Semiotics in Visualisation

Digital visualisation is a way of representing data and information with the aid of digital means. It ranges from a simple form such as a graph or chart to a complex form like animated visualisations that allows user to interact with the underlying data through direct manipulation (Chen et al., 2008). The notion of digital visualisation engages human interpretation on information in order to gain insights in a particular context (Robert, 2007, Ware, 2012, Czernicki, 2010). Hence, it is a complex process involving multiple disciplines, including the socio-technical element. The social element relates to human perception in interpreting information. The technical element on the other hand, refers to the technology used to enable visualisation, for example the SAS suite (SAS, 2014) that offers visual analytics to support interactive dashboard and reporting. A typical process of digital visualisation starts from data collection, followed by data transformation and filtering, and finally the visual display (adapted from Ware, 2012). The primary goal of digital visualisation is enhance the efficiency of understanding of certain phenomenon through visualised data and information; but it also aims to address latent aspects, such as semantic and pragmatic issues. The semantic issues are related to the interpretation of visual displays and the meaning in the context. The pragmatic issues are concerned with the effect and intention to be achieved on the users through digital visualisation. Digital visualisation deals with three components: data, information and knowledge (adapted from Chen et al., 2009). In a slightly simplified term, through digital visualisation, human users perceive data in the forms of e.g. figures, numbers and charts. Such a presentation of the data enables the human users to understand the data better and to obtain information efficiently through the processing of the data, which bring in the meaning and insight of the data to the user. Digital visualisation assists the users further to gain knowledge through interaction, interpretation and abstraction of data and information to produce generic types or patterns based on classifications of contexts and application domains. Digital visualisation is always purposeful that will illustrate relationships; discover patterns and interdependencies; or generate some hypothesis or theory. The user’s hypothesis would very much influence on what data would be interested in the analysis and be included in the visualisation. Therefore, there will be always a set of questions highly relevant in any visualisation, such as data availability, access, format (data itself and display format), meaning (i.e. interpretation), purpose of data presented, and effect of visualised data on the recipients. Such questions can be best answered by drawing input from semiotics. Semiotics, a formal doctrine of signs introduced by Peirce back in the 1930’s (Peirce, 1935), shows great relevance to digital visualisation. A special branch is organisational semiotics which has been developed by Stamper and his colleagues (Stamper, 2001) to study the effective use of information in business context. Data, under the study through visualisation, are signs. The human interpretation on information is closely related to the five types of norms: perceptual, cognitive, evaluative, denotative and behavioural (Stamper et al., 2000). Norms as a generic term are patterns, regulations, rules and laws which are the reflection of knowledge in a cultural group or an organisation (Liu, 2000). From a semiotic perspective, digital visualisation is a process of abduction which is a key process of scientific inquiry, i.e. a process of generating new knowledge. The process of abduction involves the norms extensively. When encountered with a new phenomenon, prior knowledge will enable us to produce some initial, but often, plausible explanations. Abduction thus allows us to generate hypotheses (which should be plausible) and further to determine which hypothesis or proposition to be tested (Gregory and Muntermann, 2011, Yu, 1994). Peirce defines abduction as “the process of forming explanatory hypotheses”, and the “only kind of