So, what is data visualization? Around here, we use the term “data visualization” as the overarching word for both the visual representation of data and the study of the presentation of data in a visual way. To us, infographics are a subset: data visualizations with a flow and story to them — maps, signs, charts — using visual shorthand to present complex ideas quickly and clearly.
Data visualization is important because data on its own can be hard to understand. Imagine reading row after row of little numbers in reports and you’ll soon get the idea. Data visualization helps present that information in a way that’s engaging and helps communicate complex ideas quicker. This is especially true on the web, where data visualization can grab a web surfer’s eyes and get information across much easier than paragraphs and paragraphs of words.
Plus, the world is generating more and more data. Data visualization can help with the analysis of that information and present it in a way that allows viewers to discover patterns that might otherwise be hard to uncover. Large amounts of data are hard to wade through, but data visualization can make that data easily digestible. In fact, it’s worth taking a moment to think about data analysis and how it relates to data visualization.
Data analysis is all about studying and summarizing data with the intent to extract useful information and come to conclusions based on that data. The idea is to draw inferences and confirm hypotheses. Data visualization can make it easier to convey that information and, in the case of, say, interactive infographics, even make it easy for the viewer to reach the same conclusions you have by interacting with the data.
And since we’re talking about data analysis, it’s probably a good idea to mention the code of ethics created by the Society of Professional Journalists, and our own principles about data analysis and data visualizations. Specifically, we believe it’s important that:
1. Data is accurate and verifiable. Never “lie with statistics.”
2. Visualizations are sourced and attributed. Visual.ly always gives credit where due and does its own reporting.
3. Visualizations follow best practices. Never exploit idiosyncrasies of the human visual system to exaggerate or misrepresent data.
You can read more about our code of ethics. You can also learn about the history of data visualization and the different types of data visualization tools.
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Data Visualization Diagram
