Demonstrating Editorial Focus and Learning About Your Data - Data Visualization: A Successful Design Process - page 65

The product of our work here is a more sophisticated understanding of the stories
existing in our datasets about the given subject matter. This will help us form the
specific data questions that we'll be asking our visualization designs to answer.
We've found our stories, now we need the appropriate methods to tell them and
that's what Chapter 4 , Preparing and Familiarizing With Data , will explore.
An example of finding and telling stories
Before we move on, to help embed the understanding of data familiarization, visual
analysis and the difference between finding stories and telling stories, let's work
through a basic example.
Take the following sample table of data. The subject matter is the Olympic games
and specifically the total medals won by the top eight participating nations over five
recent events. The selection of the top eight is based on them being the top ranked
countries at the Beijing Olympics in 2008.
Suppose you were briefed to unearth some key stories around Olympics medal
winning trends in recent years, how would you go about it?
Let's start by just scanning the data with our eyes to find anything that stands out.
The main data issue appears to be that the Russian Federation medals total for 1992
was actually when it was known as the Soviet Union. It is noticeably higher than for
all the other Olympic events, due to the contributions of additional member states that
then made up the Soviet Union but who are now independent countries competing
in their own right. As it will be hard to unpick this value to isolate just those athletes
who would now be considered part of the Russian Federation, it will be sensible to just
ignore this value from our analysis. Otherwise, it will skew our interpretations.