Many people have experience exploring data visually and making charts that are clear and concise, but when they present results, they don’t take the extra step to communicate to a wide audience. Instead, they take screenshots or software output and put the raw graphs into a report or post them online.
This works under the assumption that your audience understands your data in the way that you do. Great if that’s the case, but what if it isn’t? People who don’t know the background behind a dataset, or have the same technical expertise as you won’t see the same thing as those who do.
When you design visualization for an audience, you must consider what your audience knows, what they don’t know, and what you want them to know. How will they read your graphic? How will they interpret your data?
Before getting into specifics, it’s best to clear up common misconceptions about designing data graphics for a wide audience. There are a lot of books and articles that provide suggestions as unyielding rules for various purposes, and these “rules” often conflict. This leads to a lot of confusion. So it’s time to clear the air and start fresh.
There are visualization types that have been around for decades. Think bar charts, pie charts, dot plots, and the other usual suspects. People are accustomed to reading data through these traditional forms, but some see this as a negative. How can something traditional catch ...