Good ol' excel is the ultimate data visualization tool (in most cases)

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Good Ol' Excel Is The Ultimate
Data Visualization Tool (In Most
Cases)
Posted by Cyrille Vincey
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Since our post about Infographics, a *lot* of people,
stunned by Tufte's "less is more" gif, have been asking
us lately what's our recommendation for a data
visualization package.
Considering the size of their datasets, and the kind of
story they wanna tell, our answer has been invariably
the same: "Well, use your dusty ol' Excel". Surprising
isn't it?
Data Visualization, you said?
I already wrote earlier that we want to stay away from
this mainstream worship of the Infographics golden
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Good Ol' Excel Is The
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Most Cases)
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calf. Instead, we want to stick with the Data
Visualization Tables of the Law, given by God to
Edward Tufte:
1. Talk to my eye, not to my brain: rely on
proven types of visualization to make sure you
2. Less is more: suppress everything that's not
absolutely usefull for the understanding of a
chart
3. One chart = one takeaway: when looking at
your chart, I should be able to infer your
message in a snap
4. Tell a story: build a directed sequence of
messages
"Take this, Edward Tufte, and tell your disciples at
qunb
this is the worst introduction ever in the History of
Blogging"
Is there a Raise and Fall of Excel
as a Data Visualization tool?
I've been a consultant for, well, too long. Excel was my
bitch. Unfortunately, I have to concede that Microsoft
missed the boat of modern Data Visualizations.
Posts by Topic
Tutorials (11)
Data Storytelling (9)
Life (7)
Fun (4)
Updates (2)

People are now expecting well designed, webby, and
interactive data visualizations. Hence data nerds have
turned to advanced data analysis tools like Tableau.
If you're a quant, and if you can afford the time and
the money, that's an awesome tool. But if you aren't,
you're stuck with Excel, and its shitty charts.
This is why qunb was created: to help anyone to
seamlessly create and tell visual stories. Today with
your Google Analytics data (check it out), tomorrow
with SalesForce, and in 2014 with any excel data.
The wait is killing you? Well, meanwhile, is Excel that
bad?
Yes, default charts in Excel look
like they were done by your
father in 1993
Let's take as a simple dataset of the latest funding
deals, based on Form D information. By default,
here's how a chart on that dataset would look like:
Ugly, pointless...
But don't cry! Excel is (still) the

Ultimate Data Visualization tool
in most cases
Yes, my friends, no need to waste time nor money to
start mastering a complex data visualization package.
We can do a pretty good job at publishing good
looking charts, with regard to the current design best
practices, only by using the standard version
of Excel.
Provided that you don't need interactive charts
(charts enabling data discovery for the reader), nor
maps, nor fancy visualization types that your reader
will probably struggle to read anyway.
If you just need good looking modern - yet static -
charts, like donuts, 2D charts, Excel is still the best
option. Doubtful? Well, go through the data story
below, 100% made with Excel. Then we'll see the tips
& tricks to issue those kind of charts.
Here's a 100%-Excel visual story
of startup funding
First let's focus on the proportion of tech deals vs
deals in other industries:

Then let's show how this proportion evolves over
time:
Now we're intrigued. Is this trend compensated by
the size of funding deals? Let's see:
So, what does the global amount raised look like?

That's it for today. I know, it's frustrating. Be patient,
we're baking a complete and compelling data story
with this dataset of funding deals data. Crispy,
unexpected things lying in this data.
So if you need to leave us now...
Subscribe to email updates to getSubscribe to email updates to get
the complete startup funding datathe complete startup funding data
storystory
Ok, you're still here? Let's look together how to make
your excel charts look like this.
Our 8-step recipe to "tufterize"
your excel charts
I wont cover in this section the tips and tricks for data
discovery and data analysis with Excel, I'll keep that

for a later post. Soon, I promise. We'll talk about
aggregation, clustering, segmentation, made easy
with excel. Can't wait.
For now, Let's only talk here about visual
improvements to turn the default excel chart into
one of those charts. Build your pivot table, then build
your default chart (here a stacked column: Chart >
Column > Stacked Column):
Then move the chart to a new page (right click > Move
charts > New sheet).
#1. Start with deleting all the visual
pollution
Delete the legend, the horizontal gridlines, and the Y
axis.

#2. Color set : one vivid color, and grays.
No more.
Double-click on each category (blue, red, green, ...),
then go:
in the "Fill" menu: change to color
in the "Line" menu: apply the same color
in the "Shadow" menu: disable the "shadow"
option
#3. Optimize the x axis
Double-click on the x axis, then go:
in the "Ticks" menu: select "Major Tick Mark

in the "Ticks" menu: select "Major Tick Mark
Type" [none]
In the "Fill" menu: change Color from
[Automatic] to [No Fill]
in the "Line" menu : change Color from
[Automatic] to [no Line]
Then tweak the x axis labels:
Change the font (Calibri smells MS Office like
hell). I personally like "Century Gothic".
Enlarge the font
Chage the Font Color (black => gray)
#4. Add Data Labels
Legends should be banned from Excel. You should
make sure you use direct labelling only, not only for
values, but also for your categories. Here is a little
trick.
First add data labels on every category (right click >
"Add Data Labels"). Change the font, the font size, and
the font color.

#5. Delete useless labels
You should keep only the values you want your
reader to pay attention to. Usually, only extreme
values are really helpful to support your message.
Select useless labels one by one and delete them. Just
keep one useless label by category (you'll use it as a
legend in the next step).
#6. Add direct labelling to your chart
For each "useless" data label you kept in the previous
step, double-click on it, then in the dialog box

in the "Labels“ menu: uncheck "Value", and
check "Series name"
In the "Font" menu: change the "Font Color" to
the color of your category (here, red or gray)
Then select the Plot area (click somewhere in an
empty place in the chart zone) and select the right
handle to reduce the chart width a bit and let some
room for the category names. At last, move the
category names you just created on the right to place
them as category legends.
#7. Add an explicit takeaway to your chart
Even if you think your chart is self-explanatory, you
should add a title to comment your chart. You should
also make sure that this comment explains what the
chart is talking about. Global amounts raised?
Turnover? ...
If your comment does not explicitely tell the reader
what the chart is actually about, make sure you add a
technical description of your chart somewhere (ex:
"Cumulated turnover of...").

#8. Remove frames and borders
If you copy your chart like this, you'll see an ugly
border. Go in the "Chart Format" dialog box (right
click outside the plotting area > "Format Chart Area").
Then
In the "Fill" menu: change Color from
[Automatic] to [No Fill]
In the "Line" menu: change Color from
[Automatic] to [No Line]
Your chart is ready to get copied and paste wherever
you want.
Congrats, your data looks better
naked, just with excel
To conclude, here's a Darkhorse Analytics's awesome
gif summarizing this loooooong post.

Now, if you prefer to see the
future in action...
Shut up and let me test qunb with
my Google Analytics
Topics: Data Storytelling
Published on Oct 16, 2013 2:07:00 PM
173 6593
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• Reply •
CO • 7 months ago
Most of your changes are decent, but removing
lines and axes and using "direct labeling" is an
amateur hour mistake often mocked in intro stats
or data visualization courses. Everything else up to
that is fine, but once you start messing with the
natural guidelines the brain uses to line things up,
you're asking for trouble.
2
• Reply •
irinai • 6 months agoCO
Don't intro stats use terrible-looking charts?
• Reply •
CO • 6 months agoirinai
What kind of intro stats did you
take?
1
• Reply •
josep2 • 7 months ago
I agree with a lot of what it written here. I will say
excel is very limited with certain kinds of plots.
Anything outside of what you demonstrated here, it
falls apart.
3
Cyrille Vincey • 7 months agojosep2
Thanks for your comment. You are right, but
the kind of visualizations I've used in this
post represent 100% of the kind of charts
I've used in my whole consulting lifetime.
(oh, I forgot maps)
What I mean here is that more complex
representations (say bubble charts, plots, ...)
are good as WIP for analysts exploring data,
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• Reply •
are good as WIP for analysts exploring data,
but not good for the final restitution of a
story based on data.
1
• Reply •
Hubi
• 7 months ago
Cyrille Vincey
Excel 2003 maybe but if you guys
haven't seen what's happening with
Excel since the free PowerPivot add-
in was released then you need to get
with the program.
http://www.powerpivotpro.com/
• Reply •
Privat Privat
• 2 months ago
Hubi
*Excel 2003
= Good times
Excel in the webbrowser
= Horrible experience
waynergf • 7 months ago
Argh! Amazing - a blog posting that purports to
illustrate good visualization practices litters one of
the worst practices throughout: stacked bar
charts...and of data that occur over time yet!
As Tufte has pointed out, the purpose of graphs is
to allow meaningful comparisons that provoke
questions and thus begin the processes of
discovery and learning - ultimately leading to good
decisions.
Stacked bar charts obscure comparisons because
the reference point / line / plane for each of the
stacked portions changes with each bar - bad
practice. Data over time should be plotted over
time - using the appropriate Statistical Process
Control (SPC) chart so that random variation
("Common Causes") may be distinguished from
real effects ("Special Causes"). This is critical
because the two types of variation require different
actions; when the two are confused the decisions
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actions; when the two are confused the decisions
made and the actions taken make matters (the
process' performance) worse.
["In the land of the blind, the one-eyed man is
king."]
1
• Reply •
Jordan Goldmeier
• 7 months ago
waynergf
I was thinking the same thing - perhaps not
has harshly - but it's fair to say Tufte would
not approve of the solution attributed to him
here.
• Reply •
The Daily Graph • 7 months ago
Couldn't have said it better. There is a blog at
http://www.apptrac.net/en/blog... about using
Excel to
re-create charts that have appeared on the website
of The Economist.
It's all plain-vanilla Excel, no macros or VBA, and
you can download
workbooks with the charts for free.
1
• Reply •
Cyrille Vincey
• 7 months ago
The Daily Graph
Awesome site, we've just spent 1h on
apptrac. Thanks for the link!
• Reply •
Chris • 7 months ago
Might want to fix the typo unless this was done on
purpose. Also, great post. Never thought about
simplifying the charts to make them look more
elegant.
"To conclude, here's a Darkhorse Analytics's
awesome gif summarizing this loooooonb post."
• Reply •
Cyrille Vincey • 7 months agoChris
Oops. Done. Thanks Chris.
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• Reply •
• Reply •
Guest • a month ago
No Python3 support is a deal-breaker - you're
saying we should be happy to wait for years for this
package to mature only to be writing with a 10+
year old featureset? No thanks. I wish this project
all the best but I can't support it in good
conscience, for the Python community as a whole.
Rui Quintino • 3 months ago
Hi there,
Hot thread :) thought in adding our small app for
instant Excel data visualization, called smartcharts
http://rquintino.wordpress.com...
Feedback from the experts would be great! :)
Regards,
Rui Quintino
http://rquintino.wordpress.com...
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