Data visualization is the art of displaying data that people can easily understand. This might be an individual, a team, or even an entire audience. It could be in person or online.
The point is that data needs to be accessible and understandable regardless of how many people view it and their technical know-how level.
This blog post will look at various data visualization techniques and how you can use them to make data communication more effective. You will also learn about the four levels of visualization, along with the key requirements for data visualization. Let’s dive in.
What Kind of Visualization Should I Use?
There are several different visualization techniques that you can use to represent data in different contexts. Depending on your data set, some of these might be more useful than others. Here are a few examples to get you thinking about different visualization types.
- Graphs and charts – are commonly used in business and data journalism. Graphs and charts can effectively convey quantitative data like stats, figures, and trends. However, they aren’t so effective at communicating qualitative data like opinions, feelings, or general observations.
- Diagrams and illustrations – these can be used to display a wide variety of data but are particularly useful for complex data sets like processes, systems, or goods and services. They’re also great for displaying qualitative data like opinions, feelings, or observations.
- Maps and infographics – maps and infographics are great for visualizing geographically relevant data. Examples include traffic patterns, population density, and sales data by region or state.
Also read: 9 Best Data Visualization Libraries In 2022
What Are the Three Most Essential Principles of Data Visualization?
Data visualization is the process of representing data in a visual format. When done well, it can help to communicate information clearly and efficiently. Every data visualization should adhere to three fundamental principles: standard, simple and scalable.
Simplicity is essential for ensuring that people can easily understand a visualization. Using standard shapes, colors and sizes also helps make visualization more legible.
Finally, scalability is vital for ensuring that people view the visualization at different sizes without losing its impact. Data visualizations can be compelling and informative by following these three principles.
What Are the Four Levels of Visualization?
Data visualization is divided into four levels: exploration, analysis, synthesis, and presentation. At the exploration level, data visualizations are used to examine data and get a general sense of its structure and content.
In addition, data can be explored interactively, allowing users to zoom in on interesting areas or filter out irrelevant information.
At the analysis level, you can use data visualizations to identify patterns and relationships in data. You can use data to aggregate and compare across different groups. You can also use statistical techniques to find trends and correlations.
You can use data visualizations at the synthesis level to combine multiple data sources into a single view. This can help spot gaps and inconsistencies and uncover new insights that would not be apparent from looking at the data separately.
At the presentation level, data visualizations are used to communicate findings to an audience. For example, you can use data visualizations to explain complex concepts clearly and concisely or to tell stories that engage and inform viewers.
What Are the Key Requirements for Good Visualization?
You first need to understand what makes a visualization “good” to create great visualizations. This will depend on your specific goals and the audience you’re trying to reach. Here are a few key elements you should always keep in mind when creating visualizations for data analysis.
- Simplicity – the primary goal of visualization is to make data accessible and understandable. Accessible and understandable means that you shouldn’t include more data in your visualization than is necessary. You should also avoid unnecessary visual elements, such as decorative graphics or colors that don’t add value.
- Consistency – as you create visuals, you also want to maintain consistency across all your visuals. Maintaining consistency means that all your graphs should look similar, and all your pie charts are identical.
- Accuracy – you need to be careful that your visualizations are accurate and preferably verified. You don’t want people to draw false conclusions based on the data you’ve visualized. This is why you should only include the needed data and only use visual elements that add value.
- Relevance – while simplicity, consistency, and accuracy are essential, you must also be careful that your visuals are relevant. Relevancy means that your visuals should primarily focus on answering the questions most important to your audience.
Also read: Data Discovery: What It Is, Uses and Tools
How Do You Visualize Data?
Data visualization is a process for making data easier to understand. The first step is to determine the decision you want to make. This will help you identify the most critical metrics for the decision.
For example, if you decide whether to invest in a company. You would look at revenue, profit, and growth metrics.
Once you have identified the key metrics, you can develop the story you want to tell. This story should be clear and concise and explain why the data is essential.
After you have developed the story, you can select the appropriate visual. There are many different types of visuals, so it is necessary to choose one that will best communicate your message. For example, a bar chart might be a good choice if you want to compare different companies.
Once you have selected the visual, you can add relevant elements such as labels and annotations. Finally, it is essential to review the visual to ensure it is clear and accurate. A non-expert review can be especially helpful in catching errors.
With so many data visualization options, choosing the correct technique for your needs can be challenging. To find the best method for representing your data, you should consider the type of data you have and its purpose.
The examples we’ve looked at in this post are a great starting point, but many other kinds of visuals could be helpful in your data. Once you’ve chosen the correct visualization technique, take some time to experiment and find what works best for you and your audience.