Why is it suboptimal to visualize data as plain figures? What is the purpose of data visualization? Why should you care? What is the interplay between statistics, data analysis, and a good marketing story? In this talk, I'll give some answers and try to convince you to adopt best practices in dataviz.

1. Anne-Marie Tousch
Senior Research Scientist
@amy8492
please don't ruin it!
Data is
beautiful
Mach 12th, 2019

3. 2 reasons
to visualize
data
Photo by Yuri Loginov from Pexels

4. 4 •
I keep seeing plain tables.
❑ Do they want me to read all this?
❑ Did they copy-paste their slides from their paper?
❑ Do they care about their audience?
❑ Do they care about giving this talk?
❑ Are they hiding something?
❑ Do they realize a dataviz would be much more
powerful?
Most respectful interpretation?

5. 5 •
Efficient communication
A picture
tells a 1000
words.
Source: Business Insider, August 2016

6. 6 •
Summary statistics

7. 7 •
never trust summary
statistics alone;
always visualize your
data
Detecting patterns
http://www.thefunctionalart.com/2016/08/download-datasaurus-never-trust-summary.html

8. What is dataviz about?

9. 9 •
Maxwell’s model of Saturn’s rings, 1858
Visualizing is always great, not only for data

10. 10 •
Visualize algorithms http://playground.tensorflow.org

11. 11 • Pictures credits : Wiki Commons / the_jetboy CC2.0 / Sandor Vamos / Wiki Commons / Acute3D / Walkerssk / Pixabay / Wiki Commons / Wiki Commons
Many ways to apprehend the world
7,000,000 visitors a year2,500,000 rivets
10,100 tons

12. 12 •
Exploiting the human visual system
Ever heard of Gestalt
theory?

13. 13 •
Count the 3s below
Ready?

14. 14 •
Count the 3s below
756395068473
658663037576
860372658602
846589107830
Source: http://www.storytellingwithdata.com/book/downloads

15. 15 •
How many 3s?
Source: http://www.storytellingwithdata.com/book/downloads

16. 16 •
Count the 3s below
756395068473
658663037576
860372658602
846589107830
Source: http://www.storytellingwithdata.com/book/downloads

17. 17 •
Much easier now, uh?
Source: http://www.storytellingwithdata.com/book/downloads

18. How?
Eugene Kim CC2.0

19. 19 •
Define your
goal
Choose an
effective visual
Find the right
focus
Close the loop
Explore / Explain
Question?
Simple is better
Function first,
form next
Use color, size
Remove clutter
Do you answer your
question?
Do you have a
story?
Follow the process

20. 20 •
Follow best practices
Think accessibility
Use rules of thumbs
Actively take control
Be truthful

21. 21 •
When you want to focus the
attention on just a number or two
When you have a mixed
audience, for information lookup
To show the relationship between
two things
The best for continuous data over
time
Makes it very easy to compare
categories
To compare totals and also
subcomponents
Choose an effective, simple visual
Source: http://www.storytellingwithdata.com/book/downloads

22. 22 •
Pie charts are evil

23. 23 •
756395068473
658663037576
860372658602
846589107830
Remember
Source: http://www.storytellingwithdata.com/book/downloads

24. 24 •
There are many preattentive attributes
Source: http://www.storytellingwithdata.com/book/downloads

25. 25 •
But two are special
Colour is the most powerful tool you have.
Use it sparingly and resist the urge to use colour for the sake of being colourful.
Leverage colour selectively to highlight the important parts of your visual.
Size matters.
If you’re showing multiple things that are of roughly equal importance, size them similarly.
If there is one really important thing, leverage size to indicate that: make it BIG!

26. 26 •
Maximise data-ink ratio,
within reason.
Edward Tufte, The Visual Display of Quantitative Information

27. 27 •
Forgo chartjunk, including
moiré vibration, the grid, and the
duck.
Edward Tufte, The Visual Display of Quantitative Information

28. 28 •
The moiré effect

29. 29 •
Don’t let your design choices be happenstance.
They should be the result of explicit decisions.

30. 30 •
Select good defaults

31. 31 •
One Python trick:

32. 32 •
Take the control

33. Take-aways
Wikimedia Commons

34. 34 •
Know your data

35. 35 •
You should care
It’s not only about nice
graphics
There’s a wealth of resources
Well-grounded best practices

36. Further tips
Highlight the important stuff
Eliminate distractions
Create a visual hierarchy of
information
Make it accessible
1
2
3
4
Only highlight 10% of the overall visual. Use preattentive attributes to do so, even together
for very important stuff
When detail isn’t needed, summarize. Ask yourself if eliminating this would change
anything. If not, take it out. Push less impacting items to the background with light grey
Organize information to guide the audience. Follow a Z-pattern from top left to bottom right.
You might be an engineer, but it shouldn’t take someone with an engineering degree to
understand your graph.
Use simple language5
Choose simple language over complex, choose fewer words over more words, define any
specialized language with which your audience may not be familiar, and spell out
acronyms.
Be mindful of aestethics6
Be smart with colors. Pay attention to alignment to give a sense of unity and cohesion.
Leverage white space, and don’t add stuff just to fill space
Always prefer simple over complex

37. 37 •
The Visual Display of Quantitative Information. Edward Tufte. Graphics Press, 2d edition,
2001. The classic on beautiful, faithful displays.
Visualization Analysis and Design. Tamara Munzner. AK Peters / CRC Press, Oct 2014. A
comprehensive textbook.
Visualize this: the FlowingData guide to design, visualization, and statistics. Nathan Yau. John
Wiley & Sons, 2011. For practical examples and code.
The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting
Data, Facts, and Figures. Dona M. Wong. W. W. Norton & Company, 2013.
Storytelling with Data: A Data Visualization Guide for Business Professionals. Cole
Nussbaumer Knaflic. Wiley, 2015.
Books

38. 38 •
Tukey, John W. "The future of data analysis." The annals of mathematical statistics 33.1 (1962): 1-67. pdf
Cleveland, William S., and Robert McGill. "Graphical perception: Theory, experimentation, and application to
the development of graphical methods." Journal of the American statistical association 79.387 (1984): 531-554.
pdf
Gelman, Andrew, Cristian Pasarica, and Rahul Dodhia. "Let's practice what we preach: turning tables into
graphs." The American Statistician 56.2 (2002): 121-130. pdf
Gelman, Andrew, and Antony Unwin. "Infovis and statistical graphics: different goals, different looks." Journal of
Computational and Graphical Statistics 22.1 (2013): 2-28. pdf
Gelman, Andrew, and Thomas Basbøll. "When do stories work? Evidence and illustration in the social
sciences." Sociological Methods & Research 43.4 (2014): 547-570. pdf
Maaten, Laurens van der, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of machine learning
research 9.Nov (2008): 2579-2605. pdf
Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. "Examples are not enough, learn to criticize! criticism for
interpretability." Advances in Neural Information Processing Systems. 2016. pdf
Wongsuphasawat, Kanit, et al. "Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow." IEEE
transactions on visualization and computer graphics 24.1 (2018): 1-12. pdf
Research papers

39. 39 •
• Flowing Data
• Storytelling With Data
• The Functional Art
• Google Brain PAIR group
• colorbrewer2.org helps select colors
Blogs & other resources
Learn from good examples
• junkcharts
• vizwiz
• fivethirtyeight
• theguardian.com/data
But also from bad ones
• viz.wtf
Practice with makeovermonday
Interested? React on paris-wimlds.slack.com

40. Questions?
Thanks @Paolo Terzi
(Criteo) from whom I took a
bunch of slides
Colocho CC BY-SA 2.5

41. 41 •
Rule of thumb: function first, form next