Visual.ly

We've stated earlier that all this "defining our terms" business can get a little tricky. "Infographics," "data
visualization," "information design" and the like can all overlap, and trying to decide what goes where
can be complicated and maybe even unnecessary. Still, let's give it a shot.

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 to them -- maps, signs, charts -- using visual shorthand to present
complex quickly and clearly.

Data visualization is important because data on its own can be boring and hard to understand. Imagine
reading row after row of little numbers 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 data visualizations, 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:

• Data is accurate and verifiable. Never "lie with statistics."
• Visualizations are sourced and attributed. Visual.ly always gives credit where due and does its own reporting.
• 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.
Read on.

History of Data Visualization