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Data visualization allows users to obtain actionable insights from vast amounts of data. It makes it easier to recognize patterns and spot areas that need attention. For visualization to be effective, it has to help users quickly comprehend the information presented and use it to address their specific needs. Here are eight data visualization tips to keep in mind:

1. Make it audience-specific.

Identify the requirements of the audience. What specific questions do they need answers to? Understanding their needs allows you to create a purpose for the visualization and build a visual that effectively conveys a clear, concise, and constructive message.

2. Pick the most efficient visualization method.

There are many ways to communicate data. The ideal method highlights crucial information and key trends, allowing your audience to grasp the message while keeping them engaged. Here are some general pointers:

• Bar graphs present vast amounts of information efficiently. They are beneficial for comparing values belonging to the same category, such as the profitability of two products over five years.
• Line plots or trend lines are excellent for displaying the progress or progression of a numerical value over time. For example, this data visualization method is suitable for depicting a company’s monthly income over the last several months.
• Scatter plots can illustrate connections between two variables, helping users quickly detect correlations among variables or outliers in the data. You can use it to show how the cost of property varies concerning the size of its living quarters, for example.
• Pie charts are ideal for depicting proportionate distributions of items within the same category. They should be utilized with caution, though, because they represent quantities in slices—and most users are not good at approximating quantity from angles.
• Histograms plot the distribution of numerical data over a continuum by segmenting it into bins. They are fantastic for displaying the dispersion of information. For example, seeing number of orders for a product over a certain number of years.

3. Keep it simple.

Too much information often distracts from the main message. Aim for a minimalist visualization devoid of unnecessary elements that can confuse your audience. Meaningless charts and incomplete or inaccurate data take attention away from what matters. Good data visualization involves removing this noise, telling a story, and highlighting essential information.

4. Use proper labels.

Labeling makes data visualizations easier to understand. Make sure that the labels are legible and that there is an appropriate legend. Do not forget to provide a title that clearly states the subject.

5. Use appropriate colors.

When used well, color can help you communicate your data more effectively. Use the same color for the same type of data and accent colors to emphasize vital information. There should be sufficient contrast because colors that are too similar make it hard to differentiate between the different elements.

6. Use text in moderation.

Text is just as crucial as numbers in data visualization. Add annotations, sub-headings, and headings to explain the topic you are presenting, but use them in moderation. Keep the phrases simple and avoid using hard-to-read and distracting fonts.

7. Make sure you are not deceiving the viewer.

Seemingly minor things like omitting the baseline, cherry-picking the data, and information overload can make your visualization deceiving. Get straight to the point by including baselines and ensuring that you are presenting complete data.

8. Ensure interpretability.

Prioritize interpretability over the aesthetic appeal. In the end, if a simple car chart or line graph can clearly deliver the message, you do not need fancy images in your data visualization.

About The author:

Sundeep is a technology enthusiast who works with tech entrepreneurs, founders, C-suite executives, cross-functional teams, and CoE leaders to distill and share key insights with the tech community. His body of work includes Data & Analytics, Data Storytelling, Blockchain, Big Data, Business Intelligence, and GeoMarketing.