Decoding efficiency is a relative quality, that comes only fifth in the priority of qualities of data graphs. Efficiency has to do with comparing completing data graphs all of which have satisfied the qualities of accuracy, relevance, completeness and consistency.
Decoding accuracy is the topmost quality that is independent to decoding efficiency, but conforming to relevance, completeness and consistency may cause differential degrees of efficiency.
Therefore, if the graph is accurate, and encoding is relevant, complete and consistent then we say that “a graph is more efficient than another if its decoding requires a shorter observation time” (Bertin 1967, p.139).
Efficiency is directly determined by the audience’s capacity to decode. It is imperative to account for the familiarity and training of the audience with appropriate encoding mechanisms.
Identification is key to achieving efficiency, and it holds that the sharper the external, internal and direct identification the quicker the decoding. Graph enhancement can also play an important role to achieving a higher degree of efficiency.
A critical condition for efficiency is iteration. Because the same data can be graphically encoded in myriad ways, the competing visuals must be ranked according to their qualities. Indeed, the Graph Workflow model is described as an iterative experimental estimator. Following visual decoding, the process begins again until one meets all qualities of data graphics, abides to standards and satisfies the parameters set by our limited visual perception.