Good and bad data visualization examples

Data visualization is a powerful tool that helps us make sense of complex information by presenting it in a visually engaging format. And you often come across data visualizations in everyday life, when seeing informational infographics in the dentist’s office (with datasets) or charts and graphs that often populate government websites. When done well, data visualization can improve understanding and communication. However, when executed poorly, it can lead to confusion and misinterpretation. So if you’re looking to create a data visualization, you want to make sure it’s accurate, visually appealing, and engaging. The best way to understand what makes a good data visualization to create one is by looking at good and bad data visualization examples. That’s why in this article, we’ll explore both ends of the spectrum, highlighting what makes good and bad data visualizations and showcasing some examples.

Click to jump ahead:

1. What makes a good data visualization?
2. What makes a bad data visualization?
3. Good data visualization examples
4. Creating good data visualization examples with Infogram
5. Last thoughts on good and bad data visualizations 

What makes a good data visualization?

We are often exposed to different data visualization in our everyday life and we might think to ourselves “That’s a great visualization!” or “This is impressive”. However, we need to ask ourselves, what is it that actually makes these data visualization examples good? To answer this question, we compiled a list of characteristics that a good data visualization example possesses.

Clarity and simplicity

A good data visualization excels in clarity, presenting complex information in a straightforward and easily understandable manner. It avoids unnecessary embellishments or distractions that might compromise the audience’s understanding. For instance, a well-designed line chart with clear labels and minimal design elements can effectively communicate the relationship between variables without overwhelming the viewer with unnecessary details.

Relevance

Effective visualizations prioritize relevance, emphasizing the key message while removing any unnecessary information. A good data visualization example is a pie chart that concisely and accurately represents the percentage distribution of different categories, allowing viewers to grasp the essential information at a glance. By eliminating unnecessary data, the visualization becomes more focused and impactful.

Accuracy and precision

Accurate representation of data is paramount in good visualizations. They use precise scales, labels, and legends to ensure that viewers interpret the data correctly. Consider a bar chart that incorporates properly labeled axes and data points, providing a clear and accurate reflection of the values they represent. Precision in data presentation enhances the reliability of the visualization.

Interactivity

When suitable, interactivity can elevate data visualizations by fostering engagement and enabling users to explore information on their terms. Well-designed dashboards or interactive charts offer users the flexibility to delve into specific data points or trends. For example, a map with interactive features allows users to hover over regions and access detailed information, enhancing their overall understanding of the data.

Consistency

Consistency in the use of colors, shapes, and labels is essential for facilitating intuitive comprehension of information without causing confusion. A series of charts in a report that maintains a consistent color scheme for related data points ensures that users can easily connect and interpret the visual elements. Consistency enhances the overall coherence of the visualization, helping users to extract meaningful insights without unnecessary cognitive load.

What makes a bad data visualization?

Misleading scales

Misleading scales in data visualizations can create a distorted perception of the data, leading to inaccurate conclusions. Therefore, it’s crucial to choose appropriate scales that accurately represent the magnitude of values. For instance, a bar chart with a truncated y-axis might exaggerate the differences between values, giving a false impression of the significance of certain data points.

Unnecessary complexity

Data visualizations should aim for simplicity and clarity to facilitate understanding. Overly complex visualizations can overwhelm viewers and make it difficult to extract meaningful insights. The added complexity may distract from the core message and compromise effective communication of the data.

Lack of context

Providing context is key for viewers to properly interpret data visualizations. Failing to include labels, axes, or other important information can leave viewers in the dark about what the data represents. For instance, a line chart without clear labels or axes might confuse viewers and compromise the overall understanding of the presented information.

Inappropriate chart types

Selecting the right type of chart for the data at hand is critical for accurate interpretation. Choosing an inappropriate chart type, such as using a pie chart for time-series data instead of a line chart, can lead to confusion and make it challenging to understand and differentiate trends. That’s one of the reasons why it’s so important to match the visualization format to the nature of the data to convey information effectively.

Poor color choices

Improper use of colors in data visualizations can impact accessibility and readability. Poor contrast or the excessive use of vibrant hues can make it difficult for viewers, especially those with color vision deficiencies, to interpret the information. For example, a bar chart with too many contrasting colors may be hard to read and understand. Choosing a color palette that ensures clarity and accessibility is essential for effective data communication.

Ultimately, the effectiveness of data visualization weighs on its ability to convey information clearly and accurately. By following good data visualization examples and principles of simplicity, relevance, accuracy, interactivity, and consistency, you can create visualizations that improve understanding. Conversely, avoiding misleading scales, unnecessary complexity, lack of context, inappropriate chart types, and poor color choices help prevent confusion and misinterpretation in your data visualizations. As data continues to play a crucial role in decision-making, mastering the art of good data visualization becomes increasingly important.

Good data visualization examples

We’ve explored what makes good and bad data visualizations, and how each of these examples looks. Now it’s time to delve deeper and explore real-life good data visualization examples and understand what are the commonalities between them. 

Venture Capital Journal interactive guide 

The Venture Capital Journal guide by PEI Group offers a stunning combination of interactive data visualizations and visually appealing design. This guide presents an engaging and user-friendly experience, allowing viewers to navigate with ease. Once you open a new page, the animated visualizations captivate and engage. Moreover, it’s easy to navigate and analyze the charts by hovering over data points, which provide a clear and comprehensible view of the information.

PropertyGuru report

If you’re looking for a beautifully crafted report example, The PropertyGuru Singapore Property Market Outlook 2024 report stands out as a good data visualization example that encompasses valuable insights along with visually appealing visualizations. It’s a prime example of a consistent design and thought-out layout that highlights important figures and information. The report excels in maintaining visual cohesion and delivering a user-friendly experience.   

The Cato Institute: Unbanked Households chart

Here’s a good data visualization example by the Cato Institute that features a comprehensive overview of the reasons for not having a bank account among unbanked households in 2021. This grouped bar chart is part of an article that features more visually stunning visualizations worth checking out. Not only does this chart provide a clear view of the different data points but scores in visual appeal and interactivity. By hovering over each bar or clicking on labels on top, users can interact with the chart, making the viewing experience more engaging.

Encyclopaedia Britannica: The Bee Diversity Facts interactive dashboard 

A good data visualization example that scores in dashboard design and interactive experience is the Bee Diversity Facts visualization by Encyclopaedia Britannica. The interactive features along with seamless navigation provide a user-friendly experience. Users can easily click on the elements, learn more about each bee, and simply find their way back to the starting page. Plus, the choice of animations and visual elements go seamlessly together with the topic and make this interactive dashboard stand out.

Czech News Center (CNC) race bar chart

The interactive race bar chart by CNC displays the growth of the 10 largest companies in the world by market capitalization. This captivating visualization scores in showcasing meaningful data in a highly engaging and visually appealing way. By employing company-specific icons alongside strategically positioned labels, navigating the race bar chart becomes effortless. If you’re looking for good data visualization examples that attract viewers’ attention, this is it.

B Lab Global report

B Lab Global‘s annual report excels in design consistency, featuring a cohesive theme that showcases its professionalism. The report engages readers through visually appealing data visualizations, including interactive charts, which simplify complex information. Ultimately, the effective use of diverse chart types makes it easy for viewers to grasp a lot of information in an easily digestible manner. 

World Blood Donor Day infographic

If you’re looking for good data visualization examples that score in design consistency and visual appeal, take a look at the World Blood Donor Day infographic. This infographic features different interactive charts and a map that grabs attention with its visually appealing design and color scheme that matches the topic. Plus, interactive features make discovering data a more engaging process. 

Mental health crisis in the US infographic

If you don’t want or need to use a lot of charts or maps, you can also visualize your data using other media elements. This informative infographic on the mental health crisis in the US conveys important information without overwhelming the space with charts. Simply by using images, icons, and various other design elements, you can also achieve the same visual style and appeal. 

In conclusion, you can see that a lot of these good data visualization examples score in design consistency, strategic placement of information, interactivity, and diversity of charts, graphs, and maps. Also, all of the data visualizations are easy to read and understand, making them highly effective and impactful.

For more inspiration, explore the best data visualization examples of 2023, and access the full data visualization examples library here.

Creating good data visualization examples with Infogram

We know – to create a good data visualization example you need a good data visualization tool. Ultimately, having the right tool not only impacts the quality but also the efficiency of your visualizations. That’s where Infogram comes in to empower you to create compelling and informative visualizations without the need for extensive design skills.

User-friendly interface

Infogram’s strength lies in its user-friendly interface, making it accessible to both beginners and experienced data enthusiasts. With a drag-and-drop functionality, you can easily upload your data sets and choose from a variety of templates to kickstart your visualization project.

Diverse visualization options

Infogram provides a wide range of visualization options, from traditional bar charts and line graphs to more dynamic visualizations like interactive maps and infographics. This diversity allows you to select the most suitable format for your data, achieving clarity and relevance in your visualization.

Customization features

Customization is key to creating impactful visualizations, and Infogram excels in providing a wide range of customization features. For example, you can adjust color schemes, fonts, and styles to match your branding or convey specific messages effectively. You can also modify table charts to the cell level and choose how you want your labels to appear within a chart. All of these features ensure consistency and help maintain a professional look across multiple visualizations.

Real-time data updates

For users dealing with dynamic data sets that change over time, Infogram offers the advantage of real-time data updates. This feature is particularly beneficial for dashboards or reports that require constant monitoring and analysis, allowing users to stay current with the latest information.

Collaboration capabilities

Collaboration is made seamless with Infogram’s sharing and editing features. Multiple team members can collaborate on a single project in real time, streamlining the feedback and revision process. This collaborative environment fosters efficient teamwork, especially in scenarios where diverse expertise contributes to the visualization’s success.

Responsive design

In today’s multi-device landscape, ensuring that visualizations are accessible across various screen sizes is crucial. Infogram’s responsive design capabilities automatically adapt visualizations to different devices, making them easily viewable on desktops, tablets, and smartphones.

Data security and integration

Infogram prioritizes data security, providing a secure environment for you to work with sensitive information. Additionally, Infogram offers integrations with popular data sources and tools, facilitating a smooth workflow for anyone who needs to import or export data between different applications.

Educational resources

To support you in mastering the art of data visualization, you can explore a range of educational resources. These include tutorials, webinars, and guides on best practices for creating effective visualizations. Such resources empower our users to continually enhance their skills and stay updated on the latest trends in data presentation.

Last thoughts on good and bad data visualizations 

Ultimately, what separates good data visualizations from bad ones are the following characteristics: clarity, relevance, accuracy, interactivity, and consistency. When examining good and bad data visualization examples, make sure to pay attention to these 5 characteristics and see how they make a difference in the way you understand and interact with data. Additionally, Infogram can help you in the journey towards creating impactful and informative data visualizations. With the intuitive interface, diverse visualization options, customization features, and collaborative capabilities Infogram is the go-to tool for individuals and teams looking to convey their data-driven insights with clarity and effectiveness. Sign up and try Infogram today!