BI’s Impact on Analyses and Decision Making Depends on the Development of Less Complex Applications

BI’s Impact on Analyses and Decision Making Depends on the Development of Less Complex Applications

Robert Sawyer (I.B.I.S. Inc., USA)
Copyright: © 2013 |Pages: 13
DOI: 10.4018/978-1-4666-2650-8.ch006
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This paper addresses where BI developers have failed to create applications suited for the common end-user and provide a conceptual roadmap to address these shortfalls. It is argued that BI’s impact on analyses and decision-making depends on the development of less complex applications. Research conducted for this paper finds that BI lacks a commo n definition and standard, that BI tools are too complex for the common user, and that a shortage of analytical literacy relevant to BI among business professionals is a barrier to BI adoption. The paper suggests that until BI analysis tools become more “human-centric, design-oriented” and less from a “technology-centric, engineering-oriented perspective”, BI will continue to fail in its objective to routinely improve business decision-making.
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This paper examines the commonality in defining BI has led to the development of BI applications that have failed to appropriately consider the types of people performing the analysis. Analysis tools are critical for employees from all levels of an organization. However, analytical efforts are often hindered by BI tools that are “too complex for wide-spread use” (Harris, 2010). The scarcity of business analysts combined with the lack of training and skill set of most common users impedes businesses from being able to take advantage of the available sophisticated BI tools (Kelly, 2009).

According to recent estimates, 20% of BI end-users can be classified as power users which typically have both the academic training and experience to maximize the analytical tools of most BI applications. For the other 80%, which range from the most senior level to the most junior, not only do they lack the experience but also the knowledge to merely interface with the tools. Additionally, the various layers of complexity additionally frustrate users to the point of creating their own impromptu analysis tools which often leads to more than one version of the truth. Having multiple versions of the truth further denigrates the propensity for an enterprise to harness the potential of both its human and asset capital (Harris, 2010).

Maximizing the full potential of BI begins with first defining BI then designing applications that are equivalent to that definition. By incorporating a uniformed characterization of BI, “discussions…could be made more consistent and constructive [focusing]…on what matters – outcomes” (Herschel, 2010). This would help to concentrate application design efforts towards outcomes rather than building the infrastructure of data that falls short on engaging the “human-computer interaction that possesses intelligence: the human half” (Few, 2006, p. 1).

Due to BI not having a standardized definition, various interpretations from academia to IT contributes to a general misunderstanding of not only what BI is but also what it is supposed to be when it is simply about “using data and data analysis to understand and manage your business” (Davenport, 2010). Variations of defining BI has led to designing complex applications that “are some of the most difficult to use relative to a variety of technologies” (Howson, 2010, p. 1). The complexities in these applications have made it even more challenging to find a qualified work force suited to use BI for analysis.

As companies have discovered, having powerful, analytical BI applications means nothing without the kinds of people that have the “patience, aptitude, or interest to become proficient in BI software, learn how data is structured, or how to do statistical analyses” (Lucker, 2010). Training is a critical need for end-users to maximize the value of “sophisticated applications…that essentially the users are not taking advantage of [owing to a lack of training]” (Kelly, 2009).

One of the biggest issues that hinder end-users from using BI to impact analyses and decision making is because “BI tools are considered hard to difficult to use, with largely unappealing interfaces” (Howson, 2010, p. 1). Research confirms that the design of BI applications is preventing a wider adoption of BI despite the critical role it can play in discovering new opportunities and supporting more of a scientific approach to decisions (Howson, 2010, p. 3).

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