Striving for Graphical Excellence with Edward Tufte

Landscape sculpture called "Dear Leader" created by Edward Tufte

​Visualizing data is an integral part of analysts´ and data scientist’s day-to-day life. Visualizations are not produced for the sake of beauty and design – at least not exclusively. One could say the common denominator for data visualization is to make it easier to process information for the human brain and therefore for the recipients. This might lead to better decision making (this is what is often called actionable insights), meaningful storytelling (e.g. in the area of data journalism in general but maybe also particularly in the context of NPOs) and the increase of so-called data literacy. One may like it or not but we live in a highly quantified society which means that often also "non-quantitative“ professionals across industries are required to consider data.

The final product that analysts are asked for by recipients are often charts and infographics. Visualizations also play an important role in the course of data science projects. In line with CRISP DM thinking, it is often data visualizations that help develop the so-called data understanding. Modern tools such as good old Excel or more integrated and holisitc solutions like Power BI make it possible to process large amounts of data from different sources with relative ease and in short time. We can therefore draw a preliminary conclusion: The need for visualization of data will persist and steadily grow, modern tools make life significantly easier in this regard. But what does it actually mean do come up with good data visualizations?

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​The good news is: There are various sources and thinkers one can turn to get inspirations and recommendations in the context of data visualization and information design. In this month’s blog post, we will take a close look at the work of Edward Tufte. Tufte is an American statistician and professor emeritus of political science, statistics, and computer science at Yale University. He is one of the most influential contemporary thinkers in the field of information design and data visualization. The New York Times went as far as to call him the “Da Vinci of Data” in 1998. More than 35 year ago, Tufte published the first edition of The Visual Display of Quantitative Information which has become a classic on statistical graphics, charts, tables. Tufte is also known for some easy to remember quotes such as:

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"If the statistics are boring,
then you've got the wrong numbers."​
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Tufte has coined the idea of Graphical Excellence. Graphical Excellence means the efficient communication of complex quantitative ideas towards recipients. This requires clarity, precision and efficiency. What does efficiency mean in this context? The viewer should be given the greatest number of ideas in the shortest time with the least ink in the smallest space. You could say this is the application of a minimalist and “less is more” philosophy in the context of data visualization. 
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​Graphical excellence is the well-designed presentation of interesting data - a matter of underlying data, of statistics and of design. One could say in data visualization, data is not everything but without the appropriate data, everything is nothing. Data and the messages derived from it have to be correct. Tufte uses the term integrity in this regard. There is a plethora of sources on how to lie with statistics. Data has to be relevant for the respective viewer. As mentioned above, Tufte went as far as to say that if the statistics are perceived as boring, then you've got the wrong numbers. 

When it comes to design, Tufte suggests the following things that graphics should do:

• Show the data
  • This sounds trivial but contains a strong message. Refrain from adding information, styling etc. that does not contribute to understanding the underlying data
• Make the viewer think about the substance, rather than about methodology, graphic design, the technology used etc.
  • This is honestly hard to bear for some of us analysts, particularly if you are an aficionado to one tool. The viewer should neither think nor care about “Oh, was that made with XY?” but feel invited to dive into the chart from the very beginning.
• Avoid distorting what the data have to say
  • There is a whole body of literature on how (not) to lie with data. An interesting slide deck worth browsing that we have seen in this reard is from David Newbury. 
• Present many numbers in a small space  
  • This is what Tufte calls an optimized "ink ratio", a concept he already ​introduced in his early works.
• Make large data sets coherent
  • ​This is what we would call making the underlying data "digestable" for the recipient. It requires a good balance of abstraction and simplification without losing the focus on key insights.
• Intive the viewer to compare different pieces of data
  • ​This can be achied by adequate design, use of colors, annotations etc.
• Reveal the data at several levels of detail
  • This is something particularly modern data visualization tools like Power BI are good at as the provide functionalities to drill-down and drill-through into details.

"Design cannot rescue failed content!"

... is another striking quote by Tufte. We tried to put togehter an interesting (hopefully!) slideshow with some "evergreen" data visualizations and inspiring works from the recent past.

​Have a look, enjoy and read you next time!