Data graphs encode the data into a visual; they are not facts like a number. They are design objects.
Their design must be focused on accomodating our pre-attentive visual strengths and resolve the limitations of our visual perception.
As design objects, data graphs also require aesthetic attention. But design is not data, and it is the ordered as the last quality in the order of priority. Design must be exercised with restraint and never compromise any other quality of higher priority.
Good design reduces the visual prominence of non-data elements, carefully composes identification text, maximises the data-ink ratio and selects hues and patterns with excessive caution given their reduced accuracy in decoding. Representational coherence, interpretability and understandability are desired attributes of quality design.
White space is the best friend of the data graph designer.
Great design adorns the data through the application of white space, as it is through white space that the encoded data becomes visibly divisible information.
If a graph is packed with data then it would be very hard to perceive any information. It is the white space the helps the user decode discernible messages.