Bad data visualization is when a chart, graph, or map confuses people instead of helping them understand the data.
For example, a messy pie chart in an email can hide important sales details, making it hard for the reader to see what matters.
But, to understand why some of the pervasive errors occur, let’s first understand data visualization as a concept.
What is data visualization?
According to Gartner – data visualization presents information graphically that highlights patterns and trends in data. It helps the viewers gain quick insights into data analysis.
It revolves around both data science and art. While the analytical brain is required to understand the data and analyze it, art plays a crucial role in representing it in appealing visuals.
To avoid data visualization errors, it’s required that you strike the right chord between data points and presentation. Using too many colors, incorrect charts, and presenting too much information through one graph are some of the mistakes that data analysts often make. And these, in turn, will make a decision-maker’s life more difficult than easier!
Why Does Bad Data Visualization Happen?
Bad data visualization happens because of poor design choices. A 2023 study from the University of Washington found that 68% of these mistakes come from picking the wrong chart or cramming too much into one visual. Here’s why it goes wrong:
- Too much info in one chart, like squeezing 20 datasets into a tiny bar graph.
- Wrong chart type, like using a pie chart to show changes over time instead of a line chart.
- Bad colors, like red and green together, which can be hard for some people to tell apart.
- Missing labels, like not showing what the numbers on a graph mean.
- Messing up the data, like adding up numbers that don’t match.
- Using the wrong tool, like trying to make a complicated 3D chart in Excel.
- Not explaining the point, so people don’t know what to focus on.
- Not thinking about who’s looking at it, like showing a super technical chart to someone new to data.
The most common bad data visualization examples
1. Using Incorrect Axis Ranges
Incorrect axis ranges mislead viewers by exaggerating differences. Bar charts are very commonly used, and most viewers conclude by looking at the height of the bars. But, when the graph producer doesn’t follow the conventional method of starting the axis at 0, focusing on the height may mislead you into believing the differences as much higher than they are.
For example, a chart showing sales might make a small increase look huge if the y-axis starts at 50 instead of 0. Also, uneven intervals—like 0, 100, 150, 200—look nice but break the chart’s purpose.
- Fix: Start the y-axis at 0 and keep the interval between coordinates the same.
2. Trying to Be Extra-Creative
Overly creative designs can hide the data’s meaning. Charts and graphs used in dashboards are meant to give you a correct and precise idea of the underlying data.
At times, analysts go overboard with the design and lose their focus on the standardized data analysis rules and misuse charts and graphs – resulting in the biggest data visualization mistakes.
Thus, it’s crucial to know the graphs’ purposes and when to use each one of them. For instance, when visualizing time series, using a pie chart is a bad option. Though it might look perfect on the dashboard.
Pie charts are useful when there are five or fewer parameters. Each parameter should be comparable and also have a relationship with the whole. Line charts and line graphs represent time series in the best possible method.
The given figure might look very colorful, but it isn’t very meaningful because by looking at it, the viewer will have more questions than answers. This is the perfect example of misleading data visualization.
A bright, busy chart might look good but leaves viewers with more questions than answers.
- Fix: Use the right graph for the job—line charts for trends, pie charts for simple proportions—and avoid overdesigning.
3. Not Using Labels
Missing labels make graphs hard to understand. Without clear labels, people guess what the numbers mean. In Power BI, forgetting to turn on data labels in the “format” section is a common slip-up.
Just implementing data labels also isn’t enough; they should be decipherable. For instance, if labels are inside the visuals, using contrast colors helps. Imagine while presenting your retention rate; if the percentage is visible as 8% instead of 80%, what impression it have on viewers’ minds?
- Fix: Add a clear title, use visible labels with contrasting colors, and double-check readability.
4. Too Many Colors, Shapes, and Texts
Too many colors, shapes, and words clutter dashboards and confuse readers. The same applies to too many shapes and lengthy texts. A map with a massive number of tiny geographic shapes, glaring colors, and lengthy text legends will confuse the reader’s mind.
However, simple dashboards communicate the message more effectively.
- Use bold colors only for key data, keep shapes simple, add white space, and standardize colors across charts (e.g., blue for the USA every time).
- Colors have the power to evoke a host of emotions – from positivity, trust, strength, confidence, and friendliness to fear, doubt, concern, and boredom. So, while displaying sensitive information with colors, study the cultural background of your audience.
- Keep the colors in sync with the company’s brand image.
- Use images scarcely and when there’s sufficient white space on the dashboard.
- Specific colors have psychological associations. For instance, green gives the impression of positivity like profits, and red is related to losses or KPI missouts.
- Standardize colors. If you are showing the USA in blue in one graph, keep it persistent throughout the dashboard.
5. Information Congestion
Too much data in one chart overwhelms viewers. According to a study by George Miller of Harvard, the human mind can handle seven pieces of information on average, which can be more if the information is chunked.
Furthermore, a chart loaded with too much information is likely to puzzle your viewers.
- Fix: Instead of cluttering all information in one chart, it’s best to divide it into multiple charts, which together will tell the story to the viewer.
What Are Some Examples of Bad Data Visualization in Emails?
In emails, bad data visualization can look like messy pie charts or tricky line charts. An Indian online store sent an email with a pie chart showing 15 product categories in similar colors with labels that overlapped, making it tough to read on a phone.
Another email from a mobile app showed a tiny 1% increase in user activity as a huge jump by starting the graph at 80%, which made the results look better than they were.
How Can You Fix These Examples?
Make the visuals simpler and more honest. For the pie chart, switch to a bar chart with just the top 5 categories in clear colors. For the line chart, start the graph at 0% and add labels so people can see the real change.
Why Does Bad Data Visualization Cause Problems?
Bad data visualization leads to misunderstandings. A SoftServe study revealed that 65% of business leaders believe no one in their organization fully understands the aggregated data or how to access it.
Additionally, 58% reported making key business decisions based on inaccurate or inconsistent data because of misleading charts, like a bar graph that made a small sales increase look huge by cutting off the bottom, leading them to spend too much on a product that wasn’t doing well.
What Makes a Data Visualization a Bad Example?
- Too much stuff, like a scatter plot with 100 points and no labels.
- Wrong chart types, like a 3D pie chart for simple numbers.
- Confusing colors, like using shades that are too similar.
- No explanations, like a line chart with no dates.
- Scales that trick the eye, like starting a graph at 50% instead of 0%.
How Can You Stop Making Bad Visualization Mistakes?
- Keep it simple by showing less, like just the top 3 trends.
- Pick the right chart, like a line chart for tracking changes over time.
- Use easy-to-see colors, like blue and orange.
- Add labels, a title, and a legend so people know what’s what.
- Make sure the scale is fair, like starting at 0 for most graphs.
- Check how it looks on phones and computers.
- Think about who’s seeing it and make it easy for them to understand.
Why Should You Care About Good Data Visualization Practices?
Good data visualization practices make things clearer and help people decide better. A 2023 Tableau survey found that businesses using these tips made 40% more decisions based on data. Here’s what you get:
- Better understanding of big data with clear dashboards.
- Faster decisions using easy visuals, like heatmaps.
- Spotting trends, like a sales jump in a line chart.
- Finding mistakes, like weird numbers in a scatter plot.
- Managing things in real time with live dashboards.
- Telling better stories with fun visuals like infographics.
- Focusing on what matters with bar charts.
- Seeing new trends early with time-based visuals.
- Saving time on reports with tools like Power BI.
What Tools Can Help You Avoid Bad Visualization Mistakes?
- Suggesting the right chart, like Tableau picking a bar chart for categories.
- Colors that everyone can see, like in Power BI.
- Cleaning up data in Excel for simpler charts.
- Live updates in Tableau for fresh info.
- Guiding people through the data in Power BI.
What’s Coming for Data Visualization in 2025 and Beyond?
Data visualization will get better with AI, VR, and more focus on everyone using it. With technological advancements and rising user expectations, these are some trends worth monitoring:
- Artificial Intelligence and Automation
The automated creation of data visualizations will increasingly be driven by AI, providing instant insights and surfacing deeper patterns with minimal human effort. - VR and AR Data Visualization
VR and AR will change the way we work with data, allowing you to move around in a 3D atmosphere and intuitive experience complex data sets.
Accessibility and Inclusivity
Designing visualizations that are accessible to everyone — especially users with visual, motor or cognitive impairments — will be among the most important priorities in 2025, striving for inclusivity in how we convey data.
Conclusion
Bad data visualization examples, like tricky line charts or messy pie charts, makes it hard to understand data and can lead to mistakes. By knowing what goes wrong—like cramming too much in or using the wrong chart—and using simple tips with tools like Tableau, you can make visuals that are easy to understand and help make better choices.
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