The Data and Measurement Issue - 28

From Numbers to Narratives
How storytelling and visualization illuminate data's meaning
by Beth Cavanaugh and Mark VanderKlipp
Often when we think of sharing information through data, we hear " the numbers speak for themselves. " As designers and
consumers of data, we all know that this is rarely the case. Representing data through clear storytelling and visualization is
essential for effectively communicating insights and information. By combining the power of narrative and visuals, you can
make complex data more understandable, memorable, and actionable for people.
Let's explore how this concept might come to life through a simple fable illustrating the powerful role that strong storytelling
and compelling data visualization can play. Along the way, take note of best practices and principles that make this possible.
Narrative Flow:
Arrange your data and
insights in a logical
sequence that guides
the audience through
the information.
Once upon a time, in a cozy company nestled on
planet Earth, worked a team of three colleagues.
They had a lovely team and enjoyed their busy days
together. One day the three colleagues decided to
upload their project content on a shared server;
while the data uploaded, they each took a coffee
break. Little did they know that a curious coworker
named Goldi Lex was also nearby, looking to
uncover some interesting insights on this project.
Audience:
Tailor your narrative,
considering their
level of familiarity
and preferences for
data presentation.
Goldi found the project folder on the shared server,
decided that it might help her, and clicked the folder
open. In it, she found three separate folders:
The data, the visualizations, and the insights that
told the story of each.
She started with the data folder...
First, she opened a file containing raw data. This file was
incredibly complex with multiple pivot tables and reams of sheets,
seemingly impossible to decipher. Goldi spent a few minutes
exploring but her eyes started to hurt, and she quickly gave up.
She opened a second data file that was almost completely
devoid of logic, let alone having tools to manipulate or visualize
threads in it. Poorly organized, it was quickly evident to Goldi
that there was not a clear problem statement developed prior to
gathering the information. It was roughly as insightful as a bowl
of vanilla pudding.
She opened a third data file and it was just right: the raw data
were collected in such a way as to provide proper context and
prepare data for interpretation. There were a clear introduction
and points of emphasis that encouraged users to further explore.
Goldi dove into this data set and started reviewing it, admiring
its thoroughness and thoughtfulness.
Context: Explain
the thinking behind
the data gathered,
the time period it
covers, and relevant
background.
Emphasis: Highlight
the most important
insights that support
your conclusions.

The Data and Measurement Issue

Table of Contents for the Digital Edition of The Data and Measurement Issue