Information Is Beautiful: A Celebration of Data?

Yes, information can be beautiful, and David McCandless’s book and website Information Is Beautiful argue exactly that—that well-designed data visualization is not just informative but genuinely aesthetic. The project collects charts, graphs, and diagrams that turn raw statistics into colorful, often surprising visual stories, making complex topics accessible at a glance.

What “Information Is Beautiful� Actually Is

Information Is Beautiful began as a blog by British data journalist David McCandless in 2007, later becoming a bestselling book in 2012. The core idea is that data, when properly visualized, can reveal patterns and truths that are hidden in spreadsheets. The title itself is a manifesto: it suggests that clarity and elegance in presenting information are forms of beauty, not just utility.

The project covers topics as varied as the relative calorie content of fast food, the history of conspiracy theories, the cost of military conflicts, and the best times to post on social media. Each visualization is designed to be self-contained, often using color, size, and position to encode multiple dimensions of data simultaneously. The goal is to make the viewer think, “I never saw it that way before.�

How the Visualizations Are Made

McCandless and his team follow a distinctive process that blends journalism, design, and data analysis:

• Research: Data is gathered from public sources—government reports, academic studies, industry surveys, and news articles. The sources are cited on the website, though not always with the rigor of a formal academic paper.
• Data cleaning: Numbers are normalized, units are converted, and gaps are filled or noted. This is often the most time-consuming step.
• Sketching: The team experiments with different visual metaphors—bubbles, trees, maps, timelines—to find one that fits the data’s shape and the story they want to tell.
• Design: Using software like Adobe Illustrator, the final graphic is rendered with a consistent, colorful style. Labels, annotations, and a title complete the piece.

The result is a visual that can be read at multiple levels: a quick glance gives an overall impression, while closer inspection reveals specific numbers and comparisons.

Why Data Visualization Matters

Humans are visual creatures. We process images much faster than text or numbers, and well-designed charts can reveal trends, outliers, and relationships that would take hours to find in a table. For example, a bubble chart showing the relationship between a country’s healthcare spending and life expectancy immediately shows clusters and exceptions—like the United States spending far more than peers but not achieving proportionally longer lives.

Beyond speed, visualization can also make data memorable. A striking graphic is more likely to be shared and remembered than a paragraph of statistics. This is why news outlets, NGOs, and even government agencies increasingly invest in data visualization teams. The best visualizations don’t just present data; they tell a story, prompting the viewer to ask new questions.

Common Criticisms and Limitations

Despite its popularity, the “information is beautiful� approach has faced criticism. Some argue that the pursuit of aesthetics can oversimplify complex issues, losing important nuance. For instance, a beautiful infographic about income inequality might flatten the differences between urban and rural areas, or between age groups, to fit a clean visual narrative.

Another concern is the potential for misleading design. Color choices, axis scaling, and even the shape of a graphic can subtly influence interpretation. A bubble chart, for example, encodes values in area, but viewers often perceive the diameter instead, leading to exaggerated differences. McCandless has addressed some of these criticisms by adding source notes and caveats, but the tension between beauty and accuracy remains a central debate in the field.

How to Read and Evaluate an Infographic

To get the most out of any data visualization—whether from Information Is Beautiful or elsewhere—ask these questions:

• What is the source? Is it a primary source (like a government survey) or a secondary one (like a news article)? A good graphic will cite its sources.
• What is being compared? Are the categories clearly defined? Are the units consistent?
• What is the scale? Does the graphic start at zero? If not, differences may be exaggerated.
• What is left out? Are there missing data points, or time periods that are excluded? Why might that be?
• What is the message? Does the graphic lead you to a specific conclusion? Could the same data support a different interpretation?

By applying these checks, you can separate genuinely informative visuals from those that are merely pretty or even misleading.

Alternatives and Related Projects

Information Is Beautiful is not alone in the field. Several other projects share a similar ethos:

• The Guardian’s Data Blog and The New York Times’s Upshot produce interactive visualizations for news stories, often with a more journalistic rigor.
• Gapminder, founded by Hans Rosling, uses animated bubble charts to show global development trends, emphasizing the importance of data literacy.
• FlowingData is a blog that teaches people how to create their own visualizations using programming tools like R and Python.
• Tableau Public and Datawrapper are tools that allow anyone to create and share charts without coding.

These alternatives range from purely artistic to strictly analytical, but all share the belief that data can be made more accessible and compelling through design.

Frequently Asked Questions

Is “Information Is Beautiful� a book or a website?

Both. The website (informationisbeautiful.net) has been running since 2007 and features many visualizations for free. The book, published in 2012, is a curated collection of some of the best graphics, organized by theme like “Life,� “Death,� and “Money.� A second book, Knowledge Is Beautiful, followed in 2014.

Does David McCandless have a background in data science?

No, his background is in design and journalism. He studied graphic design and worked as a creative director before turning to data visualization. This outsider perspective is often credited with giving his work a fresh, accessible style that differs from more academic approaches.

Are the visualizations always accurate?

Most are based on cited sources, but some have been criticized for errors or misleading design choices. McCandless has a “corrections� section on his site where he addresses mistakes. As with any secondary source, it’s wise to verify important claims with the original data.

Can I use the visualizations for my own projects?

The graphics are copyrighted, but the site offers a “licensing� page for permission. For educational, non-commercial use, you may be able to use them with attribution, but it’s best to contact them directly. Many of the underlying datasets are also available for download, which is a great resource for learning.

What is the most famous visualization from the site?

One of the most shared is “The Billion Dollar-o-Gram,� which compares the scale of various large expenditures—like the Iraq War, global HIV/AIDS spending, and corporate bailouts—using colored rectangles. It’s a striking example of how relative scale can be made tangible.

Does the project make money?

The website is supported by book sales, speaking engagements, and licensing fees. There is also a “support us� page where readers can make a donation. The visualizations themselves are freely accessible, which aligns with the project’s mission of spreading data literacy.

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