22 Data Visualization Best Practices

Seeing data is way better than reading data.

Data visualization is more than just choosing a chart and filling it with data—it’s about communicating insights effectively and clearly. Done right, a chart can reveal key trends, make a compelling argument, and provide easy access to complex information. Done wrong, it can confuse, mislead, or overwhelm your audience. To ensure your data tells the right story, here are 22 essential tips for crafting effective survey data visualizations that inform, engage, and guide your viewers towards accurate insights.

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1. Choose the Right Chart/Scales (match chart to data) 📊

Great data? Wrong format? Won’t work. Choosing the right chart type and scales ensures that your data is represented accurately and is easy for viewers to understand. Matching the chart type to the nature of the data enhances clarity and prevents misinterpretation. For instance, use line charts to depict trends over time, bar charts for categorical comparisons, and scatter plots for relationships between variables. Ensuring that the scales are appropriate for the data helps avoid misleading viewers and enhances the accuracy of the visual representation.

2. Correlation ≠ Causation (no hasty comparisons) 🖼️

Hey, this assumption happens to the best of us. Correlation between data points does not necessarily imply causation. Avoid making hasty comparisons without sufficient evidence to establish a causal link. Highlight the relationships carefully to prevent misguiding viewers. Just because two metrics move in tandem does not mean that one causes the other. It’s important to provide context and, where possible, to use additional data or statistical analysis to substantiate any claims of causation. Misinterpreting correlation as causation can lead to incorrect conclusions and misguided decisions.

3. Simplify Visuals (keep clean and focused) ✂️

Things can get a little too crazy when you love data like we do. Simplifying visuals helps maintain a clean and focused presentation. Avoid overloading charts with unnecessary elements. A clear visual conveys your message effectively without overwhelming the audience. Remove redundant information, such as extra gridlines, background colors, or overly detailed legends. Focus on the most critical data points and use a minimalist approach to ensure that the key insights from your survey data visualization are not lost in visual clutter. A simplified visual helps viewers grasp the main message quickly and without confusion.

4. Use Consistent Colors (avoid random colors) 🎨

Colors matter? Uh, yeah they do! Consistent colors help viewers understand and relate the data. Avoid using random colors that may confuse the audience. A cohesive color scheme enhances readability and visual appeal. Use colors consistently across similar data series or categories, and ensure that colors have meaning, such as using the same color for the same category across different charts. This consistency helps in building familiarity and makes it easier for viewers to draw comparisons. Avoid bright or clashing colors that can strain the eyes and distract from the main message.

5. Highlight Key Insights (direct attention efficiently) 🔍

“Cut to the chase” is what every stakeholder is thinking when you show them a ton of data. Highlighting key insights helps direct attention to the most important aspects of the data. Use techniques like bolding, coloring, or annotations to make significant information stand out clearly. For instance, use a distinct color to highlight a trend or a key data point that you want viewers to focus on. Adding arrows, text boxes, or other visual cues can also draw attention to critical areas in your survey data visualization, ensuring that viewers quickly understand the main message without having to search for it themselves.

6. Label Clearly (use good titles and axis labels) 🏷️

Clear labeling is essential for a comprehensive understanding of the data. Good titles and axis labels guide the viewers and provide context, ensuring that the visual can be interpreted accurately. Titles should be descriptive, while axis labels should clearly indicate the units of measurement and categories. Avoid abbreviations or jargon that might be unclear to the audience. Well-labeled charts reduce ambiguity, helping viewers make informed decisions based on the data presented.

7. Limit Data Points (don’t overwhelm viewers) 🚫

Limiting the number of data points helps prevent overwhelming viewers. Too many points can make charts cluttered and hard to follow, so it’s best to show only the most relevant data. Consider aggregating data or using representative samples to simplify the visualization. When there are too many data points, viewers may struggle to understand the chart, leading to confusion. By focusing on key data points, you make it easier for viewers to identify trends and draw conclusions without getting lost in unnecessary details.

8. Use White Space (let visuals breathe) 🦼️

White space gives visuals room to breathe, making them easier to digest. Crowded visuals can feel overwhelming, while appropriate use of space helps emphasize key parts of the chart. White space helps separate different elements, making the visual cleaner and easier to navigate. It also allows viewers to focus on the most important information without distraction. Proper use of white space can enhance the overall aesthetic of the visual, contributing to a more professional and polished survey data visualization.

9. Tell Data Stories (narrate stories, but with data) 📚

Telling a data story provides context and meaning behind the numbers. Use visuals to narrate stories, ensuring the data is presented in an engaging way that resonates with the audience. Craft a narrative that explains the data’s significance, highlighting trends, outliers, or changes over time. A good data story answers the “why” behind the data, giving viewers insight into the implications. By structuring your visual as part of a larger narrative, you help the audience understand the data’s relevance and keep them engaged.

10. Be Accurate (measure twice, always) 📏

Accuracy is critical in data representation. Double-check measurements and calculations to ensure the data presented is correct. Errors can lead to misguided decisions. Inaccurate data can damage credibility and lead to mistrust among viewers. Make sure to verify all data sources, units, and calculations before finalizing your visualization. Accuracy also extends to chart elements like scales, legends, and labels, all of which should correctly reflect the underlying data.

11. Avoid 3D Charts (distorted representation) ❌

3D charts often distort data representation, making it harder for viewers to understand true values and relationships. Stick with 2D charts for a more accurate depiction. The visual effects in 3D charts can make it difficult to accurately compare data points, as perspective and angles can create misleading interpretations. Simplifying to a 2D representation ensures that all data is easy to compare and understand, leading to a more reliable and truthful survey data visualization.

12. Use Trend Lines (visualize patterns over time) 📈

Trend lines help visualize patterns over time and can provide valuable insight into long-term data trends. They add an extra layer of interpretation to line charts. Trend lines can help viewers quickly understand whether data is increasing, decreasing, or remaining stable, and they are particularly useful in forecasting or predicting future values. Adding a trend line can also help highlight overall patterns that might not be immediately obvious from individual data points.

13. Group Similar Data (compare apples to apples) 🍏🍎

Grouping similar data ensures that comparisons are valid. Comparing data within similar categories provides a more meaningful analysis. For example, comparing sales data across similar product lines or customer demographics ensures that the insights drawn are relevant and accurate. Avoid comparing unrelated data sets, as this can lead to confusion and incorrect conclusions. Proper grouping allows for a focused analysis that draws meaningful insights from comparable data points.

14. Limit Pie Chart Slices (too many slices confuse) 🥧

Too many slices in a pie chart can be confusing. Stick to a few key segments to keep the visualization simple and comprehensible. When there are too many categories, consider aggregating smaller segments into an “Other” category. This helps maintain the clarity of the chart and ensures that the most important information stands out. Pie charts work best when used to show simple proportions, so limiting the number of slices makes the visual more effective and easier to interpret.

15. Use Heat Maps for Intensity (show concentrations) 🔥

Heat maps are useful for showing concentrations of data. They effectively highlight areas of intensity, making patterns and anomalies easy to spot. By using color gradients, heat maps provide a visual representation of data density, which can be particularly useful in identifying hotspots or trends across geographical regions or categories. The use of a color scale helps viewers quickly understand where data is most concentrated, providing valuable insight at a glance.

16. Include Data Labels (don’t make users guess) 💲

Including data labels helps viewers understand specific values without guessing. Clearly labeled data points improve accuracy and the viewer’s experience. Labels should be concise yet informative, indicating the exact values or categories being represented. This helps eliminate ambiguity and ensures that viewers can easily interpret the visual without needing to cross-reference other materials. Data labels add transparency and precision to the visualization, making it more trustworthy.

17. Visualize Distribution (box plots or histograms) 📦

Visualizing data distribution using tools like box plots or histograms helps reveal the spread and shape of the data. This provides valuable insight into data variability. Box plots can show median values, quartiles, and outliers, giving a complete picture of the data’s spread. Histograms, on the other hand, help identify frequency distributions and can highlight patterns such as skewness or uniformity. Both tools are valuable for understanding the underlying characteristics of your data.

18. Use Icons or Images (add context to data points) 🖼️

Icons or images can add context to data points and make the information more relatable. Adding visual elements supports storytelling and engagement. For example, using industry-specific icons can help convey data meaning more intuitively. Images can be used to represent categories or values, making the visual more engaging and easier to understand. Visual elements should enhance the message without distracting from the data itself.

19. Avoid Clutter (remove unnecessary gridlines) 🧹

Clutter distracts viewers from the main message. Removing unnecessary elements like extra gridlines or borders helps maintain a clean and effective chart. Visual clutter can make it difficult for viewers to focus on the data that matters. Simplify the chart by only including essential elements that contribute to understanding the data. By removing non-essential details, you create a more focused and impactful visual that communicates the key insights effectively.

20. Match Fonts to Tone (professional, readable fonts) 🔠

Using fonts that match the tone of the content keeps visuals professional and readable. Consistent and appropriate fonts contribute to an overall polished presentation. For instance, use sans-serif fonts for a clean and modern look, or serif fonts for a more formal and traditional feel. The size and style of fonts should be chosen to enhance readability, especially for titles, labels, and annotations. Avoid overly decorative fonts that can detract from the data’s clarity.

21. Break Down Data (use multiple simple charts) 📚

Breaking down complex data into multiple simple charts makes the information easier to digest. Presenting data in smaller, manageable visuals helps maintain viewer engagement. For example, instead of combining multiple data sets into a single, cluttered chart, split them into separate visuals that each tell a part of the story. This makes it easier for viewers to follow along and understand the key messages without feeling overwhelmed.

22. Consider Accessibility (clearly readable palettes) 👁️

Accessibility is important in data visualization. Use color palettes that are clearly readable, even for those with visual impairments, to ensure inclusivity and usability. Choose color combinations that have sufficient contrast and avoid using color as the sole means of conveying information. Consider using textures or patterns in addition to colors to differentiate data points. Accessible visuals ensure that all viewers, including those with color vision deficiencies, can interpret the data accurately.

That’s it! I hope you find these 22 tips valuable.

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