Research conducted by the University of Arizona has shown that using visual aids increases the persuasiveness of content by 43%. Correctly visualizing data helps users get the underlying message at a glance. Convincingly and effectively displaying information plays an important part in data analytics. But presenting the data goes beyond picking the right chart.
In this article, you’ll learn some of the most common data visualization types. You’ll also learn some tips and best practices for making your visualizations as effective as possible.
What Is Data Visualization?
The term data visualization refers to representing data in a visual way, such as a chart or a diagram. Visualizing the data can help you quickly make sense of data in an intuitive way. Instead of scrolling down endless rows in a spreadsheet, you can understand the message in a graphic way.
Some of the benefits of using visualization techniques to represent data include:
- Faster decision-making—data visualization enables decision-makers to act faster by making information clear and vivid. Senior management can then quickly decide upon actionable operational and business information.
- Quickly understand patterns—users can easily detect patterns and connections between the data by interpreting it through heat maps and other graphics.
- Detect emerging trends—data visualization enables decision-makers to spot the hidden niches and shifts in consumer behavior, detecting trends.
- Geospatial Visualization—this type of visualization uses location-specific information, such as zip codes, enabling easier geographic analysis.
Data Visualization Types
Data visualization types can be separated into different categories. The most commonly used include:
Temporal data is data that changes over a time variable. This type of data is linear and one- dimensional. Examples of temporal visualizations can include scatter plots, timelines, polar area diagrams.
Example of a scatter plot: Image source
These data visualizations are characterized by organizing groups of data within larger groups. These graphs tend to be more complex to understand. You should use hierarchical visualizations for displaying clusters of information. Examples of hierarchical visualizations include tree and sunburst diagrams and tree-diagrams.
Example of tree diagram: Image source
Network visualizations present the relationships between datapoints in a clear, graphic way. Examples of network visualizations include word clouds, node-link diagrams, and matrix charts.
Example of a node-link chart: Image source
Multidimensional data visualizations can help you combine more than two variables to create 3D visualizations. These types of visualizations tend to be more eye-catching. They are more suited for desktop visualizations than for small screens due to the number of details. Examples of multidimensional data visualizations include pie charts, Venn diagrams, stacked bar graphs, and histograms.
Image source Example of stacked-bar chart
Data Visualization Tips and Best Practices
Visualizing data helps you convey complex concepts in a simple way. While you can experiment with different techniques and visualization types, there are some common best practices you should know.
Target your audience
You should present the graphic in a way that meets the user’s level of technical knowledge. What you present to an inexperienced audience should be bolder with a clear-as-day takeaway. When you present data to an experienced or scientific audience, the content can be more detailed and require interaction from the user.
First, clean the data
Before choosing a visualization chart, you should ensure your data is clean of duplicated records and errors. You can use open-source resources such as Open-Refine to help you refine your data more easily.
Choose the right visualization type
The type of visualization you choose can help users understand your message. The first step to choosing the right visualization type is assessing the type of data you are using. For instance, timelines are better to present data over time, while a pie chart is a better fit to present percentage-based distributions.
You should match the data to the appropriate visualization by keeping in mind the following factors:
- The relationship between data sets—what relationship you are trying to prove between the data points.
- The number of variables—if you want to analyze a single variable over time or compare multiple variables.
Make the visualization responsive
Visualizations are used in varied environments and with many devices. To account for this, you should make sure that your images and visualizations are responsive. Responsiveness means that the visualization can be adapted to multiple screen layouts. There are a variety of tools that can help you create responsive images, such as digital asset management solutions.
Prioritize data visually
You can use color contrast to guide the user through the visualization. A contrasting color palette can serve to prioritize the data, showcasing the most important parts. Data organization and the layout helps create a hierarchy between the different parts of the graphic.
When you are presenting your data, it is a good practice to include comparisons. Presenting two charts or diagrams together, you can give the user a clear picture of the message behind your data. Examples of comparisons can be strengths and weaknesses charts, or comparison between two time-periods.
Focus on the story
The data visualization should tell the story behind the data in a visual format. You should focus on making it easy for people to understand. Think like a writer, create your visual narrative building tension points and adding impact.
The Bottom Line
Data visualization is one of the most impactful ways to share the results of your analyses with others. Correctly implemented data visualizations are powerful tools that can help organizations make decisions and detect trends. Hopefully, this article helped you understand the basics of data visualization and provided some useful tips for improving your visualizations.
If you’d like to learn more about how data visualizations are being used, consider checking out the field of visual analytics. This field is working to improve the way visualizations are used with the application of machine learning.
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