Data at work: a data visualization book for Excel users

There are three things I pay attention to when I see people making charts in Excel:

#1: Aesthetics. We, Excel users and other mere mortals, aren’t artists or graphic designers, and although we should be aware of the role of aesthetics in our work, we can’t let aesthetics define it. We don’t have the right skills, and when we try to compensate, more often than not we fall into the greasy hands of pseudo-3D effects and other unsavory options. That’s why I almost never worry about aesthetics when making a chart. When I do, the result is as ugly as hell. I don’t have a drop of artistic talent in my blood (no, I don’t say it with pride.) I don’t like it when people tell me “Your charts are prettier than mine” (I hate it,  it’s frustrating, and I’m not alone, feeling that way.) I don’t even care if that’s true or not. What is important is that, if people turn making charts into a beauty contest they are totally missing the point.

#2: Defaults. Edward Tufte wrote about that “cognitive style of PowerPoint” that forces you to think and present in a certain way. Many argued that bad presentations  are presenter’s, not PowerPoint’s, fault. Problem is, when you’re offered a default behavior it’s easier to follow it than rebel against it. When you have to juggle so many things at the same time, choosing the path of least effort is hardly surprising. That same happens in Excel. Excel charts are more about aesthetics and canned wow effects to support sales pitches than about communicating effectively. Don’t take me wrong: Excel charts are so flexible that basically you can do whatever you want to do with them, and that’s great. Problem is, bad defaults induce a way of thinking about data visualization that spreads across the organization and beyond.

#3: Data structure. One of the worst side-effects of all that flexibility you find in Excel is that users don’t learn about the basics of structuring their data. Storing the data and presenting the data is often the same thing. If you want to do anything with the data other than reading the table you may need to spend hours cleaning until you can use it. In this case, it’s not Excel’s fault. Tables and pivot tables are there fore anyone to use.

When I began writing my book these topics were high on my list. The first one was especially painful, because we Excel users often suffer from a badly misplaced sense of aesthetics. Many pieces of the puzzle were still missing, and others had the wrong shape. I believe the resulting book communicates a clear, consistent and practical idea of what data visualization should be in a business context and helps to untangle the three knots above.

Table of contents

I already shared a draft version of the table of contents. This is the final version:

  1. The Building Blocks of Data Visualization
  2. Visual Perception
  3. Beyond Visual Perception
  4. Data Preparation
  5. Data Visualization
  6. Data Discovery, Analysis, and Communication
  7. How to Choose a Chart
  8. A Sense of Order
  9. Parts of a Whole
  10. Scattered Data
  11. Change Over Time
  12. Relationships
  13. Profiling
  14. Designing for Effectiveness
  15. Color: Beyond Aesthetics
  16. Conclusion

Chapters 1-6 discuss the foundations of data visualization, chapter 7 is a task-based classification of chart types, chapters 8-13 goes through each of the categories and chapters 14-15 are about design and aesthetics.

What’s different about the book?

There are many great data visualization books, so you can’t avoid some overlap. I was already writing the book when Robert Kosara wrote this post. I felt he was telling me I was on the right path:

  • I made almost all charts in the book, using Excel alone (no add-ins, no post-processing);
  • When discussing perception, there is a chart for every concept, not a generic image;
  • There are several bad examples, I explain why they are bad and offer a better alternative;
  • Lots of text to explain the why. I think there is a unique value proposition, but that’s for the readers to decide.

Not mentioned by Robert, but important:

  • All authors should do their best to share the data they used in there visualizations; I share the data and the charts.
  • I prefer real-world data instead of dummy data, and that’s what I used in the book.

The companion site is the companion site for the book. You’ll find a page for each chapter, and in each page you’ll find corrections (if any), links for the workbooks and a list of resources. I plan to keep adding relevant resources for each chapter.

I don’t explain how I made each chart because I’m unsure what level of explanation is required. So the idea is to interact with readers, answer your questions and write tutorials for the more complex charts.

Hope you like the book. Any comment, suggestion or review are much welcome!

Here is the link for Amazon and for the publisher’s page.