Getting value from Business Intelligence systems: A review and research agenda
Introduction
‘Business Intelligence’ (BI) has become an increasingly important concept with the availability of ‘big data’ and advances in machine intelligence [1]. Receiving widespread interest in both academia and industry [2], BI systems are now used extensively in many areas of business that involve making decisions to create value. However, to help BI achieve its full potential, practitioners and researchers need to more fully understand the processes through which organizations can get value from BI. To date, researchers have examined BI using a variety of theories, research lenses, and empirical approaches. While these various streams of study provide diverse views on BI, they can also make it difficult to build a holistic and integrated view of BI business value and sustain a cumulative research tradition. While many authors address rather specific research questions relating to how BI creates business value, no comprehensive research agenda has been developed to understand the process of organizations obtaining business value from BI. Therefore, the research question addressed in this paper is: What do we know, how well do we know, and what do we need to know about the processes of organizations obtaining business value from BI systems? The aim of this literature review is to learn the extent to which we can answer this question based on existing literature, identify which parts of the answer are most in need of further research, and reveal key research questions for future work.
Rather than having a well-accepted and specific definition [3], BI is typically used as an ‘umbrella’ term to describe a process [2], or concepts and methods [4], that improve decision making by using fact-based support systems. Many terms (such as “business intelligence”, “business analytics”, “big data”, “data mining”, and “data warehousing”) are often used interchangeably in the literature, with authors variously describing BI as a “process and a product” [5 p.121], “a process, a product, and a set of technologies, or a combination of these” [2 p.87], or a product alone [6]. As a result of these diverse definitions and perspectives, and the growing interest in BI in academia and importance to industry, it is important to synthesize the literature to determine what we already know about the process of generating business value from BI, what we still need to know, and how we can get there. There are a number of studies that contribute, in different ways, to this knowledge. Seddon et al. [6], for example, developed a BI success model but did not expose gaps in the literature or propose future directions. Similarly, while Arnott and Pervan [7] analysed BI studies from 1990 to 2003, and Jourdan et al. [5] analysed BI studies from 1997 to 2006, neither paper focused on the process through which BI contributed to business value. Thus, there remains a need for a deeper analysis of the processes of organizations getting value from BI [8].
In keeping with past literature, in this paper the term BI is used to refer to a set of concepts and methods based on fact-based support systems for improving decision making [9], and the term ‘BI system’ is used to refer to both model-oriented [7] and data-oriented decision support systems [7], [10], [11]. Specifically, BI system here is defined as a system comprised of both technical and organizational elements that presents historical information to its users for analysis, query and reporting, to enable effective decision-making and management support, to increase the performance of business processes. To learn what the research literature can tell us about the processes of organizations obtaining value from BI, the IS business value model of Soh and Markus [12] is used, incorporating constructs suggested by Melville et al. [13] and Schryen [14]. Drawing on BI research published from 1/2000 to 8/2015, insights are explored in each area of the framework to expose gaps and reveal unexplained or partially unexplained areas in need of further research.
Section snippets
Review of prior literature: paper selection, framework, and coding process
In this section, the conduct of the literature review is explained and the framework used to structure the coding is described and illustrated.
What we know
The analysis was conducted in three phases. First, a broad sense of how many articles study BI business value was obtained. Next, papers were reviewed and synthesized for each concept in the BI business value framework. Finally, relevant aspects of these studies as appropriate for each concept and the relationships between them were explored.
What we need to know
As noted earlier, the focus of this study has been to learn which parts of the BI business value framework have attracted researchers' attention and what opportunities these offer for future research. The analysis reveals five broad themes that could motivate further work. Research questions corresponding to each of the five themes have been identified (see Table 7). While these opportunities are not the only ones, the results of the literature review suggest that they are significant.
Having
Limitations
Although great care was taken to review the literature thoroughly, three limitations should be noted. First, the study only examined the attention that researchers paid to particular constructs and relationships in their research. The review did not include a quantitative evaluation of the strength of relationships among concepts in the framework. A meta-analysis could be conducted, as a next step, to extend this study. Second, the findings of the review and the opportunities identified are
Conclusion
In this paper, a literature review of empirical studies in Business Intelligence was conducted to examine research into the processes of organizations obtaining value from BI systems by learning from the IS field's empirical BI studies. Through the discussion, gaps in current BI research have been identified as opportunities for future work.
Generally, the BI literature was found to be lacking an overarching framework to systematically guide future research and to integrate findings. The
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