Examples of Data Visualization - Great and Bad ones

Explore good, bad, and ugly examples of data visualizations, along with best practices to improve your data storytelling

Examples of Data Visualization - Great and Bad ones

According to an MIT study, humans can identify images in as little as 13 milliseconds, around 20 times faster than reading text or raw data. 

We also retain visual information much better, with molecular biologist John Medina stating:

“When people hear information, they're likely to remember only 10% of that information three days later. However, if a relevant image is paired with that same information, people retain 65% of the information three days later.” – Brain Rules book by John Medina.

That’s why data visualizations are so useful when trying to communicate data insights. 

By transforming numbers and facts into visual data stories, we help people to understand and retain the info. Check out our guide – what is data storytelling?

However, you can’t just throw together a few charts and hope that they work together. You need to plan the story, create engaging interactive data visualization examples, and put them together in a way that engages your audience.

In this guide, we’re going to look at the good, the bad, and the ugly data visualization examples to help you along the way.

The Power of Good Data Visualization

Imagine these two examples of the same scenario, a board meeting in which a colleague is presenting last quarter’s sales figures:

  • Example 1: A typical PowerPoint presentation displaying graphs and some bullet points for talking points.
  • Example 2: An interactive data visualization, that shows an animated line graph of sales that zooms in on the data when a product is launched. Then it morphs into a pie chart showing market share. 

Most people would agree that example two is much more engaging and dynamic. However, it must follow some core data visualization tips to qualify as a good example of a data visualization.

Core Elements of a Good Data Visualization

  • Accuracy: The most important aspect of a good data visualization is that it must represent the data truthfully, without distortions or misleading scales.
  • Clarity: The viewer should be able to understand the data and the message behind it quickly and easily. Avoid any excess clutter or overly complex visuals.
  • Relevance: The visualization should address the question or speak to the issue at hand, highlighting the most important insights.
  • Appropriate Chart Type: You can go for famous charts like line graphs for trends, bar charts for comparisons, scatter plots for relationships... Choosing the right chart is essential for clarity.
  • Focus: Guide the viewer's eye to the key takeaways using color, size, annotations, and a clear visual hierarchy.

Elevating Good to Great

  • Aesthetics: Well-designed animated data visualizations are easier to process and more appealing. Pay attention to color schemes, fonts, and spacing.
  • Storytelling: Don't just show the data, guide the viewer through a narrative with clear titles, annotations, and logical transitions between visuals.
  • Audience Focus: Tailor complexity, language, and visuals to your audience's knowledge level and goals.
  • Interactivity (When Appropriate): Allowing users to explore, filter, and drill down into the data can unlock deeper insights.
  • Context: Provide background information and labels where needed to ensure proper interpretation.

Common Pitfalls in Data Visualization

Data visualizations can fall victim to issues such as misleading scales, too much visual trickery, or overwhelming visual clutter. Being able to spot these issues takes skill and attention. Always make sure you run your data visualizations past some unbiased viewers before presenting them. If your test viewers are left scratching their heads or seem overwhelmed, then you know that it isn’t working as intended.

Some data visualization basics to watch out for include:

  • Charts with chopped-off starting points
  • Explosions of color that distract from the data
  • 3D effects that skew proportions
  • Unclear or inconsistent scale

To avoid these common pitfalls, always focus on the fundamentals: clarity and accuracy. Choose the right chart type for the data story you're telling and keep things as simple as possible, prioritizing clear communication over visual flair. 

Now let’s take a look at some good data visualization real life examples and bad data visualization design examples.

Good Examples of Data Visualization

Example 1: 2020 US Election Data

Back in 2020, the eyes of the world were on the run up to the US presidential election. Rather than just showing the dry data or static graphs with a recorded commentary over the top, Vizzu went a step further and created an engaging interactive data visualization to tell the story. The animated data and beautiful graphs painted a clear picture of the polls compared with the 2016 election and forecasted a win for the Democrat party, a prediction that was later proven correct.

Example 2: An Interactive Journey Through Billboard Top 5 Hits Since 1960

Source: The Pudding

This creative interactive data visualization displays a scrolling representation of the top five billboard hits throughout history from 1960 to 2019, while playing the music from the number one hit at any given time, as well as data on how long each held the top spot. You can simply click to jump directly to an era in musical history and listen to the tunes that shaped that generation.

Example 3: Exploring the Science of Selfies

Source: SelfieCity

This light-hearted cultural study analyzed a collection of 1000’s of selfies from five cities around the world for various features and presented them in an engaging interactive data visualization. You can adjust the filters to see how many selfies match your settings. You can discover the hidden stories behind the data yourself. For example, you can select an angry expression setting and change the city one-by-one to find out which part of the world takes the angriest selfies!

Bad Examples of Data Visualization

Example 1: Confusing Colors

Source: Awful data visualizations

The data visualization shown above is supposed to show the most-used brand colors in a nice, appealing way. However, it quickly becomes confusing as soon as you try to interpret the data. At initial glance, you might think that the different size circles represent different proportions, but a closer look reveals that 13% is bigger than 33%, so this isn’t the case. The overlapping circles look like Venn diagrams, which is also misleading. A bar chart would have been a much safer option here and would communicate the data story much more clearly.

Example2: Fake News

Source: Fox News

A rundown of some of the worst data visualizations wouldn’t be complete without a Fox News chart! The example above is a classic cut-off bar chart, a tactic Fox News has used often to try and manipulate the data story. There is no scale on the graph, allowing them to present the gap between the actual enrollment as much bigger than it actually is proportionally. The gap appears to be at least three times the figure of 6,000,000, when in fact it is approximately one-sixth of the amount. 

Ugly Examples of Data Visualization

Example 1: Making Your Audience Work Too Hard

Source: Awful data visualizations

Displaying pie charts within pie charts is a recipe for confusion, but aesthetics aren’t the only problem with this data visualization. The data isn’t explained very well either – what does it mean by active users – how often do they use the app? What does it mean by total users – does it refer to having the app downloaded but not using it frequently? The whole visualization needs a makeover, otherwise viewers will be left scratching their heads.

Example 2: Confusing 3D Data Visualization Example 

Source: 3D Bar Chart

One of the golden rules of data visualizations is to make sure they can be easily understood without being misinterpreted. Unfortunately, when it comes to certain data visualisation types, that’s easier said than done. 3D bar charts are notorious for being difficult to read at first glance. The example above is a particularly difficult one. It’s not a “bad” data visualization, as it does represent the data, but it’s definitely ugly, as you have to start at it for a while to work out what’s going on and how to compare the results with each other. 

Data Visualization Best Practices

Now that we’ve seen some data visualization real life examples of what to do and what not to do, let’s boil it down to a set of best practices.

1. Clarity is Key

The purpose of a data visualization is to tell a data story. Most people don’t want to hear a complicated story that’s difficult to understand. Ideally, the viewer should be able to grasp the message behind the data at first glance. 

  • Choose simple charts: You don’t need to use a highly innovative chart. Bar charts, line graphs...the classics are powerful for a reason! Emphasize the key points using color, size, and strategic annotations and headings.
  • Tell a story: Even a simple chart benefits from a clear title and context. To make things even more engaging, use interactive features such as animation.

2. Choosing the Right Chart Type

Whether you’re creating a static data visual visualization or a series of animated charts, you need to select the right ones. Here are some of the main reasons for displaying data and the types of chart that match:

  • Trends over time: Line graphs rule here. Area charts and candlestick charts can also be used.
  • Comparisons: Bar charts excel at side-by-side comparisons. Column charts are a vertical alternative.
  • Relationships: Scatter plots let you explore correlations. Bubble charts can also be an engaging way to present data, especially when animated. Heatmaps are also good for analyzing data density between two or more variables.
  • Proportions: Pie charts can help people compare parts of a whole. Donut charts and treemaps can also represent hierarchical data well.

3. Accuracy Matters

Misleading viewers is even worse than being unclear. If, for whatever reason, viewers feel like they can’t trust your data, then you quickly lose all credibility.

  • Double-check source data: Is it reliable and complete?
  • Aim for transparency: Include labels and axis definitions for full context. Never, ever cut off the bottom of bar charts!
  • Cast a critical eye: Does the visualization feel right, or does it leave you questioning the data? Better yet, ask others for feedback before going live with it.

4. Relevance and Focus

Your audience matters. You might not show the same data visualization to a group of data scientists as you would to the general public, for instance. Make sure you spend some time working out what the average viewer’s knowledge level is and what you want them to take away from the data visualization.

  • Define your goals: Are you exploring trends? Highlighting comparisons? Nailing this down will make your visualization choices much easier.
  • Add titles and annotations: Improve clarity with a few well-chosen words to explain things in a way that’s appropriate for the audience.

5. Design Principles

Don’t let a pretty design overpower the message you’re trying to share. Good design enhances clarity rather than just decorating the chart. Color, font, and space all affect how easily and well the data story comes across. See the sections above for data visualization design examples.

  • Color choices: Highlight key data points with contrasting colors. Use color association strategically (red for warnings, green for growth, etc.). 
  • Font Power: Clear, readable fonts are a must. Use varying text size and weight to guide the eye to important elements.
  • Layout Matters: Don't be afraid of white space – it allows your chart to breathe. If it’s too overcrowded, people will get confused. Place elements logically to create a natural flow for the viewer.

6. Interactivity and Dynamism 

Interactive data visualizations bring an extra dimension and allow you to tell a more complex data story, while still keeping the flow simple. Interactive features include clicking to filter, zoom, or reveal deeper layers of data. 

Dynamic data visualizations are animated to tell a more detailed data story. The example below is an animated data visualization made using the Vizzu tool to reveal some interesting charts about Oscar winners over the past 30 years.

7. Simplicity vs. Complexity

Impactful data science visualization examples are ones that hit the right spot with viewers, without being overwhelming or oversimplifying the data story. 

  • Focus on the core message: Don't try to say too much or everything at once. This is where good data storytelling comes in (see next point). Plan a simple, yet engaging story and use multiple visualizations to tell the full story if needed.
  • Clear visual flow: Use size, color, and placement to emphasize the most important points.

8. Storytelling with Data

Last, but definitely not least is data storytelling. As with most things in life, data really comes to life when it connects to some kind of story that people can relate to. Below is a basic outline for an engaging data story. You can find more in our article about storytelling in business.

  • Hook: Set the scene clearly. Ideally, you want to spark some curiosity to engage viewers. The hook can be a question, statement, or a chart that reveals something interesting or thought-provoking.
  • Data Visualizations: Select some cool charts to reveal your story bit-by-bit. An animated data visualization is perfect for telling a flowing story in a way that hits home.
  • The “A-Ha!” Moment: This is where the key message of your data story is revealed. A chart reveals a key piece of data or correlation that forms the “moral” of your story.
  • Resolution: Leave your audience thinking about what action can be taken or what possibilities could your findings open up.

Inspired? Create your own animated data visualizations!

By now, you should have a good idea of what makes a good or bad data visualization. Simplicity, accuracy, and clarity are key, but above all – telling an engaging data story is what will make it stand out.

A dynamic approach can help you transform raw data into clear insights that stick in your viewer’s mind.

But how do you bring data stories to life without a team of designers, data scientists, and coders?  

That's where Vizzu comes in.  

Vizzu has an easy-to-use interface to help you build stunning interactive charts from scratch.  Simply drag-and-drop the data and Vizzu automatically adds smooth animated transitions that guide the viewer through your story, step by step.

Sign up here to be one of the first to try out Vizzu when it launches soon!