Big Data in Business Intelligence

Conclusions

This study utilized the tool of CiteSpace to implement a bibliographic study on 681 non-duplicate citations retrieved from WoSCC and Scopus databases from 2000 to 2021, the research topic is related to the application of big data and predictive analytics to business intelligence. Findings show that the publications on this topic are at an increasingly developing trend, which is predicted to be continuing in the next few years. Besides, the most academic influential countries, institutions, journals, authors, and articles are identified in this study. Disciplinaries, hotspot metrics, and topic burst history trends are discussed. The social network between countries, institutions, authors, and categories is explored. The developing trend of methodologies, BI applications, and challenges related to big data, predictive analytics, and BI. The reason hot topics burst in 2021 is discussed. It contributes significant reference value for related researchers in the future, especially for the topic selection and method application.

Limitations are concluded as the following. Firstly, articles of WoSCC and Scopus only in English are involved in this study, other literature databases should be considered in future research. Secondly, the insights are extracted based on the results analyzed by the tool of CiteSpace, where papers are requested to be imported as the standard format, the information other than the imported citation is not able to be explored. Thirdly, without considering other measurement methods, this article identifies the contributed institutions, scholars, journals, and topics only at the academic level.

Thus, four pieces of advice are delivered for future research. Firstly, articles in more than one language from multiple databases are suggested to be analyzed. More keywords, like "data mining", should be considered during the searching in databases. Secondly, different tools, including text mining tools, are encouraged for scientific article explorations. Thirdly, when it comes to identifying the contributed institutions, scholars, journals, and topics, a practical perspective, such as economic and social contributions, should be explored, especially for the firms and managers. Fourthly, this study recommends a research direction for future research, which is that big data, predictive analytics, and BI could be considered applied to the industries related to COVID-19, healthcare, hospitality, and 5G. Explainable big data and AI approaches should be paid more attention, since, without a high level of interpretability, transparency, and accuracy, the black-box AI prediction algorithms may cause a huge economic loss. Finally, studies of the novel method of text mining based on social media data are suggested for BI enhancements.

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