Elsevier

Expert Systems with Applications

Volume 111, 30 November 2018, Pages 2-10
Expert Systems with Applications

Research Landscape of Business Intelligence and Big Data analytics: A bibliometrics study

https://doi.org/10.1016/j.eswa.2018.05.018Get rights and content

Highlights

Abstract

Business Intelligence that applies data analytics to generate key information to support business decision making, has been an important area for more than two decades. In the last five years, the trend of “Big Data” has emerged and become a core element of Business Intelligence research. In this article, we review academic literature associated with “Big Data” and “Business Intelligence” to explore the development and research trends. We use bibliometric methods to analyze publications from 1990 to 2017 in journals indexed in Science Citation Index Expanded (SCIE), Social Science Citation Index (SSCI) and Arts & Humanities Citation Index (AHCI). We map the time trend, disciplinary distribution, high-frequency keywords to show emerging topics. The findings indicate that Computer Science and management information systems are two core disciplines that drive research associated with Big Data and Business Intelligence. “Data mining”, “social media” and “information system” are high frequency keywords, but “cloud computing”, “data warehouse” and “knowledge management” are more emphasized after 2016.

Introduction

The rapid proliferation of information and communication technology has resulted in a rapid growth of digitized data and has also brought significant attention on research opportunities in Big Data analytics and Business Intelligence in management, social science, and humanity. The trend of Big Data and analytics for Business Intelligence provides great resources and powerful methodology to support the data-driven decision-making process, which is the core of “Business Intelligence.” Many enterprises today are utilizing Big Data to optimize their Business Intelligence process, while the academic research related to Big Data and Business Intelligence has thrived. The number of research papers is increasing very fast. Research topics range from concepts, methodologies, applications, and management. Hence, it is valuable to provide an overview of the published research so that interested scholars can easily know the research profile so far.

For this purpose, we conducted a bibliometric study to examine the academic research output related to “Big Data” and “Business Intelligence” and analyzed publication data obtained from Web of Science, that includes papers indexed in Science Citation Index Expanded (SCIE), Social Science Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources Citation Index (ESCI). The data period is from 1990 to December 31, 2017. Indexed publications with key words of “Big Data” and “Business Intelligence” in their title, abstract or subject are retrieved and analyzed. Findings are then presented.

Section snippets

Research background

Both “Big Data” (BD) and “Business Intelligence” (BI) are fast growing key words in recent academic research. While “Big Data” becomes popular recently, “Business Intelligence” was proposed much earlier. Luhn (1958) began to use the term “Business Intelligence” to describe an automatic system that disseminates information and supports decision-making process. The concept was later assimilated into the area of decision support and information systems. For instance, Vitt, Luckevich, and

Research methodology

In order to have a more comprehensive profile of BD and BI, we built our data set from Web of Science, an online subscription-based scientific citation indexing service originally produced by the Institute for Scientific Information (ISI), now maintained by clarivate analytics (previously the Intellectual Property and Science business of Thomson Reuters). We built the database and used the bibliometrics methodology to map the time trend, the disciplinary distribution, the high-frequency

Time trend of publications

The first analysis is publication trend. Fig. 1 shows the time trend of “Big Data” and of “Business Intelligence.” Less than 38 academic outputs of “Big Data” were found until 2011. The number increased to 92 in 2012 and multiplied very quickly afterward. In the single year of 2016, the number of BD publications went up to 3287. In contrast to “Big Data”, the number of BI publications stayed relatively stable over the years. The trend of “Business Intelligence” started long before 2012, and

Major keywords and topics

Table 2 summarizes the high frequency keywords of the “Big Data” and “Business Intelligence” publications. The keywords are listed in descending order of frequency. Among the 10,637 “Big Data” publications, the top 5 associated keywords are “model”, “algorithm”, “system”, “MapReduce” and “cloud computing”. Among the 1168 “Business Intelligence” publications, the top five keywords are “management”, “data warehouse”, “Big Data”, “data mining” and “systems.” Although a few keywords such as “data

Evolution of keywords and topics

The 10,637 BD publications covered a wide range of fields. To be more focused, we give a closer look at the 141 publications with both BD and BI as key words. Among these BD&BI publications, “management” is the most frequent keyword, followed by “Big Data analytics”, “data mining”, “social media” and “information system”. Fig. 4 shows the evolution of high-frequency keywords in chronological order. The timeline shows that “cloud computing”, “data warehouse” and “knowledge management” are more

Disciplinary distribution and major journals

Another issue we may look into is the disciplines involved in BD and BI. We use special issues published by research journals as our evidence. Table 4 summarizes the academic fields of eight special issues on BD and BI. Three journals fall into the Computer Science field, and the others are related to Information Science and Management. This implies that computer science has been the core discipline that drives the research on BD and BI, while information science and management are also

Major authors and influential publications

Our dataset allows us to find most influential authors and most cited papers among these 141 BD & BI publications. Table 6 lists the publications with the most citation and centrality in the academic networks. “Citations” are the frequency of being cited in the whole data bank, while “Links” is the frequency of being linked among the 141 BD-BI publications Both Citations and Links measure publication importance and author influence.

Table 6 shows that, among these 141 BD & BI publications,

Future research directions

Given the profile indicated in previous analysis, we are able to identify a few key directions for future research. Fig. 7 shows a general framework that divides research topics into four dimensions: technology, applications, management, and impact. Within each dimension, many possible topics need to be further explored.

The technology dimension, for instance, includes issues related to data collection, storage, analytics, and integration infrastructure. For example, sentimental analysis needs

Concluding remarks

This paper reports results from a bibliometric analysis on published academic papers associated with “Big Data” and “Business Intelligence”. Using CiteSpace, VOSViewer and descriptive statistics, we analyzed publication data from 1990 to 2017 in journals indexed in Science Citation Index, Social Science Citation Index and Arts & Humanities Citation Index. A total of 10,637 publications with “Big Data” as key words and 1,168 publications with “Business Intelligence” as key words were identified

Acknowledgements

This research was partially funded by grants to the first author from the Ministry of Science and Technology under grant number MOST-106-2420-H-110-014, the Ministry of Education's Global Research Center of Intelligent E-Commerce, and Research Institute for Humanities and Social Sciences of Ministry of Science and Technology.

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