Data Visualization Best Practices

Introduction  

Data visualization has become an increasingly valuable tool for understanding and communicating data in recent years. As the volume and complexity of data continue to grow, so does the need for effective data visualization.  

Businesses and professionals can communicate data in various ways, including through charts, graphs, maps, and infographics when using data visualizations. Effective data visualization usage can help make data more understandable and accessible to a wider audience. Several best practices can be followed when creating data visualizations. These include effectively choosing the right chart type for the data, using clear and concise labeling, and using colors and other visual cues.  

It is important to keep the audience in mind when creating data visualizations. The goal should be to create visualizations that are both informative and visually appealing. By following some of the best practices for data visualization, it is possible to create visualizations that can effectively communicate data to a wide audience.  

What Is Data Visualization?  

Data visualization is the graphical representation of data. It involves creating and studying the visual representation of data to see relationships between data, patterns, trends, and other information. Data visualization is used in a variety of fields, including business, science, medicine, and engineering. It can be used to communicate information clearly and concisely or to explore and discover new relationships in data.  

There are a variety of data visualization tools and techniques, including maps, graphs, and infographics. The best data visualization tool for a particular task will depend on the data being visualized, the purpose of the visualization, and the audience.   

Data Visualizations Examples  

• A bar graph showing the number of hours spent studying each week for a group of students 
• A line graph tracking changes in global temperatures over time 
• A pie chart illustrating the percentage of people who voted for each political party in an election  
• A scatter plot showing the relationship between the amount of money spent on advertising and the number of sales made by a company  

Data visualization is an important skill for anyone working with data. It can help you to see relationships and patterns that would be difficult to spot otherwise and to communicate your findings to others. If you are working with data, whether it be for business, science, or another field, learning how to visualize data can be a valuable skill effectively.  

When data is represented visually, it is easier to see trends over time. This can be useful for businesses, as they can use data visualization to track sales, website traffic, or any other important metric. There are many ways to visualize data; the best way to do it depends on the data itself and the audience. However, data visualization is a powerful tool that can be used to help people understand data, find trends, and make decisions.  

Best Practices for Data Visualization 

Data visualization is the process of representing data in a graphical or pictorial format. It is a powerful tool that allows us to see relationships, patterns, and trends in data that would be difficult to discern otherwise. There are a few things to keep in mind when creating visualizations:  

Identify Your Audience and Figure Out Their Needs  

Before you begin creating your data visualization, it is important to think about who your target audience is. This will help you to determine what kind of information to include and how to present it in a way that is most useful for your audience. For example, if you are creating a data visualization for a presentation to senior management, you will want to focus on high-level overviews and trends. However, if you are creating a data visualization for a more technical audience, you will want to include more detailed information and allow for more interactivity.   

It is also important to keep your target audience in mind when you are creating the data visualization. This includes making sure that the visualization is easy to understand and interpret. If your target audience is not familiar with data visualization, you may want to include a legend or explanatory text.   

Choose the Right Key  

There are a few factors to consider when choosing the right key for data visualization. The first is the type of data you are working with. If you are dealing with categorical data, then you will want to use a qualitative key. If you are dealing with quantitative data, then you will want to use a quantitative key. The second factor to consider is the level of detail you need. If you are working with a large dataset, then you should use a more detailed key. If you are working with a small dataset, then you should use a less detailed key. The third factor to consider is the purpose of the visualization. If you are trying to communicate a complex message, then you will want to use a more complex key. If you are trying to communicate a simple message, then you will want to use a simpler key.   

There are many diverse types of keys, and the best one to use depends on the data represented. Some common keys used in data visualization include:  

• Bar charts: Bar charts are a good choice for data that can be divided into categories. 
• Line charts: Line charts are a viable choice for data that shows a trend over time. 
• Pie charts: Pie charts are a desirable choice for data that can be divided into parts. 
• Scatter plots: Scatter plots are an excellent choice for data that has two variables.  

Apply Text Carefully  

Data visualization is a process of representing data in a graphical or pictorial format. It is an effective way to communicate complex information in a way that is easy to understand and interpret. When creating data visualizations, it is important to apply text carefully. This means using clear and concise language, choosing an appropriate font size and style, and ensuring that the text is legible. Data visualizations should also be easy to understand. This means using simple shapes, colors, and patterns and avoiding cluttered and busy designs.  

Utilize Predictable Patterns for Layouts  

There are many ways to lay out data visualizations, but some ways are more effective than others. One data visualization best practice is to utilize predictable patterns in your layouts. People are used to seeing data visualizations in certain ways. For example, most people are used to seeing bar charts laid out horizontally, with the x-axis representing the various data points and the y-axis representing the values of those data points. If you were to lay out a bar chart in a unique way, such as vertically or with the axes reversed, it would be much harder for people to interpret.   

Another best practice is to use colors consistently throughout your data visualizations. For example, if you are using a bar chart, you could use different colors to represent different data points. However, it is important to use the same colors for the same data points throughout all your visualizations. This will make it easier for people to compare different data sets.  

Finally, you should always label your data visualizations clearly. This means including titles, axes labels, and legends (if necessary). People should be able to look at your visualization and understand what it is showing without having to guess. By following these best practices, you can create data visualizations that are both effective and easy to understand.  

Use the Correct Tool  

The correct tool depends on the data set, the question being asked, and the audience. Different tools are better for several types of data and diverse types of questions. Some common tools for data visualization include histograms, bar charts, line graphs, and scatter plots. Many more tools are available, and the best tool for a particular situation can vary depending on the data set, the question being asked, and the audience.  

When choosing a data visualization tool, it is important to consider the type of data being visualized and the question that is being asked. For example, a histogram is a desirable choice for visualizing data distributed along a continuum, such as data representing the ages of people in a population. A bar chart is a better choice for data that can be divided into categories, such as the number of people in each age group.  

Utilize the Right Colors for Impactive Storytelling  

Different colors can evoke different emotions in people. When choosing colors for data visualization, it is important to consider what type of emotion you want to evoke in your audience. For example, if you want to create a calming visualization, you might use cool colors like blues and greens. If you want to create a more exciting visualization, you might use warm colors like reds and oranges. It is also important to consider the different meanings that colors can convey. For example, red is often associated with danger or warning, while green is often associated with health or growth. Yellow is often associated with happiness or optimism, while blue is often associated with sadness or calm.   

Keep the dashboard straightforward  

The best practice for data visualization is to keep the dashboard straightforward. This means that the dashboard should be easy to read and understand. It should not be cluttered with too much information.  

The dashboard should have the most essential information displayed prominently. It should also be easy to navigate. Users should be able to find the information they are looking for easily.  

Make Inclusive Visualization  

One key principle that should always be kept in mind is the need for inclusivity. This means creating visualizations that can be understood and used by as many people as possible, regardless of their level of expertise. One way to achieve this is to use clear and concise labeling that can be easily understood. Another is to use colors and other visual cues to help distinguish different data sets. When in doubt, it is always best to err on the side of simplicity. It is also important to keep in mind the purpose of the visualization. Is it meant to be explanatory or simply descriptive? The answer to this question will help guide the design choices.  

Consider Business Insights  

The best practice for data visualization is to consider business insights. This means that when creating visualizations, you should think about the story the data tells and how it can be used to improve business decision-making. When done correctly, data visualizations can be incredibly powerful tools for understanding complex data sets and uncovering new insights.   

There are a few things to remember when creating business insights visualizations. First, make sure the visualization is clear and easy to understand. The last thing you want is for your audience to be confused by the data. Second, focus on the most valuable information and do not try to include everything. You want your audience to be able to take away the key points from the visualization without being overwhelmed. Lastly, use colors and other visual elements to highlight the most important information. This will make it easier for your audience to see what is most important and understand the story the data is telling.  

Conclusion   

After reading this article on data visualization best practices, you should have a better understanding of how to communicate data effectively. When creating visualizations, it is important to consider the audience and what types of information they are looking for.  

There are many best practices regarding data visualization, but some of the most important ones to keep in mind are to use clear and concise labeling, use colors effectively, use appropriate chart types, and create easy-to-understand visualizations. By following these best practices, you can ensure that your data visualizations are effective and informative. Interested in learning in depth about various data visualization methods? Do check out  UNext’s PG Certificate Program in Data Science and Machine Learning program in collaboration with Manipal Institute of Technology.