The Impact of Information Visualisation on the Quality of Information in Business Decision-Making

Introduction

Information visualisation in business decision-making is a phenomenon that is relatively poorly understood. This is most likely because it integrates the knowledge of many normative and descriptive disciplines; namely, business studies (business decision-making), information technology, art theory, architecture, design profession, cognitive psychology, philosophy and other social sciences. Another reason is found in Muhovič´s indications (1997), where it is noted that since 1970 the quantitative increase in image information has also launched a revised understanding of the role of images in human cognition, thus triggering a higher number of scientific studies of information visualisation in particular, often in separate fields of psychology, art theory, philosophy and, more recently, information technology and information systems management. The current research in the field of information visualisation is mostly focused on empirical methodologies and applications have been developed to help design effective visualisations (Liu et al., 2014).

The main purpose of information visualisation is to create designs that help users improve their cognitive skills, such as the perception and understanding of abstract data and to enable them to access, discover, design decisions, and interpret the data. Generally speaking, information visualisation provides mental models of information (Liu et al., 2014) and thereby helps users analyse complex relationships (Wu & Hsu, 2013). In the business context, visualisation has three fundamental functions: it serves as a communication medium, as a knowledge management means, and as a decision-support instrument (Al-Kassab et al., 2014). The fact that business data can be extremely complex and messy provides interesting and important opportunities for visualisation. Some of the difficulties when visualising business data are heterogeneous, disparate and unrelated data sources, data sources might be highly unstructured, the information in those sources might be highly speculative and unconfirmed, the data might contain multilevel entities of interest and might be multidimensional, and data might be incomplete, containing errors and uncertainty (Basole et al., 2012; Liu et al., 2014). Moreover, the same information can be visualised in many different ways. For example, an OLAP cube, a typical data structure used in business decision-making processes (Prado, Freitas, & Sbrici, 2010), can be visualised as a multiscale visualisation system, as a hyperbolic space, using wavelet-viewing methods, using regular graphics elements built into the display, or as an enhanced decomposition tree (Xusheng & Beifang, 2012).

Dull and Tegarden (2004) present the research of various authors studying the impact of format or forms of visualisation on business decision-making. They stated that most of the research results indicate that the use of visualisation by different decision-makers allows them to understand the forms (graphs) and improve their decisions. Visualisation is an enabler of visual analytics – the fusion of analytical reasoning and computational data analysis with rich, interactive visual representations. The ability to perform visual analytics is nowadays an important capability for decision-makers in virtually all industries (Basole et al., 2012). Vessey (1991) presents a Cognitive Fit Model (CFM model) in which the efficacy of the process of solving problems associated with characteristic decision-making tasks and presentation (visualisation) problem is assessed. If visualisation corresponds with a decision-making task, the decision-maker will generate a desirable mental activity visualisation – the speed and accuracy of the problem-solving process will thereby be improved. Although it seems obvious that information visualisation positively contributes to a competitive advantage through accelerated perception, improved insights and control, the empirical evidence lacks visualisation benefits (Al-Kassab et al., 2014). One of the scarce examples is a study which shows that interactive visualisation increases decision-maker confidence and accuracy in a financial decision-making context (Fengchun et al., 2014).