Edward Tufte –Data Visualization Pioneer
Who is Edward Tufte?
Edward Tufte is a statistician and artist, and Professor Emeritus of Political Science, Statistics, and Computer Science at Yale University. He wrote, designed, and self-published 4 classic books on data visualization. The New York Times described Tufte as the “Leonardo da Vinci of data,” and Bloomberg as the “Galileo of graphics.” He is now writing a book/film The Thinking Eye and constructing a 234-acre tree farm and sculpture park in northwest Connecticut, which will show his artworks and remain open space in perpetuity.
When & where was he born?
Tufte was born on March 14, 1942; in Kansas City, Missouri and grew up in Beverly Hills, California, where his father was a longtime city official.
Education
Tufte graduated from Beverly Hills High School. He received a BS and MS in statistics from Stanford University and a PhD in political science from Yale. His dissertation, completed in 1968, was entitled The Civil Rights Movement and Its Opposition.
Career
He was hired by Princeton University’s Woodrow Wilson School, where he taught courses in political economy and data analysis while publishing three quantitatively inclined political science books.
In 1975, while at Princeton, Tufte was asked to teach a statistics course to a group of journalists who were visiting the school to study economics. He developed a set of readings and lectures on statistical graphics, which he further developed in joint seminars he taught with renowned statistician John Tukey, a pioneer in the field of information design. These course materials became the foundation for his first book on information design, The Visual Display of Quantitative Information.
Author
After difficult negotiations with mainline publishers failed, Tufte decided to self-publish Visual Display in 1982. He financed the work by taking out a second mortgage on his home. The book quickly became a commercial success and secured his transition from political scientist to information expert.
Other achievements
On March 5, 2010, President Barack Obama appointed Tufte to the American Recovery and Reinvestment Act’s Recovery Independent Advisory Panel “to provide transparency in the use of Recovery-related funds”.
Self-criticism
He is intensely critical in the self-editing process. He pulls in and casts out ideas from books, journals, posters, auction catalogs, and other less common sources. He invites others to critique his work in-progress and may nurture dozens of ideas over months in various states of growth and fruition. He deletes almost every photograph he takes. Over time, he deletes most of what he writes on his own forum, ET Notebooks. Every printing of every book corrects numerous small blemishes, ranging from color registration to kerning and hinting.
Chartjunk and data-ink ratio
Tufte coined the word chart-junk to refer to useless, non-informative, or information-obscuring elements of quantitative information displays. Tufte’s other key concepts include what he calls the lie factor, the data-ink ratio, and the data density of a graphic. He uses the term “data-ink ratio” to argue against using excessive decoration in visual displays of quantitative information
“Sometimes decoration can help editorialize about the substance of the graphic. But it is wrong to distort the data measures — the ink locating values of numbers — in order to make an editorial comment or fit a decorative scheme.” — Visual Display, Edward Tufte
Small multiples & sparklines
Small multiples — allows quick visual comparison of multiple series is the small multiple, a chart with many series shown on a single pair of axes that can often be easier to read when displayed as several separate pairs of axes placed next to each other. Tufte suggests this is particularly helpful when the series are measured on quite different vertical (y-axis) scales, but over the same range on the horizontal x-axis (usually time).
Sparkline — Though Tufte didn’t actually invent the sparkline chart, he did invent the name and popularized it as technique. Sparklines are a condensed way to present trends and variation, associated with a measurement such as average temperature or stock market activity, often embedded directly in the text; to show fluctuations.
Exploratory analysis
When working with data, we must be careful to not impart our bias into the analysis and to let the data speak. As stated by Tufte in the Microsoft Machine Learning & Data Science Summit 2016: “We should be mucking around in our data to find out what’s going on. We can learn from it. We can run it through powerful exploratory things. We can run it like a map through millennial time and look it over and say, that look interesting….and what this means, though, this kind of searching, is that you must have an honest replication of the results of the search. To go back on innocent data, maybe somebody 500 miles away does it. Maybe that’s better, independent replication of the search results to distinguish now between noise and signal.”
Data visualization guidelines
· Remove clutter; above all else show the data…erase everything you don’t need. Less is more when putting together a data visualization/ dashboard.
· Don’t use pie charts; they tend to confuse the audience and makes it difficult to interpret the results.
· If you don’t have a lot of data to work with, don’t use a graphic; you can use a table or even just the actual number on the screen to tell your story.
· Communicate complex ideas with clarity, precision, and efficiency
· You must give the audience the greatest number of ideas in the shortest time with the least ink in the smallest space.
· Avoid distortion. Be fair with the data and present it without twisting it to fit your agenda.
· Use stacked charts to see values over a time period.
· Use horizontal bar charts to see values over a time period, primarily if you need longer text descriptions. Vertical ones have little space for text.
· Limit your color palette. While finding a great palette is an art unto itself, make sure readers can easily distinguish between the different hues you use in the graphic.
· Induce the viewer to think about the substance rather than about methodology, graphic design, the tech of graphic production, or something else.
