5 Tips for Successful Data Visualization
I’m pretty sure we all could use some help in navigating the new art of data visualization. Luckily, I’ve found a post by FlowingData that can help us out. In it, they outline five misconceptions of data visualization that we should all be cognizant of.
Visualization is for making data look cool
Arguably the most common misconception of data visualization. It’s real easy to look at the best visualization projects, infographics, and presentations and want your data to look and feel the same way. While creating a “cool” look and feel is important, you can spend a lot of time with fancy icons or print without making the graphic entertaining. The key to an exciting, entertaining graphic is the data. When thinking about data visualization, it should always be about the data first. Remember that certain graphics will get attention because they show something that wouldn’t be seen in a table.
There is no all-in-one software for visualization
When it comes to visualization there are tons of tools out there and depending on whom you ask, the “best” tool will most certainly vary. Some software is good for analysis, some for storytelling, some for graphics, regardless there is no one piece of software that will do everything for you.
The more information in a graphic, the better
While it’s important to have information in a graphic, it’s really a story about quality not quantity. It’s really easy to load up graphics with all sorts of great data, but before you know it your audience is completely lost and confused. Some people try to be clever and use multiple axes or multiple visual cues in a single chart to save space. This tactic may work sometimes, but for the majority of the time it doesn’t. Oftentimes, simpler is better. FlowingData presents a great idea to test if your graphic is too “busy” by showing it to someone who doesn’t know the data and is not a visualization expert to see what they take away from the visual.
Visualization is too biased to be useful
Granted there is a certain amount of subjectivity that goes into making a graphic. By focusing on one part of the data, you might inadvertently obscure another. However, if you are careful and really get to know the data, it should be easy to overcome any bias.
Precision is key
If your goal is to show the exact value of every single data point, your visualization will probably not come out as you hoped. Yes, accuracy is important, but visualization is less about the individual values and more about the distribution of them over time and space. You’re trying to show patterns, or comparing and contrasting. If you care about expressing individual data points, tables well be better suited for your needs.
Do you know of any common misconceptions of data visualization?