Review of The Visual Display of Quantitative Information

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I work as a data analyst and part of my job is to create dashboards for our clients. Dashboards are how we communicate our findings in the data to the relevant stakeholders. Graphic design and style are not strengths of mine. I am more of a coder and math-person. Tufte’s The Visual Display of Quantitative Information was recommended to me as a way to up my dashboard making game. Reading this book has certainly made my dashboards clearer and more informative.

The book is interesting, well laid out, and full of great graphics. It’s an aesthetically pleasing book.

“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.”

The book is full of great information. It’s not a how-to book filled with best practices. If you’re looking for a step-by-step guide on how to make better graphs, this isn’t the book for you. Instead, it’s an examination of principles that could/should be applied to data graphics. It floats concepts and formulas out there to be considered when crafting a data graphic.

“Tables are preferable to graphics for many small data sets. A table is nearly always better than a dumb pie chart the only worse design than a pie chart is several of them…pie charts should never be used.”

That is not to say there is not practical advice to be gleaned from this book. He has effectively dissuaded me from the pie chart. I was already in that camp, now I’m the leader of the camp.

Tufte has also convinced me in the power of tables. I used to think tables were bland, inelegant, and ineffective. But he makes great sense about when to use tables(It’s with smaller datasets). And Tufte has converted me, and I am now pro-table.

On a smaller note he also convinced me to use serif fonts, instead of my preferred sans-serif. He’s right. They’re clearer fonts.

I have one issue with his book. Tufte will often introduce a concept like the Data-ink Ratio. The concept is that you should try to maximize ink used on data, relative to the total ink used on the graphic. It’s a great rule-of-thumb. However, Tufte in the book takes it too far. He takes an inefficient graph and turns it into an efficient graph with this concept. Then he takes the efficient graph and turns it into an oversimplified confusing graph with the same concept. He cranks his concept to 11, and it becomes too much.

My criticism is a bit unfair. His epilogue alludes that perhaps taking his concepts literally is a bad idea.

The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper.

My criticism is certainly of a peevish spirit. I think showing the logical ends of his concepts is an appropriate demonstration of their use, albeit impractical. This is a book on theory, not a manual. I often forget that.

But I think this is a symptom of a broader issue with this topic. Data Visualization is at the intersect of art and mathematics. These enemies make uncomfortable bedfellows. Art is concerned with beauty and elegance. Mathematics, however, is perfectly logical. It cares not for aesthetics.

This intersection is why there are so many terrible data visualizations. Most data graphics are either too artistic or too mathematical. The successful marriage of the two is no easy task. Because of that, I find Tufte’s book to be excellent. It is a great starting point for marrying the two disciplines. This book is filled with valuable concepts that will greatly increase one’s skill with regards to data visualizations. 

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