A new book, Data Points: Visualization That Means Something, by Nathan Yau is a good guide and reference book for any reader that needs to present data to an audience in proposals, in their own books or for persons interested in putting stats into graphical form. The author starts out by explaining what good visualization is all about.
He writes about visualization, “It is a representation of data that helps you see what you otherwise you would have been blind to if you looked at only the naked source. It enables you to see trends, patterns, and outliers that tell you about yourself and what surrounds you.”
The book is about the process of design and analysis, “where each chapter represents a step toward visualization that means something.”
The author tells readers that he refers to visualization throughout the book as a medium versus calling it a specific tool. He offers rules and suggestions for each visualization type. The first chapter gives a tutorial on what data represents and how to understand the data at hand.
“Data is more than numbers, and to visualize it, you must know what it represents. Data represents real life. It’s a snapshot of the world in the very same way that a photograph captures a small moment in time,” Yau writes.
He offers plenty of visualization of his ideas in the book with many graphical charts and ways to present samples of data that he uses for case studies in the book. The book has plenty of details about using entertainment type graphics and data art along with a twist on many standards such as bar graphs and pie charts.
Yau offers a thorough section on visualization components detailing visual cues, positioning, scale, context and the working parts of graphs. The last part of the book looks at programming, illustration and other related tools.
There is a chapter on designing data presentations for the audience that is helpful. Yau writes, “There are visualization types that have been around for decades. Think bar charts, pie charts, dot plots, and other usual suspects. People are accustomed to reading data through these traditional forms, but some see this as a negative.”
He suggests that “visualization can be appreciated purely from an aesthetic point of view.” But he adds that the visual form is most interesting when it represents data that is worth looking at. Start with the data, explore it and then show the results he recommends.
Data background, guidance for concepts, data narrative, reliability and of course the visualization are all components that author recommends one needs to consider when presenting data.
While the book is filled with plenty of graphics and is written in an easy to read language, it does contain some technical language. For readers who have difficulty finding an interesting way to graphically represent data, the book can help.
Some of the graphics Yau presents will get readers excited. After reading this book, readers may get pretty bored and be pretty critical of data presentations they view at their next meeting or conference.