Epistemologies of Data Visualisation
- Data Visualisation
I visualised connections between the various key texts in data visualisation that I have been reading. My categorisation tries to break down each practitioner’s approach towards knowledge, its visualisation & their audiences.
I outline a set of categories that distinguish different approaches to data visualisation, organised across two broader frames: data as a means of inquiry and visualisation as a method of communication. As an underlying foundational philosophy, differences emerge in theories of knowledge and ways of knowing, including ontological and epistemological positions. On one end of this spectrum, knowledge is treated as objective and absolute, where understanding is hierarchical and progresses from a broad overview toward finer details. In this view, understanding derives from observation and deduction. On the other end, knowledge is understood as subjective and situated, where understanding is interconnected and grows by making connections. Here, understanding derives from sensation and emotion.
In terms of approach to visualisation, one position treats visualisation as a cognitive tool, emphasising function. This approach prioritises showing the data, using well-understood visual grammar, and relies primarily on visual representation. An alternative position treats visualisation as an emotive tool, emphasising aesthetic expression. This approach focuses on showing the story, creating one’s own visual grammar, and working with multimodal forms.
Differences are also evident in assumptions about the audience. In one case, the audience is understood as a motivated expert looking to make a decision quickly. They are assumed to be data literate and responsible for interpretation. In contrast, another assumption frames the audience as a lay person interested in satisfying curiosity, where the practitioner is responsible for facilitating interpretation.
Taken together, two overarching perspectives emerge: One group of practitioners consider data as objective & absolute source of knowledge, its visualisation as a cognitive artefact & their audience as data-literate experts. Another considers knowledge as subjective & situated, its visualisation as an emotive tool & their audience as curious non-experts.
Contextualising these differences will be instrumental in helping me establish my own position by devising a personal definition of data visualisation that takes a humanistic stance centered around people & narratives rather than merely on data, and multi-sensory, visceral experiences instead of just visualisations.