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Now You See It: Simple Visualization Techniques for Quantitative Analysis Hardcover – April 1, 2009
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The first half of the book has a different focus than I expected. Few suggests that "...we've largely ignored the primary tool that makes information meaningful and useful: the human brain. While concentrating on the technologies, we've forgotten the human skills that are required to make sense of the data." He describes the human visual system, how it processes information, and the errors in perception it sometimes makes. His emphasis, however, is on the strengths of visual perception which he links to best practices in data analysis. One of the most useful parts of this section is in Chapter 2, where he lists and describes the "aptitudes and attitudes of effective analysts."
The book's second half describes and illustrates specific visual analysis techniques. It is rich with visual examples, comparisons of effective and ineffective displays, and series of related visualizations which show incremental steps of data transformation and analysis. Chapters are organized by specific data patterns and analytical techniques, describing how to look for the following six kinds of patterns:
- Ranking and part-to-whole relationships
- Patterns in multivariate data
Two final chapters present recommendations for developers of data analysis software and make predictions about future trends in visual data analysis.
The book is recommended for any researcher who works with large data sets. It is well-written, contains clear examples, and references recent research and the latest tools available for data analysis. Readers may also be interested in Few's Show Me the Numbers: Designing Tables and Graphs to Enlighten which discusses how to best describe patterns in data to nonresearchers.
The author's (achieved) goal was to provide a number of methods by which that data can be used more effectively. But he does more than provide a catalog of methods. He shows how some of them are better in one situation, and that other methods are better in other situations or when the presenter's goals are different. By doing so, he has also given us a very nice set of tools for determining if the next graph or chart is failing to provide all it can or if the presenter is misleading his audience. Mr. Few provides figures on nearly every page of the book and does an outstanding job of explaining them.
This book will be referred to often in my career.
If you're new to creating charts, have a deviant's level of interest in data geekery, or need this book for a class, get it. And get it in print. The Kindle version doesn't have the same impact.