Edward Tufte, Information Sage

The comments are interesting. Tufte alumni are starting to network.

Edward Tufte does leave a deep impression. I stumbled upon his books in my university's library, about eight years ago, while studying design.

Some years later—while going through files on my old computer— I found a pair of schedules. I'd design these every semester so my friends knew where to find me, since I didn't have a cel phone.

Before Tufte (c1999): http://blog.duopixel.com/images/horario.gif

After Tufte (c2002): http://blog.duopixel.com/images/horario2.gif

If I'd design it now, I'd take a different approach yet again. In the end, good design is neither a nice style or maximizing data-to-ink ratio, it's about making information understandable and pleasurable to use.

Congratulations sir, and thank you for that excellent example. Professor Tufte held a master class while I was at UCLA Design, and his belief that information should be comprehensive, clear and legible, while obvious, still holds for me significant poetic challenges. Being comprehensive is not about simply adding more information. Instead it's about removing unnecessary information so as to prioritize the most relevant information. My favorite example of late is the NYT 2008 ballot redesign. Not only is it clear and legible, but it successfully removes all the unnecessary information to make the remaining information more actionable. --> http://www.nytimes.com/interactive/2008/08/25/opinion/200808...

Something to keep in mind with Tufte's ideas is that, while he has many wonderful ways of thinking about visualizing data, often it's hard to generalize his concepts into a simple set of general purpose visualizations, suitable for use on a variety of problems. Instead, you find yourself seeking to find a way to display data in as "Tufte" a way as possible, which more of than not means a discrete visualization for every kind of data you have.

This becomes a problem in data visualization software, where the number of data visualizations is far less than the number of possible datasets of interest. So many datasets will tend to map to very few visualizations: bar chart, scatter chart, both with a time series, relatively uncomplex graphs of low connectivity density, etc.

You can see Tufte has this problem himself, with his singular "invention" the sparkline really just being a form of a tiny time series line graph. Which is cool, but it's not particularly earth shattering.

My takeaway has always been to be mindful of Tufte's analysis, but don't be slavish to it unless you can produce a unique visualization for each kind of data.

For a more stats oriented take on data analysis (and more pragmatically useful, I think), check out Bill Cleveland:

http://www.stat.purdue.edu/~wsc/

Note, yet ANOTHER brilliant guy to come out of Bell Labs.