Terms and definitions

Since the aim of this study is to conduct empirical research into what people in organizations do in terms of BI once it is produced, that is, how BI as a product is used in organizational decision making processes, it is important to define some of the key concepts that are central to this investigation. Specifically, the terms business intelligence (BI), BI output, device, and organizational decision are central to this project. Because these terms are used, defined or can be understood in many different ways, it is crucial to make clear the meaning that it is assigned to them in this study.

The BI literature lacks a universally accepted definition of BI (Pirttimaki 2007; Wixom and Watson 2010). The definitions range from one-dimensional definitions, in which BI is viewed as a set of technologies or as a process, to multidimensional definitions, in which BI is viewed as a process, a set of technologies and a product (a detailed discussion about the BI definitions is provided in chapter two). In line with the multidimensional view, Davenport (2006) defines BI as a term which:

“encompasses a wide array of processes and software to collect, analyze, and disseminate data, all in the interest of better decision-making.” (pp. 106-107) In the same way, Wixom and Watson (2010) define BI in their paper as:

“a broad category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help its users to make better decisions.” (p. 14)

Building on the definitions of Davenport (2006) and Wixom and Watson (2010) I formulate the following definitions of BI and BI output. The later depicts how the term BI output is used in the remainder of the document:

Business Intelligence is data-driven analysis - a process of gathering, storing, and analyzing data through the use of different technologies and applications - which is relevant for decision-making.

The BI output is the outcome of data-driven analysis.

This BI definition is similar to the Davenport (2006) and Wixom and Watson (2010) definitions in the sense that it accounts for the process of gathering, storing and analyzing data. Also in common is the notion that technologies and applications support this process. The definition of the BI output differs in that it highlights the output of BI both as a process and as a technology.

Further, it should be noted that views in the BI literature differ on what constitutes the output of BI amongst data, information and knowledge (discussed in detail in chapter two). I refer to the outcome of data-driven analysis as the BI output for several reasons.

First, by taking an output-centric perspective on BI, the confusion between data, information and knowledge is eliminated. Second, the concept of the BI output helps to depict the fact that this output is produced by people, processed by technologies and used in organizational settings. Third, in this way, researchers studying the use of BI in organizations have a tangible, concrete conceptualization of BI output, which can serve to crystallize the object of their investigation.

In this way, the BI output can be variously a spreadsheet, a table, a graph, a report, an indicator, a measure, the results of a query, or a collection of the above such as dashboards and scorecards. Technology is considered as an important component in facilitating the process of producing the BI output, which in many instances is in a digital form. However, the BI output may also appear embedded in a hardcopy report.

The concept of the BI output is also useful because it allows us to analytically separate prescribed use from actual use. As Orlikowski (1995) remarks, “while the artifact and context of use may prescribe and proscribe particular images and forms of use, how actors make sense of the artifact and what they actually do with it is not predetermined” (p. 3). That is, simply because the BI literature prescribes the BI output as ostensibly rational it does not mean that this is also the way it is always used in decision-making.

Additionally, after the empirical data analysis I observed that the BI output was treated as a device used by decision-makers. Therefore, for clarity purposes I define below the term device, which is frequently used in Chapter 7.

There are numerous definitions of the term device but the most well-known is the Foucault’s. Foucault defines the notion of device (in French: dispositif) as following:

What I’m trying to pick out with this term is, firstly, a thoroughly heterogeneous ensemble consisting of discourses, institutions, architectural forms, regulatory decisions, laws, administrative measures, scientific statements, philosophical, moral and philanthropic propositions – in short, the said as much as the unsaid. Such are the elements of the apparatus. The apparatus itself is the system of relations that can be established between these elements. ... I understand by the term “apparatus” a sort of – shall we say – formation which has as its major function at a given historical moment that of responding to an urgent need. The apparatus thus has a dominant strategic function. (Foucault and Gordon 1980, p. 194, italics in original)

That is, according to Foucault the device serves a need. In this study it is the need of the decision-makers to reduce their cognitive burden and as such uncertainty and ambiguity. Further, the device has a strategic function meaning that it is fused, used, and deployed by decision-makers in order to achieve something, i.e. to reduce uncertainty, shape collective judgment or reach collective decisions. Agamben (2009) broadens the class of devices by including

… literally anything that has in some way the capacity to capture, orient, determine, intercept, model, control, or secure the gestures, behaviors, opinions, or discourses of living beings. Not only, therefore, prisons, madhouses, the panopticon, schools, confession, factories, disciplines, judicial measures, … but also the pen, writing, literature, philosophy,

agriculture, cigarettes, navigation, computers, cellular telephones and – why not – language itself, which is perhaps the most ancient of apparatuses – one in which thousands and thousands of years ago a primate inadvertently let himself be captured, probably without realizing the consequences that he was about to face. (p. 14).

In this study the term device is used to refer to methods used by decision makers to manage uncertainty, ambiguity and at the same time to influence collective judgment and reach collective decisions.

In the BI literature there is no discussion about what is meant by decision-making although the term is used quite often by BI scholars. Further, there is no distinction betweenindividual decision-making and organizational decision-making.

This study investigates the use of the BI output in organizational decision-making, viewing decision-making as a collective process involving multiple actors and decision-makers that usually are not consistent in terms of their preferences and identities. At the same time, these actors might or might not be inclined to eliminate the conflict that arises from these inconsistencies. As such, the actors or decision-makers have to reach organizational decisions despite their conflicts due to inconsistent preferences or identities. Organizational decisions are decisions that have a great impact on the present and the future of the overall organization.