Database Reference
In-Depth Information
Visualizations are especially valuable when it comes to extremely large data sets such
as Data Scientists confront daily. Even with enormous data volumes, the right graphic ren-
dering, or collection of graphic renderings, can help people spot patterns more easily and
quickly than via any other method - grasping facts which would not otherwise be obvious.
But it is very easy to over-reach when doing graphic visualizations of data - to unwit-
tingly go for art instead of information. This should, of course, be guarded against.
According to Dr. Jerome Friedman, professor of Statistics at Stamford, the “main goal
of data visualization is to communicate information clearly and effectively through graph-
ical means. It doesn't mean that data visualization needs to look boring to be functional or
extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form
and functionality need to go hand in hand, providing insights into a rather sparse and com-
plex data set by communicating its key-aspects in a more intuitive way. Yet designers often
fail to achieve a balance between form and function, creating gorgeous data visualizations
which fail to serve their main purpose - to communicate information.”
We've all heard the phrase “simple is better.” We've all encountered the famous quote
from Henry David Thoreau: “Simplify, simplify.” A large part of the job of a well-con-
structed visualization is to remove as much unnecessary complexity as possible while still
remaining true, accurate, and useful. Thus, whatever the form of the visualization - be it
a simple chart or an elegant, integrated animation - it should be streamlined to embrace
only the most essential variables and exclude any and all extraneous data. By this we do
not mean excluding data that does not tend toward a particular pre-defined result. Hardly.
But we
do
mean to exclude data which is
irrelevant
to ascertaining an accurate result.
With an increasing number of “gee-whiz” simple, powerful (and, frankly,
fun
) data
visualization tools at our fingertips everyday, there is always a great temptation to “over-
design” our graphics, thus eliminating the very simplicity which is their goal, and thus also
making it harder - not easier - to spot patterns. As one data visualization guru has ex-
plained: “The best design gets out of the way between the viewer's brain and the content.”
Noah Illinsky, an IBM visualization expert, tells us that “despite what you were told
in school, most people don't care about seeing your work. They don't care about how much
data you can process every day or how big your Hadoop cluster is. Customers and internal
users want specific, relevant answers, and the sooner they can get those answers, the better.
The closer you can come to giving them exactly what they want, the less effort they have
to expend looking for answers. Any irrelevant data on the page makes finding the relevant
information more difficult; irrelevant data (no matter how valid) is noise.”
And noise is what we want to avoid at all costs.
