Designing for an Audience - Data Points: Visualization That Means Something - page 584

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?

COMMON MISCONCEPTIONS

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 topics
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.

NOVEL GRAPHICAL FORMS

There are visualization types that have been around for decades. Think bar
charts, pie charts, dot plots, and the other usual suspects. People are accus-
tomed to reading data through these traditional forms, but some see this as
a negative. How can something traditional catch readers' eyes and keep them
engaged? Some think you always have to use new and exciting graphical forms
to make visualization interesting, but this idea misses the point of visualizing
data (which is why I don't like visualization contests that weigh “innovation”
as heavily as insight).

Note: Experimentation with new visualization
methods is great, but you also want to make sure
that others can decode your encodings. Often,
traditional is the best route. Traditional methods
have been around for a while because they work.

Visualization can be appreciated purely from an aesthetic
point of view, but it's most interesting when it's about data
that's worth looking at. That's why you start with data,
explore it, and then show results rather than start with a
visual and try to squeeze a dataset into it. It's like trying to
use a hammer to bang in a bunch of screws.