The page provides an overview of data visualization with best practices and recommended resources for visualizing your data better.
Data visualization is a process of presenting data with visual elements such as charts, graphics, plots, infographics, and maps. It is a powerful technique/tool during Exploratory Data Analysis, a process to understand, investigate and summarize data. By visualizing data we can identify patterns, trends, relationships, anomalies, and potential problems in data. It is also widely used to communicate and share findings or insights of the data with others, which we often refer to as explanatory visualization.
Data visualization is an important tool for researchers when collecting and analyzing data at all stages of the research data lifecycle. It helps researchers better understand their data, quickly identify patterns and relationships in the early stages, and effectively present and communicate research data and findings more clearly and concisely as well.
Below are some best practices to consider when creating effective and engaging visualizations:
Before creating a visualization, it is important to understand your audience: who they are, what they need, their level of expertise, and the main message you want to communicate. It helps define the purpose, the data to be used, and the level of detail and complexity in visualization.
There are many types of charts and graphs but each type has its own advantages and limitations. You should pick up the one that represents your data and conveys your message best. Sometimes you need to consider trade-offs between accuracy, simplicity, and clarity. There is no one-size-fits-all in visualization.
The visualization should focus on essential data elements and critical points to avoid any distractions. A simple and clear visualization will help your audience understand the information quickly.
Color is an important element of visualization. It can be used to highlight the most critical or interesting data points or to group related data. However, using too many colors can be distracting and confusing. Don't use more than 6 colors. Choosing colors that are accessible and inclusive for all readers.
Titles, axis labels, legends, and annotations are an essential part of the visualization as well. Those elements help the audience understand the context and the data quickly. But they should be found easily and aligned to visuals properly.
Share the data visualization and collect feedback with your target audience on its effectiveness if possible. Adjust and revise to make it accurate and easier to understand.