64 Little Little Business Intelligence Advancement Workshop

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64 Little Little Business Intelligence Advancement Workshop

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Received: 31 December 2022 / Revised: 5 February 2023 / Accepted: 7 February 2023 / Published: 10 February 2023

Over the past few decades, business analytics has been widely used in various business sectors and is effective in increasing enterprise value. With the advancement of science and technology in the era of big data, business analysis techniques are changing and developing rapidly. Therefore, this article reviews the latest techniques and applications of business analytics based on the existing literature. Meanwhile, many problems and challenges are inevitable in the development of business analytics. Therefore, this review also presents current challenges facing business analytics and open research directions that need further consideration. All scientific articles were obtained from the Web of Science and Google Scholar databases and filtered with several selection rules. This paper will help provide important insights for business analytics researchers as it presents the latest techniques, various applications, and several directions for future research.

In recent decades, data has rapidly changed the world. Especially in the era of big data, data is cheap and ubiquitous, but what makes data a valuable asset is how it is used to obtain useful information. Because there are many different types of business objectives, different analytical techniques are needed to achieve them. These techniques have many applications in the business realm and “business analytics” enable the business application of Big Data. Since the emergence of the term business analytics, it has grown by leaps and bounds, reflecting the increasing importance of data in terms of volume, variety and velocity [1]. Although there is no single definition of business analytics, the existing definitions can be summarized in several dimensions, such as movement, transformation process, set of capabilities and so on [2].

Interest in analytics and data science is growing as business organizations are massively using business analytics to improve their business value. Business analytics has become an important part of the business decision-making process, using data to drive decisions and assist decision-makers in making strategic, operational and tactical decisions [3]. Specifically, business analytics can help companies harness the value of historical data by leveraging the power of statistical and mathematical models and advanced techniques such as artificial intelligence algorithms. Through these models and algorithms, businesses can integrate various data sources to predict trends, optimize decisions, and more. As business analytics continues to evolve, its applications continue to expand. It has been adapted in some functional departments within the enterprise and some non-business areas.

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Judging by the volume of literature in the database, there are many types of literature on the study of business analysis, including its techniques, impact, applications in some fields, etc. Among them, several scholars have systematically summarized the many aspects of business analysis. However, the techniques and applications of business analysis have changed significantly as technology has developed rapidly in recent years. Thus, to organize the latest knowledge on business analytics, we present four main research questions:

This article is structured as follows. Section 2 presents the methodology used in this review and conducts a simple bibliometric analysis of the literature. Section 3 concludes the definitions of business analytics in four categories to answer RQ1. The techniques used in business analysis are presented in Section 4 to answer RQ2. Section 5 describes applications of business analytics in several business areas and industry sectors to answer RQ3. RQ4 is answered in Section 6 to reveal the challenges facing business analytics. Finally, Section 7 concludes this paper.

To understand research trends in business analytics, we collected related academic literature from the Web of Science and Google Scholar databases, as they are widely recognized and cover a large number of high-quality publications in peer-reviewed journals [4]. We then conducted a bibliometric analysis of the existing literature in terms of the number of publications per year and their research directions in Section 2.2. Since there was a lot of material on business analytics research, we developed several selection rules to filter the literature for further review. First, “business analysis” should be contained in the title or abstract of the publications. Second, we focused only on publications in English. Third, we examined different types of publications, including scientific articles, reviews, and book chapters. Moreover, to consider both novelty and impact of the articles, at least ten citations were required for the publications before 2020, while at least two citations were required for those after 2020. Based on the selection rules, academic works with high impact were selected. In addition, it was possible to read all selected documents in full. We then read the abstract of each body of literature to decide whether it meets the purpose of our further review.

Based on the methodology, we conducted the literature selection process. Figure 1 shows the flow chart of the selection process. We searched Web of Science with the keyword “business analytics” in the title or abstract and no other selection rules, and the number of results was 821. After filtering by language (English) and publication types (scientific articles, reviews, and book chapters), 365 articles remained. We then restricted the number of citations before and after 2020 and excluded 193 results. Finally, we read the abstract of the selected articles to further filter relevant articles, and there were 76 articles ready for in-depth review.

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First, we conducted a quantitative analysis of the business analytics literature regarding the number of publications per year from 2012 to 2022, which is shown in Figure 2. From 2012 to 2017, the number of publications per year showed a significant upward trend and peaked in 2017. After 2017, the number decreased slightly, but still remained at a high level compared to 2012. , which means that business research analysis continues to attract many scholars now.

Second, we conducted an analysis of the top ten research areas in the business analytics academic literature in Figure 3. It is clear that computer science is the most popular research area among the published business analytics literature. This is because computer science is an essential part of business analytics and drives the development of business analytics applications. The second most popular field of study is engineering, which implies the application area of ​​business analytics, while the third is business economics, showing the value of business analytics on the economy. The remaining research areas also reflect the techniques and applications of business analysis, respectively.

At the moment, there is still no single definition of business analytics. Scholars in various fields have defined the term business analysis from several perspectives. Holsapple concluded 18 definitions of analysis in 6 dimensions [2]. Referring to the dimensions, this article organizes recent definitions of business analytics into four categories in Table 1.

First, in terms of techniques, business analytics is considered an application of any data analysis [5] or data science [6] in business fields that uses tools and techniques statistically and quantitatively to analyze a vast collection of data sources to support business decisions [7]. More specifically, business analytics can be viewed as “a broad category of applications, technologies, and processes for collecting, storing, accessing, and analyzing data to help business users make better decisions” [8]. With the continuous emergence of new technologies, business analytics can also be seen as a combination of operations research, artificial intelligence (machine learning) and information systems [1].

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Second, from a process perspective, business analysis is an encapsulation of tools for

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