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9 Rules Of Data Visualization

Visualization is critical today, as people need to catch up in the abundance of surrounding information and spend too much time on its perception.

Therefore, boring, incomprehensible texts should be noticed. The reader will need more time to understand them.

Visually presented information compared to plain text and tables:

  • attracts a much larger audience;
  • increases reader engagement;
  • quicker to absorb;
  • easier to remember.

There are many modern data visualization methods. In this article, we will talk about the most common and accessible of them – graphs and charts. Illiterate use of even the most straightforward graphs can spoil the impression of your work and you as an expert. To prevent this from happening, follow the basic rules of data visualization.

Rule 1. The Right Type Of Graphic

The primary purpose of visualization is to simplify and speed up the perception of information. The chosen format and chart type should facilitate this, not hinder it.

For example, if there are more than three to five values in a pie chart, the chart becomes unreadable. In such a case, choosing a regular line chart is better.

Rule 2. Logical Order

Please put the data in a logical order. Most often, this is sequentially from more excellent to lesser.

If you show the survey results on the chart, where there is a division into positive and negative answers, it is more logical to line them up in this order: “Yes, Most Likely Yes, No, Most Likely No, Difficult to Answer.”

Rule 3. Simple Design

Avoid useless design elements such as gradients, shadows, and 3D effects. They only distract the reader's attention from the essence of your message.

Your chart must be more beautiful and impressive because it is drawn three-dimensionally. This might have been surprising twenty years ago, at the dawn of Excel's heyday, when few people knew how to draw charts. Moreover, 3D charts can cause optical deception.

Rule 4. Easy Comparison Of Data

One of the primary purposes of visualization is to compare two or more metrics conveniently and clearly.

So, to make your charts valuable and useful, could you show the relationship between the data? The visualization becomes meaningless if you break down the same information into many separate charts.

Rule 5. Minimum Elements

Could you remove all uninformative elements from your charts and graphs and leave only the necessary ones? Cluttering with unnecessary information makes it difficult to perceive.

For example, if there are value captions, the grid lines and axis are unnecessary, as this is a duplication of information and is graphical “garbage.” If they are still needed, the primary and auxiliary grid lines should be simple and not conspicuous. The emphasis should always be on the main idea, not on additional elements.

Rule 6. Don't Overload With Information

Try to fit only some of the information you have into one diagram for the sake of making your chart seem clever and significant. The visual series should be manageable with complex and tiered charts.

When it is necessary to visualize many different data types and categories, dividing the chart into several parts is more appropriate. For example, if a line chart has more than four or five lines or a bar chart has more than two categories, you should not fit them into one graph.

Rule 7. Title And Captions

Could you ensure your diagram has a clear title and all necessary captions? Otherwise, there is a risk of misinterpretation.

The period and units of measurement must always be precise. Everything should be obvious, and the reader should be confident interpreting the data presented.

Rule 8. Common Color Schemes

There are a few basic categories that we always associate with a particular color:

  1. positive and negative values: green and red;
  2. yes/no, agree/disagree: green and red;
  3. men and women: blue and pink;
  4. other/other/other/other/no answer/difficult to answer – gray.

If you show these categories on the diagrams in the expected color scheme, the user only needs to look at the legend; it is clear what color means what.

Rule 9. Minimum Chart Types

Use one type of chart for the same kind of data. It takes time for the reader to get used to each new class of chart and to understand what each line, circle, or bar means.


Pay attention to these simple but fundamental visualization rules. Take care of your readers. No one likes to feel silly looking at obscure or cluttered charts and graphs.

Frequently Asked Questions

Why is choosing the right chart type necessary?
The right chart type simplifies and speeds up data comprehension, making complex information easily understandable.

How does logical data sequencing help in data visualization?
It organizes information in a way that makes sense to the viewer, similar to a well-structured story, enhancing understanding.

Why should design elements like gradients and shadows be avoided?
These elements can distract from the main message and clarify the chart, reducing effectiveness.

What is the significance of effective data comparison in charts?
It allows for easy comparison of different data sets, making complex relationships and differences more understandable.

How does clutter affect data visualization?
Clutter can overwhelm the viewer, making it difficult to focus on the vital message of the data.

Why is avoiding information overload critical in a chart?
More information can be overwhelming, making it easier for the viewer to process and understand the key points.

What is the role of titles and captions in charts?
They provide context and clarity, guiding the viewer through the data and preventing misinterpretation.

How do color schemes impact data visualization?
Standard color associations help convey meaning quickly and universally, enhancing the viewer's understanding.

Why is consistency in chart types important?
Consistency helps the viewer understand and interpret the data more efficiently, avoiding confusion.

How can data visualization improve communication?
It transforms complex data into visual formats that are easier to understand and remember, enhancing communication effectiveness.

What makes a chart easy to read?
A clear layout, logical data arrangement, minimal design, and appropriate use of color make a chart easy to read.

How does data visualization aid in decision-making?
It presents data clearly and concisely, making it easier to identify trends, patterns, and insights for informed decisions.

Can data visualization be misleading?
If not done correctly, it can misrepresent data, leading to incorrect conclusions.

What is the impact of using too many chart types?
It can make it easier for the viewer to understand and compare data effectively.

How important is the accuracy of data in visualization?
It is essential, as inaccurate data can lead to incorrect interpretations and decisions.

What role does audience understanding play in data visualization?
Understanding the audience's knowledge level helps design appropriate and easily understandable charts.

How can one ensure data integrity in visualization?
By using reliable data sources and accurately representing the data without distortion.

What is the benefit of minimalistic design in charts?
It focuses the viewer's attention on the data rather than decorative elements.

How does the choice of colors affect data interpretation?
Colors can evoke emotional responses and carry specific meanings, influencing how data is perceived and understood.

What is the best way to present complex data sets?
Breaking them into more straightforward, separate charts or visualizations makes them more digestible and easier to understand.

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