Data Visualisation: Impact on Decision Making

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Impact of data visualization on decision quality


1.1 Purpose of the study
The purpose of the study is to research the effect of data visualization on decision quality.

1.2 Context of the study
Visuals have been used for centuries, saying a picture is worth more than a thousand words proves this. From geographical maps to stock prices, data visualization is widely used to represent information visually. In BI, due to the increasing trend into data collection and other data mining techniques, 3D charts and dashboards among other visuals (Duke et al. 2005; Lester n.d. & Blewett n.d.), have been widely accepted as important tools in business analysis and have aided in companies maintaining some competitive advantage by making quick and accurate decisions.

Data visualization therefore can be seen as an important part in the decision making process. How then can businesses make sure that the decisions they make are of a high quality? This research will focus on the data visuals and fill the gap in research as well as practice.

1.2.1 Data mining
Data mining uses mathematical formula in constructing models(Davenport et al. 2001), which would lead into establishing patterns(Viktor 2009), in mapping out business decisions and strategies. This has lead to the extensive widespread use of data mining in marketing, medicine, finance. It is the production of these patterns that make data mining techniques very crucial, though huge data sets cannot always produce results difficult to understand.

Watson (2009) highlights the numerous benefits of BI, as well as pointing out how difficult it is to measure them. Though providing examples, of only successful implementation, it fails to reflect on possible weaknesses of BI thereby missing on lessons to learn from failed implementations. In conclusion, as BI becomes more pervasive so does the cost of training and support.

1.2.2 Decisions and decision-making process
Decision theories are very conflicting and could be classified into normative and descriptive (Clark 2010) and (Keren & Bruin 2003), and judged by the process or by outcome(Keren & Bruin 2003). Decision quality is always a hindsight action, and as such makes judging it even more difficult and complex.

Clark (2010) though full of practical examples contains no empirical data to support its claims. This paper is written in a marketing manner than an academic paper. Keren & Bruin (2003) though well written and containing numerous practical examples and simulations, has a lot of statistics. This paper however, could be used in selecting a weighing method to use. These two papers do provide more insight into the complexities of decision quality and try to shed light, into which methods are more accurate than others. Focus though tends to be on the decision analysis and less on decision quality

Due to myriad definitions and the fact that it is a complex subject, which could be broken down into a systematic and concise manner (ibid.) Judging the decision quality could be viewed from two angles, one being the decision maker, secondly the judge. Both present their own problems, the decision maker has to deal with anchor traps, status quo traps, sunk cost traps and confirming-evidence traps among others (Hammond et al. 1998). The judge on the other hand might choose a different model to the decision makers’ thereby wrongfully assessing the decision quality(Keren & Bruin 2003). The judge assesses using three methods: gambling paradigm, conflict method and accountability method (ibid). Numerous statistical weighing methods could be employed (Jia et al. 1997), though practice and theory does yield differences.

In general, people do not really care how decisions are made but as to the quality of these decisions, and moreover people focus mostly on outcomes rather than the process itself (Keren & Bruin 2003). An example provided is that of a doctor conducting...
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