A Review of Business Intelligence and Analytics in Small and Medium-Sized Enterprises

A Review of Business Intelligence and Analytics in Small and Medium-Sized Enterprises

Marilex Rea Llave (University of Agder, Kristiansand, Norway)
Copyright: © 2019 |Pages: 23
DOI: 10.4018/IJBIR.2019010102

Abstract

Business intelligence and analytics (BI&A) has become a cornerstone of many organizations in making informed decisions. Despite the importance of small and medium-sized enterprises (SMEs) to all world economies, research in BI&A focuses mostly on large enterprises. To aid in closing this gap, this study provides a comprehensive review of the literature sources in this domain. Through a systematic literature review, this study collects, categorizes, synthesizes, and analyzes 78 articles related to BI&A in SMEs. The research topics that are addressed include BI&A components and solutions, Mobile BI&A, Cloud BI&A, and BI&A application, adoption, implementation, and benefits. Further, the research gaps and suggestions for future research are presented to facilitate the progression of BI&A in SME research.
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Introduction

Small and medium-sized enterprises (SMEs) play major economic and social roles because they account for about 90 percent of businesses and more than 50 percent of employment worldwide according to the International Finance Corporation (IFC, 2012, p. 1) Thus, they have become an important source of economic development (Olszak & Ziemba, 2008). The need to improve the worldwide competitiveness of SMEs is crucial. However, SMEs are typically vulnerable and not robust enough to withstand the onslaught of economic and global competition (Ngah, Abd Wahab, & Salleh, 2015). To survive, SMEs must be able to monitor their businesses and use all of their resources efficiently, especially their information resources (Raj, Wong, & Beaumont, 2016).

A substantial difference can be found between SMEs and large enterprises. SMEs usually have limited internal information technology (IT) resources and competencies as well as financial resources. They are also dependent on external expertise when embarking on new IT projects because of the limited human capital and resources for employee training (Blili & Raymond, 1993; Levy & Powell, 2000). SMEs also differ from large enterprises regarding ownership, management, decision making, structure, culture, processes, and procedures. These differences influence SMEs’ ability to implement enterprise systems in general (Zach, Munkvold, & Olsen, 2014).

Business intelligence (BI) is an overarching term for decision support systems that are used to collect, analyze, and disseminate organizational data to improve business decision making (Fink, Yogev, & Even, 2017). According to Yeoh (2008), the term “business intelligence” was first coined by Luhn (1958). However, as Burstein and Holsapple (2008) recalled, the term was reintroduced by Howard Dresner when he defined it as “a broad category of software and solutions for gathering, consolidating and analyzing, and providing access to data in a way that let enterprise users make better business decisions” (Gibson, Arnott, Jagielska, & Melbourne, 2004).

Business analytics (BA), a more recent term, emerged in the late 2000s, and it focuses on the analytical components of BI (Chen, Chiang, & Storey, 2012). Thus, business intelligence and analytics (BI&A) was developed as a unified term to describe information-intensive concepts and methods of improving decision making in business (Chiang, Goes, & Stohr, 2012). According to a recent Gartner survey, BI&A is the chief information officer’s top technological choice to obtain competitiveness (King, 2016). Chaudhuri, Dayal, and Narasayya (2011) stated that “today, it is difficult to find a successful enterprise that has not leveraged BI&A technology for their business” (p. 91). Therefore, the term BI&A is used for the rest of this paper.

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