operational Research | 83 | 0.07 | 6.02 |
Ranking | Authors | Count | Centrality | Involved Publications |
---|---|---|---|---|
1 | Davenport, T.H. | 71 | 0.1 | Big data: the management revolution [32]. |
2 | Chiang, Roger HL | 5 | 0.07 | Strategic value of big data and business analytics [33]. |
3 | Dubey, Rameshwar | 45 | 0.06 | Education and training for successful careers in big data and business analytics [34]. |
4 | McAfee, Andrew | 39 | 0.06 | Big data: the management revolution [32]. |
5 | Davenport, Thomas H | 20 | 0.06 | Data scientist [35]. |
Ranking | Count | Centrality | Year | Cited Reference | Number of Cited by (According to Google Scholar) |
---|---|---|---|---|---|
1 | 71 | 0.12 | 2012 | Business intelligence and analytics: From big data to big impact [36] | 6376 |
2 | 45 | 0.05 | 2013 | Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management [37]. | 1336 |
3 | 38 | 0.09 | 2015 | How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study [38]. | 1407 |
4 | 34 | 0.07 | 2015 | Beyond the hype: Big data concepts, methods, and analytics [38,39] | 3929 |
5 | 31 | 0.05 | 2017 | Big data analytics and firm performance: Effects of dynamic capabilities [40]. | 986 |
Ranking | Category | Count | Centrality |
---|---|---|---|
1 | Big data | 111 | 0.26 |
2 | Business, management & Economics | 207 | 0.25 |
3 | Computer science, Interdisciplinary applications | 23 | 0.13 |
4 | Machine learning | 24 | 0.12 |
5 | Economics | 13 | 0.12 |
6 | Engineering | 42 | 0.11 |
7 | Computer science | 102 | 0.1 |
8 | Big data analytics | 44 | 0.1 |
9 | Artificial intelligence | 74 | 0.09 |
10 | Mathematics | 46 | 0.04 |
Ranking | Keywords | Count | Centrality |
---|---|---|---|
1 | big data | 368 | 0.14 |
2 | artificial intelligence | 160 | 0.25 |
3 | machine learning | 109 | 0.03 |
4 | business intelligence | 99 | 0.07 |
5 | data mining | 80 | 0.23 |
6 | predictive analytics | 78 | 0.26 |
7 | forecasting | 75 | 0.03 |
8 | big data analytics | 70 | 0.13 |
9 | decision making | 67 | 0.14 |
10 | data analytics | 64 | 0.08 |
Burst Year | Methods | References | Conclusions |
---|---|---|---|
2012 | data mining | Open business intelligence: on the importance of data quality awareness in user-friendly data mining [41]. | A highly qualified guiding mechanism of data mining of linked open data is necessary for open business intelligence, especially for non-expert users. |
2013 | digital storage | Business Process Analytics Using a Big DataApproach [42]. | Based on Hbase and Apache Hadoop, big data analytics in distributed environments present great advantages for business performance management. |
2014 | predictive analytics | Big data and predictive analytics in ERP systems for automating decision making processes [43]. | By identifying potential risks and opportunities, big data predictive analytics in enterprise resource planning (ERP) plays a great role in automating the decision-making process. |
2015 | ML | Efficient Machine Learning for Big Data: A Review | ML is responsible for decision making processes for BI, where efficient sustainable data modeling is necessary for big data processing. |
2016 | distributed computer system | Business-intelligence mining of large decentralized multimedia datasets with a distributed multi-agent system [44]. | Agent-oriented modeling techniques are novel solutions to meet the distributed data-mining processes. |
2016 | SVM | Big data analytics in healthcare: A survey approach [45]. | SVM is a typical ML algorithm for big data analytics in the BI area, which is responsible for classifying data into binomial classes or multilevel classes. |
2016 | regression | A comparative analysis on linear regression and support vector regression [46]. | Regression algorithms play a great role in BI research, which is mostly utilized for time-series data analytic tasks. |
2017 | classification algorithms | Knowledge management for business intelligence measurement in an e-business system [47]. | Algorithms of hierarchical ascendant classification and product classification present a great strength for BI knowledge management. |
2018 | NN | Deep learning architecture for high-level feature generation using stacked auto encoder for business intelligence [48]. | NN-based deep learning model delivers great performance for big data-based BI tasks. |
2018 | RF | ||
2018 | DL | Deep learning architecture for high-level feature generation using stacked auto encoder for business intelligence [48]. | Compared to ML algorithms, DL strategies show a great advantage for high-level representation extractions. |
2019 | prediction algorithm-based time-series | Large Multivariate Time Series Forecasting: Survey on Methods and Scalability [49]. | Forecasting models play a vital role in BI time series data analysis, where the prediction model optimization of selection, dimension reduction, and shrinkage are highlighted. |
2020 | sentiment analysis | Big data and sentiment analysis: A comprehensive and systematic literature review [50]. | Sentiment analysis based on textual big data is helpful for BI enhancements in aspects of efficiency, flexibility, and intelligence. |
2021 | text mining | Research trends on big data domain using text mining algorithms [51]. | Text mining based on big data for clustering and association evaluations is helpful for BI management, modern techniques like cloud computing, green information, and open source should be considered in future research. |
Burst Year | BI Applications | References | Conclusions |
---|---|---|---|
2013 | decision making | Data science and its relationship to big data and data-driven decision-making [52]. | Data-driven decision-making process has the potential for maintaining BI sustainability, especially for large-scale datasets. However, an explicit research design based on the fundamental of business management is necessary. |
2013 | business process intelligence | Business process analytics using a big data approach [42]. | Big data analytics in a distributed environment is a key solution for evidence-based business process management (BPM), especially to meet high requirements of low cost, high quality, and timely measurement. |
2014 | competitive intelligence and competition analysis | Research on Enterprise Competitive Intelligence Development and Strategies in the Big Data Era [53]. | Big data-driven strategies based on deep insight exploration are key for enterprise competitive intelligence development, especially for company organization, resource sharing, collaborations, and security protection. |
2015 | administrative management | Impact of ICT on administrative management processes [54]. | With the development of ICT, electronic administrative management is enhanced, and participants are able to be involved in the decision-making process directly. |
2015 | commerce enhancement | Big data-based system model of electronic commerce [55]. | By analyzing performances of customer behaviors, deliveries, sales, marketing, competitors, and payments, the big data-based e-commerce model shows a great advantage for online store management. |
2015 | manufacture development | Application of business intelligence solutions on manufacturing data [56]. | Analysis based on manufacturing data is an efficient way to generate strategic reports and enhance manufactory efficiency. |
2016 | sale prediction | Prediction of sales using Big data analytics [57]. | With big data solutions of Apache Flume, hive. And HDFS, analysis of smart data is an effective way for purchase intention exploration, which is significant for marketing enhancements. |
2017 | information management | Improving Governance of Integrated Reservoir and Information Management Leveraging Business Process Management and Workflow Automation [58]. | Information management plays an important role in business process management, which is responsible for process performance metric tracking, communicating value enlargement, strategic decision-making, etc. |
2017 | quality management | Deep-level quality management based on big data analytics with case study [59]. | Deep-level quality management based on process large-scale data |
2017 | knowledge management | Towards integrated models of big data (BD), business intelligence (BI) and knowledge management (KM) [60]. | An integrated pattern of big data, business intelligence, and acknowledgment management is an advanced solution for competitive advantage enhancements. |
2018 | risk assessment | Adaptive management approach for more availability of big data business analytics [61]. | Through forecasting and measuring analysis patterns, adaptive risk assessment methods based on a big data environment present a great advantage to meet the requirements of time-consuming and high accuracy. |
2018 | customer satisfaction management | Advanced customer analytics: Strategic value through integration of relationship-oriented big data [62]. | Big data-based customer analytics deliver the potential for companies’ sustainable competitive advantages. |
2020 | service improvement | The application of a business intelligence tool for service delivery improvement: The case of South Africa [63]. | BI-based decision supporting system (BIDSS) model is responsible for service improvement, big data analytics on users’ feedback and service delivery is a key method for BIDSS building. Quick response, quality service delivery, transparency, and accessibility are the key aspects of service improvement. |
2020 | user acceptance development | Understanding user acceptance of blockchain-based smart locker [64]. | The key factors that influence user acceptance of new technology-oriented products are function, convenience, and security insurance. |
2021 | satisfaction improvement | Quality Big Data Analysis and Management Based on Product Satisfaction Index [65]. | Big data analysis and product satisfaction management are effective solutions for customer relationships and quality performance management. |
Burst Year | Challenges | References | Conclusions |
---|---|---|---|
2013 | software reliability | Cloud solution in Business Intelligence for SMEs–vendor and customer perspectives [66]. | Reliability and cost are the core issues for BI analytics. |
2016 | privacy | CRSA cryptosystem based secure data mining model for business intelligence applications [67]. | Privacy and authenticity of datasets are significant issues for BI applications. Solutions of secure and privacy preserved mining models are responsible for resource and time saving and high accuracy, preventing. |
2017 | personal information | Risk magnification framework for clouds computing architects in business intelligence [68]. | Control system of sensitive and personal data is necessary for BI management, especially for a distributed cloud computing environment, which is significant for the maintenance of security, reliability, and compliance. |
Burst Year | Latest Topics | References | Conclusions |
---|---|---|---|
2021 | COVID-19 | [69,70,71] | The major topics of COVID-19, big data, predictive analytics, and BI research falls on the challenges and BI solution for firms due to the epidemic. The other topic is the application of BI utilized in the research on the influential effects of COVID-19 on business industries. |
2021 | healthcare | [72,73,74] | The major topics are related to the challenges and advanced technologies of BI applied to healthcare industries. Patient data safety and its business application are incentive topics, which should be discussed in the future. |
2021 | hospitality | [75,76,77] | Hospitality is one of the most influential industries by COVID-19, where BI and information technology-driven solution is the most effective and novel methods for levering the hospitality growth trend. |
2021 | 5G | [78,79,80] | 5G technology brings new opportunities for BI in the aspects of quality service monitoring, effective decision-making, efficient operation management, etc. |
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Chen, Y.; Li, C.; Wang, H. Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021). Forecasting 2022, 4, 767-786. https://doi.org/10.3390/forecast4040042
Chen Y, Li C, Wang H. Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021). Forecasting. 2022; 4(4):767-786. https://doi.org/10.3390/forecast4040042
Chicago/Turabian StyleChen, Yili, Congdong Li, and Han Wang. 2022. "Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)" Forecasting 4, no. 4: 767-786. https://doi.org/10.3390/forecast4040042
APA StyleChen, Y., Li, C., & Wang, H. (2022). Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021). Forecasting, 4(4), 767-786. https://doi.org/10.3390/forecast4040042