What makes good data visualizations?

Browsing through the web or attending conferences in the area of IT and marketing, one might get the impression that data science is ubiquitous. If somebody starts researching what data scientists actually do, they mostly come across terms like predictive modelling, machine learning and data mining. These terms sound sophisticated – and the activities they imply certainly are. However, apart from all modelling, algorithms and fancy tools it should not be overlooked what I think is a core task of a data scientist. They have to help people to understand data and support their decision making. To achieve this, data has to be presented in an understandable, “digestible” and convincing manner. This is where the field of data visualization comes into play which is an essential and indispensable aspect of doing data science.

Data visualization was invented long before the IT revolution – you could actually say it was not so long after the French revolution. Charles Joseph Minard pioneered the graphical illustration of information in the first half of the 19th century. A lot of his work is still inspiring today. In 1869, when he was already retired, Minard produced a chart that many regard as one of the best data visualizations ever made. On the very left, it shows Napoleon Bonaparte’s Grande Armée at the start of the 1812 Russia campaign. The thickness of the line represents the size of the army. In addition to the size of the army, the visualization incorporates the distance travelled by the soldiers, the temperature, direction of travel, latitude and longitude and the relative location. That is tons of information integrated in one beautiful and graspable visualization!

[Click here to see a version that can be enlarged]

What makes good data visualizations? Yale professor emeritus Edwart Tufte wrote in his book The Visual Display of Quantitative Information that “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.”  So far, so good. But what does that mean? Visualizing data is daily business for data analysts and data scientist. As with many things, it is combination of theoretical knowledge and good practices finally lead to good data visualizations. As far as the tools are concerned on the one hand, a lot depends on your weapons of choice. Regardless of whether you use R (e.g. package ggplot2), Python, STATA or “just” MS Excel (you can do a lot with it), you have to have a sound command of the tools to make things look as you want them to look. On the other hand, dealing with what makes good (and bad) data visualizations from a more theoretical standpoint is worthwhile. I can recommend some reads that I found insightful here:

• 8 Ways to Turn Good Data into Great Visualizations by Divya Manickam
• 5 Data Visualization Best Practices by Emily Rugaber
• If you want to dive deeper into human perception, read 39 studies about human perception in 30 minutes by Kennedy Elliott, a visual Journalist at Washington Post
• I have already mentioned Information is Beautiful in an earlier blog post. It is definitley worth visiting.
• For the German-speaking audience I can recommend the work coined by Dr. Rolf Hichert. See the company´s website for resources and background Information.
  
• Last, not least, you might wish to follow some of the leading thinkers in the field. Here is a list of data visualization influencers. We got to know Max Roser in the very first post of this blog.

As always, I´d be happy to hear about your opinions, views and good practices. In the meantime I wish you Happy Visualizing! :-)