Data professionals who work with data visualization have heard of Edward Tufte. He became famous in the 1980’s and 90’s for his studies of graphical displays of quantitative information and what makes them truly excellent.

Here are his “Principles of Graphical Excellence” from “The Visual Display of Quantitative Information,”

“Graphical excellence is the well-designed presentation of interesting data – a matter of substance, of statistics, and of design.

Graphical excellence consists of complex ideas communicated with clarity, precision, and efficiency.

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

Graphical excellence is nearly always multivariate.

And graphical excellence requires telling the truth about data.”

There is wisdom in his formulation. He incorporates ideas about respecting a viewers time, inspiring ideas, clarity and precision of ideas, and truth. These are all noble ends and data professionals would do well if they followed them.

But there is a word that sits in amongst principles without explanation and potentially circular definition: design.

Design is a word overloaded with meaning and history that – unfortunately – has for many become merely synonymous with “aesthetics.”

Dieter Rams, an industrial designer and near contemporary of Edward Tufte provided his own principles of design:

Good design

  1. is innovative
  2. makes a product useful
  3. is aesthetic
  4. makes a product understandable
  5. is unobtrusive
  6. is honest
  7. is long-lasting
  8. is thorough down to the last detail
  9. is environmentally friendly
  10. is as little design as possible.

These two sets of principles intersect at ‘truth’ and ‘honesty,’ which is an excellent place to begin. But each set says so much more about the nature of good design.

I wonder whether they should be revised is an age of digital products, when using ‘ink’ may not be an option.

What can these principles teach us about good design in data products?