Effective Data Visualization Tips For Improving Your Data Storytelling

Author(s): Sarang S

Originally published on Towards AI.

Data Visualization

Visualized data and information are collectively known as data visualization. The primary goal of data visualization is to enhance the understandability of large and complex data patterns. Charts, graphs, and other graphical elements are used to make data visualization effective and attractive. Although this article covers some basic best practices for improving storytelling abilities, it will help you clear your basics.

Creating data visualization is an art and it needs to be mastered over time. Below are some data visualization techniques and tips given which will help you in improving your data storytelling so that they can move you in the right direction. If you want to create an effective and successful visualization, then it is necessary to know the main perspective of the viewer, then only you can succeed in your motive. So always try to put your best efforts into knowing the main perspective of the audience.

1. Data visualization must be audience-specific, along with clear requirements.

This is considered the first tip in data visualization. It is mandatory to get information about the needs of charts and the audience while designing data visualization. If you want to take your data visualization from zero to hero then having clear information about requirements helps a lot. Through this, you not only Design a visualization along with a strategic purpose that provides a solution for the particular problem but also the audience can easily understand it.

Let’s take an example: suppose your audience does not belong to a science environment then you do not need to design and fill the visualization with scientific terms. Also, if you bombard your chart with lots of trends, then it may increase the chances of the division of the viewer’s attention and also fails the purpose of creating a visualization.

• Know the visualization needs. This makes sure that the message represented by the charts must be clear and crystal. It ensures that your chart should not have any unnecessary information so that the audience must not be confused. The chart must not be overloaded. Therefore, you need to know about the requirements of the charts. And highlights only specific points in the charts and keeps it simple.
• You must need to know about the audience. You have to think about what the audience wants to see, before creating a visualization. Need to know about the preference and requirements of your audience. Must know about their background. If they have sufficient time to know about the visualization in detail or not.
• Whether they know the context of visualization or not. They are looking for what type of additional information if they know about the graph that is used in the creation of visualization. And so much more information regarding the needs of the audience. All this information will guide you in designing compelling data visualization.
• The visualization, which is filled with lots of information, makes it difficult for the audience to understand the main purpose of the visualization. That type of visualization will not be able to represent its main purpose to the audience.

2. Always select the right Data Visualization Technique for your Data

This is considered one of the utmost important tips among all data visualization tips. There are several visualization graphs. But it is important to select the best one to effectively highlight the main purpose of the data. The message will be represented easily and in an attractive manner only if the right graph is selected for the creation of visualization. It is important to know where to use which graph as each graph has its specific purpose.

• One of the most popular types of data visualization is bar graphs. They can represent a large amount of information at a glance. For comparing some values within the same category, then it proves to be the best choice. Suppose we want to compare the sales of two products in the whole year, then the bar graph is the right choice.
• If the data is represented in numerical format for continuous time intervals, then in this condition, line plots are very useful. These are very useful in comparing various values and are also able to effectively capture patterns and trends in data. Let’s take an example, there is a visualization of data for representing the monthly income of the company for the last few months, then a line graph is the best choice for the visualization.
• If you want to represent the relationship between two different variables, then scatter plots are the right choice. Scatter plots make it easy to spot the correlation between outliers or variables. Let’s take an example if we want to show how the house prices vary with the size of the living room, and then scatter plots are useful in this situation.
• For representing the proportional distribution of items in the same category, pie charts seem to be very useful. These types of charts must be used prudently. Otherwise, they can also be proven as harmful in place of good. Suppose we want to represent the percentage of the number of Android users to the number of iOS users in a country. Then the pie chart is the best choice.
• It is also possible to join two types of graphs for creating data visualization. This will result in a detailed representation of data to viewers.

3. Always Keep Visualization Simple

It is very simple and easy to add a large amount of information to a visualization. But it becomes difficult to get rid of the information, i.e., unnecessary ones. Messages can be easily conveyed to the viewer with the help of minimalist visualization. And minimalist visualization is those that do not have unnecessary patterns and distractions.

4. Also, use labels for your data visualization

Labeling of data visualization is considered an important technique for data visualization. What visuals want to say is easily conveyed by the labels. Maybe the labeling is missing during the data visualization, so always ensure to check the data visualization labeling again.

Following are some of the important points that can be considered while choosing labels for data visualization.

• Legible labels should be used. Labels do not have any use if it is not clear. Therefore always make sure to use labels that are easy to read.
• Provide the graph title. When a suitable title is given to the graph, then the viewers easily get the immediate essence of what the graphs want to represent.
• Legends are used so it becomes easy to find the difference between the different lines that are used in graphs. But also tries to label directions in the case of line charts. By this line, identification becomes easier for the viewers.
• Provides labels for the axes. Sometimes labeling of the axes is required as sometimes the title does not provide clear information about what the axes represent.

5. Understand the Text Importance in Charts

Data visualization does not only revolve around numbers. Important context is provided by the text of the graph and by which the right message is conveyed to the viewer.

Information presented in the visualization is explained by the annotations, headings, and subheadings used alongside the graph. But data Visualization can also be backfired if the same message content is repeated multiple times or unnecessary message content is used.

• Try to use simple phrases in the text of the graph. Allowing visualization to speak for itself is also considered one of the objectives of data visualization.
• It’s better to hold only those annotations that give sufficient information. There is no use in adding annotations for each data point as it can also distract the viewers and also lead to clutter the actual visual.
• For highlighting important sections of the graph, you can use bold or italic text. But keep in mind one thing that doesn’t highlight the text unnecessarily, as after extra highlighting, there is no major difference between emphasized and regular text.
• Don’t reiterate the same message. Let’s take an example, it’s not good if the heading and subheading display the same message.
• It becomes difficult to read the data if you are using distracting fonts. The data visualization must be created in such a manner that the graph must represent the actual message to the viewers, and the viewer can understand it with less effort.

6. Use Colours Effectively in Data Visualizations

We all are aware of the impact of using color in the text. One of the most important data visualization tricks is using colors in the creation of data visualization. It can add the right amount of zest in data visualization that is required to attract viewers. But if the colors are not used properly, then sometimes it may lead to the viewer misleading.

• For the same type of data, always use the same color. Suppose a bar graph representing data on the sales of cars and bikes in the whole year. So, in this case, use one color to represent the sale of a car and another color to represent the sale of a bike.
• It is important to keep the same color for text annotation with the line or bar that represents it. So, viewers can easily differentiate what data is displayed by the text.
• For representing the waning intensity of data, you can also use different shades of the same color. For an indication of patterns, Choropleth maps can be used.
• Try to use the color within the limit, as the use of too many colors will result in creating a cacophony in your visualization.
• Try to use colors with which the viewer can relate. Let’s take an example, for representing hot temperature, use red color, and for cool temperature, use blue color. As a result, users can understand it easily even if a detailed explanation is not given in the visualization.

7. Avoid Deceiving with your Visualizations

It does not matter how good a data analyst or data scientist you are; sometimes, it may be possible that we may deceive the viewers when we are trying our best to make a visualization stunning. And also, sometimes there may be a condition where we also don’t know that we are deceiving the viewers.

• In your graph, use baselines. The most common deception for viewers is not using the baselines in the graph.
• It is important to include all the data in the graph. The viewer may make a wrong decision when there is an incomplete representation of data in the graph. Suppose you are representing a part of the sale that has an upward trend only, but if you are taking it as a whole, you find out that it is a downward trend.
• Adding so much extra information to the graph is one of the smart ways of deceiving the viewer. And due to this, viewers cannot be able to focus on a particular trend which creates a lot of confusion.
• Always follow conventions. Suppose you are using green color for identifying something wrong, and for representing something right, you are using a red color which gives the wrong impact of data to the viewer.

8. Make interpretable Data Visualizations

Making the data visualization interpretable is the last and the most important tip for designing data visualization. It is more important than visual appeal. All the tips mentioned above will help in making the visualization interpretable. Colors, fonts, and visual images seem to be good if they are used in visualization, but if the data can be represented better using a simple line graph, then it is suggested to avoid using fancy logos or images in visualization.

While visualizing the data, always give priority to interpretability over beauty.

Conclusion

• Visual data and information is known as data visualization.
• The main aim of data visualization is to make the understandability of large and complex data patterns easier.
• Always try to put your best efforts into knowing the main perspective of the audience so that you can make your visualization effective and successful.
• The message will be represented easily and in an attractive manner in the data visualization only if the right graph is selected for the creation of visualization.
• Messages can be easily conveyed to the viewer with the help of minimalist visualization.
• Important context is provided by the text of the graph, and so always try to provide the best content in the graph.
• One of the most important data visualization tricks is using colors in the creation of data visualization.
• While visualizing the data, always give priority to interpretability over beauty.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI