Designing with a Purpose - Data Points: Visualization That Means Something - page 340

The bottom line: At its most basic level, visualization is turning data, which
can be numbers, text, categories, or any variety of things, into visual ele-
ments. Some visual cues work better than others, but applicability also
varies by dataset. A method that's completely wrong for one dataset could
fit perfectly for another. With practice, you can quickly decide what fits
your purpose best.
Good Visualization
Although people have been charting and graphing data for centuries, only
in the past few decades have researchers been studying what works and
what doesn't. In that respect, visualization is a relatively new field. There
still isn't a consensus on what visualization actually is. Is visualization
something that has been generated by a computer following a set of rules?
If a person has a hand in the design process, does that make it not a visu-
alization? Are information graphics visualization, or do they belong in their
own category?
Look online, and you can find lots of threads discussing differences and
similarities between information graphics and visualization or essays that
try to define what visualization is. It always leads to a never-ending back
and forth without resolution. These opposing opinions lead to varied crite-
ria for what makes a data graphic good or bad.
Statisticians and analysts, for example, generally think of visualization
as traditional statistical graphics that they can use in their analyses. If a
graphic or interactive doesn't help in analysis, then it's not useful. It's a
failure. On the other hand, if you talk to graphic designers about the same
graphic, they might think the work is a success because it displays the
data of interest fairly and presents the data in an engaging way.
What you need to do is smush them all together, or at least get them in the
same room together more often. The analytically minded can learn a lot
from designers about making data more relatable and understandable,
whereas design types can learn to dig deeper into data from their analytic
counterparts.