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In our last video, we learned that our job as practitioners of data visualization is to provide context and purpose to data in order to convert that data into information. Then, we use that information to tell our audience a meaningful story. But what does that process look like? Let’s say a colleague hands us an Excel or Google spreadsheet full of facts and figures. Are we ready to jump right in and start making a chart?
Four steps before starting data visualization
- Identify your audience.
- Decide on your story.
- Double-check your data
- Choose the right amount of data
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In our last video, we learned our job
as practitioners of data visualization
0:00
is to provide context and purpose to data
to convert that data into information.
0:06
And use that information to tell
our audience a meaningful story.
0:12
But what does that process look like?
0:16
Let's say a colleague hands us an Excel or
0:20
Google spreadsheet full of facts and
figures.
0:22
Are we ready to jump right in and
start making a chart?
0:26
Not quite.
0:30
Before we start thinking visually,
we need to take four preparatory steps to
0:31
help us get ready to
select the right visual.
0:36
The first step is
identifying your audience.
0:39
In a textbook called Good Charts, author
Scott Berinato illustrates the importance
0:44
of knowing your audience by asking
a simple question: is this a good chart?
0:49
The presentation seems convincing enough.
0:55
The y-axis is labeled clearly
spanning from 0 to $50 million.
0:59
The x-axis presents the years 2010
to 2013 divided into quarters.
1:04
And the chart shows
an upward trend in the data.
1:11
While not every quarter
was equally strong,
1:14
it looks like the global revenue
increased from about $15 million
1:18
in early 2010 to closer to
50 million in 2012 and 2013.
1:24
So this is a legible illustration of
financial data. But is it a good chart?
1:30
The answer is we don't know because
we don't know who this chart is being
1:37
presented to.
1:41
As Berinato notes if you're presenting
to the company's board of directors,
1:42
this chart is probably
insufficiently detailed.
1:47
On the other hand, this might be a useful
chart in welcoming a new hire to
1:51
the company by presenting a broad
overview of the company's finances.
1:56
In the case of illustrating
Dennis Rodman's rebounding dominance,
2:02
who do you think these charts are for?
2:06
Would a casual fan who watches a few
games each year care whether Rodman or
2:09
Wilt was the more efficient rebounder?
2:14
Probably not.
2:17
This information is aimed at
hardcore fans with a solid
2:18
understanding of
basketball statistics.
2:22
Ben Morris does explain some
advanced concepts to make his case,
2:25
like providing a definition
of team rebound shares.
2:29
But Morris doesn't waste time
explaining what a rebound is or
2:34
that Wilt Chamberlain and Dennis Rodman
were both All Star rebounders,
2:38
since his audience should
understand that already.
2:42
If you find you don't know much about
your audience, do some research.
2:46
If you're not into basketball, and
2:51
someone assigns you to visualize a set of
basketball stats, read online articles and
2:53
talk to some hardcore fans until you
understand the language they speak.
2:58
The second thing to do before
beginning the process of selecting
3:03
a visual is to decide on your story.
3:08
You should have a specific
audience in mind at this point.
3:11
Imagine them encountering
your visualization somewhere.
3:15
What would you like them to do afterward?
3:18
Sometimes the goal of data visualization
is to help viewers understand
3:22
complex data.
3:26
A 2017 Wall Street Journal visualization
called the job market tracker presents
3:28
the United States employment data
from the late 1940s to the mid 2010s.
3:33
Provided you have no problem
distinguishing color, the chart makes it
3:40
pretty obvious that American unemployment
was especially low in the late 1960s, and
3:44
especially high during the early 1980s,
and during the recession of 2008 and 2009.
3:50
A nice feature of this
chart is its interactivity.
3:58
We can compare unemployment rates
in America by sex or by race.
4:01
Black unemployment numbers in America
are distressingly high compared to overall
4:07
employment rates.
4:12
So this chart tells a clear story of
racial inequity provided again that
4:13
viewers can distinguish green from red.
4:18
This chart could create problems for
colorblind users, an audience we'll talk
4:21
about in a later video on creating
accessible visualizations.
4:26
Data Visualization can sometimes
evoke an emotional response.
4:32
On the interactive timeline called
the Poppy Field project visualizes
4:35
the number of deaths from human conflict
since the start of the 20th century.
4:40
Poppies have been used as a symbol of
war remembrance since World War I, and
4:46
this graph uses the scale of individual
poppies to represent the number of
4:51
deaths in each war.
4:55
We can quickly see that World War II was
the deadliest war in modern history.
4:58
In addition, the length of the poppy stems
symbolize the length of each conflict.
5:05
The Israeli occupation of Palestine,
for example,
5:10
began in the late 1940s and
still persists today.
5:14
Other times,
you'll use your data to inspire action.
5:20
At the time Ben Morris wrote the case for
Dennis Rodman,
5:24
Rodman had been left out
of the 2010 vote for
5:27
the pro-Basketball Hall of Fame,
in his first year of eligibility.
5:31
Morris's story was to convince voters
of Rodman's underappreciated rebounding
5:36
greatness, and it must have helped as
Rodman made the Hall of Fame in 2011.
5:42
If you're trying to compel
users to invest in a company,
5:47
vote on a ballot initiative, or
take action on climate change,
5:51
you'll want to make sure the story
you tell is a powerful one.
5:55
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