Bad data visualization practices to avoid

Nowadays, information moves faster than attention spans, and data visualization is an essential tool for communicating insights clearly and quickly. But when done poorly, it can do more harm than good. Bad data visualization doesn’t just confuse; it can mislead, frustrate, or even discredit your work entirely.

To ensure your visuals support your message (instead of working against it), here are the most common pitfalls to avoid and how to address them.

1. Misleading Scales

Truncated axes or inconsistent intervals can bias the viewer’s interpretation. For example, starting a bar chart’s Y-axis at 50 instead of 0 might exaggerate minor differences, making them appear more significant than they are.

Tip: Always consider how scale influences perception. If there’s a compelling reason to use a non-zero baseline, make it clear with labels or annotations.

2. Overuse of 3D Effects

While 3D charts may seem more engaging, they often distort the data, making values harder to compare accurately. They can also create unnecessary visual noise.

Tip: Stick with 2D charts unless 3D serves a specific, meaningful purpose. Clarity should always come before aesthetics.

3. Too Much Color

Colors can emphasize important data points, but too many colors can overwhelm or confuse viewers. Worse, it may make your graphic inaccessible to those with color vision deficiencies.

Tip: Use a consistent color scheme with deliberate contrast. Consider using pattern fills or labels in addition to color to ensure readability.

4. Choosing the Wrong Chart Type

Not every dataset works well in every format. Pie charts, for example, often make it difficult to compare similar values. Using the wrong chart can make data harder to interpret.

Tip: Let the structure of your data determine your chart. If you’re comparing quantities, bar charts work well. For trends over time, line charts are generally more effective. Infogram’s AI Chart Suggestions can help with this, giving you the best chart specifically for your data type.

5. Information Overload

Cramming too much data into one visual can confuse the audience and bury your key message.

Tip: Focus on one main insight per graphic. Break complex stories into multiple, simple visuals. Less is almost always more.

6. Lack of Context

Charts that lack titles, labels, sources, or explanations leave the audience guessing. Even well-designed graphics can fall flat if the context is missing.

Tip: Always provide a descriptive title, label your axes, and cite your data sources. If there’s a story behind the numbers, guide the viewer with annotations or captions.

7. Ignoring Mobile Users

Many charts are designed for large screens but are viewed on small ones. If your visuals aren’t mobile-responsive, you risk alienating a significant portion of your audience.

Tip: Choose platforms or tools that support responsive design. Keep text legible and avoid overly complex visuals that don’t scale well.

Why Avoiding Bad Data Visualization Practices Matters

Bad data visualization isn’t just a design issue; it’s a communication issue. When visualizations confuse rather than clarify, they create friction between the presenter and the audience. Over time, repeated mistakes can erode trust in your data and your credibility.

For professionals who regularly share reports, insights, or public-facing content, avoiding these mistakes is critical. Simple, well-structured visuals not only improve comprehension but also encourage action, whether that means making a decision, understanding a trend, or sharing your findings.

A Smarter Way to Visualize

You don’t have to be a designer to avoid these mistakes. Tools like Infogram are built with these best practices in mind, offering templates and charts that guide users toward clean, accurate, and accessible data visualizations.

More importantly, choosing the right tool can help you focus on your message instead of worrying about formatting details.

Final Thoughts

In the world of data communication, how you show your information is just as important as what the data says. By avoiding common pitfalls associated with bad data visualization, you can ensure your audience not only sees your data but also understands and trusts it.

Clear, honest visuals help tell better stories, and better stories lead to smarter decisions.