Demonstrating Editorial Focus and Learning About Your Data - Data Visualization: A Successful Design Process - page 72

The aim here is to provide a clear visual hierarchy emphasizing the two main
countries in our story and diminishing the contextualizing six nations into the
background. We could have removed the other six countries but, through the
use of a subtle shade of grey, we can still see them well enough to get a sense
of the overall rankings. That is all we need from them—context.
The title neatly frames the story, the subheading describes the chart and the data,
and the labels help the reader compare the two countries' relative trajectory.
The use of color attempts to help imply the positive improvement (orange = hot =
good) of China and the negative decline (blue = cold = bad) of Germany. Only the
bare minimum chart apparatus (the axis line) is included, once again, to allow the
main story to come to the fore.
Contrast this design approach for telling a story (explanatory) with the design of
the same chart method we used to find the stories (exploratory); here we provide
nothing more and nothing less than the reader requires to easily interpret the story.
This use of contrasting visual approaches for the same chart types but for different
intentions is important to recognize in your design work.
Summary
In this chapter, we have learned about the importance of editorial focus and
content reasoning—the ability to recognize the most important and relevant
stories in your data and the discipline of taking responsibility to optimize the
interpreting experience of your intended audience.
We worked through the mechanics of acquiring, preparing, and familiarizing
with your dataset. In particular, we highlighted the importance of our own
graphical literacy in the task of conducting visual analysis.
We identified numerous physical characteristics of our data that will help us
to discover key stories and help inform the types of data questions we may
seek to answer in our designs.
Finally, we worked through a demonstration of using visual analysis to make
sense of your data, to find stories yourself and then tell those stories to others.
We also saw an example of the contrasting visual design solutions used for
exploratory and explanatory visualization.
Now that all our preparatory work has been covered, in the next chapter, we
will move the methodology forward onto the design stage. Here we will learn
about all the options we need to judge and the decisions we need to take across
the five key layers that form the anatomy of any data visualization design.