Qualities of data graphics

This page follows the from the Graph Workflow Model page; please make sure to read those material first.


Data graphing is an information system.

The input is the factual tabular data (that is assumed to be truthfully recorded). The processing of the input is performed by visual encoding tools, including visual implantations, retinal variables, graph identification, and graph enhancement tools.

The output (the graph) is a graphical estimation of the data, and like any estimator it is subject to estimation error (from the decoding perspective). The error can be quantified in terms of how accurately the graph reflects the real data signals and the user’s understanding of the graph objective, given the choice of encoding.

One of Jacques Bertin’s most important contributions to the theory of graphics is the separation of the notion of data from the information that is decoded due to choice of visualisation method. Again, it holds that the data is exact and absent of bias but its visualisation is a subjective process that inescapably introduces decoding errors and ambiguities that could result in a biased interpretation.

A data graph is of high quality if its key objective can be decoded with speed, accuracy and great confidence. To do so, the graph must be effective at exploiting the strengths of our visual system and also account for the limitations of visual perception. A quality graph applies considerable care in its encoding approach and focuses attention only to the data.

To see excellent examples of graphs see Edward Tufte’s (1997) The Visual Display of Quantitative Information, who defines graphical excellence as follows:

Graphical excellence is that which gives to the viewer the greater number of ideas in the shortest time with the least ink n the smallest space

I identify six broad qualities for data graphs, that describe the fundamental principles of crafting truthful graphs. They are presented below in order of priority (click on the links to learn more):

  1. Decoding accuracy
  2. Completeness
  3. Encoding relevance
  4. Encoding consistency
  5. Decoding efficiency
  6. Simplifying complexity
  7. Encoding design

Back to Intended audience ⟵ ⟶ Continue to Decoding accuracy

Demetris Christodoulou