Business Intelligence and Analytics Research: A Peek Inside the Black Box

Business Intelligence and Analytics Research: A Peek Inside the Black Box

Gregory S. Richards (University of Ottawa, Ottawa, Canada)
Copyright: © 2016 |Pages: 10
DOI: 10.4018/IJBIR.2016010101


The tremendous growth of data of all forms has led to an increase in research on the uses and outcomes of Business Intelligence and Analytics (BI&A). Much of the current research however, focuses on the technological aspects. The process of decision making with data is treated more or less like the proverbial black box. If one is to better understand how BI&A can help managers make informed decisions, then more effort is needed to explore the decision making process. This paper argues that decision-making in organizations is enacted by a sociotechnical system in which human information processing forms the key constraint. By considering the stages of cognition and the use of rules-based versus heuristic-based decision making, the paper identifies a number of core questions related to the contribution of a BI&A system to the decision making process in organizations.
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Decision Making With Data: A Process Of Inference

The commonly accepted model in much of the BI&A research appears to be that humans, despite their limited information processing capabilities and the socially constructed power struggles that accompany life in organizations, consume the output of information systems to make informed and therefore more effective decisions. We have known for some time about the limits of human information processing capacity (Simon, 1955), and so the assumption of a purely rational decision-making process that underlies much of BI&A research is likely untenable.

Decision making with data is based on inferences drawn from cues in the data set (Chater, Tenenbaum, & Yuille, 2006). The drawing of inferences relies on inductive reasoning, which has been classified into either automatic (heuristic-based) or the more familiar rational choice model that uses rules-based processes (Ferreira, Garcia-Marques, Sherman, & Sherman, 2006). The heuristic-based approach involves pattern matching: the decision maker recognizes cues and the associated responses reflective of similar previous situations. The cues more or less trigger an automatic response selected from the range of recalled responses. In fact, the proponents of Naturalistic Decision Making (NDM) have argued that the while rules-based model might apply in some situations (Klein, 2008), in the real world, most complex decisions are actually made through this process of pattern matching.

The components to be matched include organizational or individual goals, critical cues, expectancies and typical actions (Klein & Klinger, 1991). For relatively simple decisions, this pattern-matching process is very quick. In this case, the situation is immediately recognized and a match to previous decisions is quickly found. The decision maker simply implements actions similar to those applied in the past.

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