This document provides best practices for data visualization based on a presentation by Dr. Shilpa Balan at California State University, Los Angeles for Big Data Day LA 2018. It discusses what visualization is, the potential for storytelling with data visualization, making sense of data, letting the data speak, using shapes and symbols in maps, dealing with data uncertainty, focusing on certain parts of the data, showing data connections, using limited color palettes, color scale options, avoiding color blindness issues, examples of bad charts to avoid, scaling charts to zero appropriately, avoiding 3D charts, enhancing visualizations with narrative commentary, the importance of good design, and creating good charts through high contextual awareness and excellent design execution.

1. Data
Visualization Best
Practices
DR. SHILPA BALAN
CALIFORNIA STATE UNIVERSITY, LOS ANGELES
BIG DATA DAY LA 2018

2. Figure Source: https://www.slideshare.net/PeterCochrane/big-data-v-data-mining

3. What is Visualization?
• Really powerful
visualization
touches both the
mind and the
heart.
Source: Beautiful Visualization. 2010. Edited by Julie Steele and Noah Iliinsky. Chapter 20. Jessica Hagy. O’reilly.
Original Figure Source: http://nobudgettravel.files.wordpress.com/2008/11/elephant-whole1.jpg
• 3 blind men touch
a part of an
elephant to
describe what an
elephant looks
like.

4. Data Point
Figure Source: https://en.wikipedia.org/wiki/Alpha_Centauri

5. DATA
VISUALIZATION..POTENTIAL
OF STORYTELLING

6. Figure Source: https://moz.com/blog/heres-how-to-combine-storytelling-and-data-to-produce-persuasive-content

7. Storytelling and
visual expression
are integral parts
of human culture

8. Make sense of data
Figure Source: https://www.huffingtonpost.com/paul-fp-pogue/guide-to-light-bulb-types_b_13675360.html

9. Let the
Data
Speak

11. Question +
Visual Data +
Context = Story

12. Shapes and
symbols are
commonly
used with
maps
Source: Stephen Thomas, 2015. Data Visualization with Javascript. No Starch Press.

13. DATA
UNCERTAINTY

14. Pre-attentive
Processing

15. Focus on certain parts of the data
Source: Evgeniy Yur’evich Gorodov and Vasiliy Vasil’evich Gubarev. Analytical Review of Data Visualization Methods in Application to Big
Data. Journal of Electrical and Computer Engineering. Vol. 2013. 7 pages.

16. Data
Connections

17. Source: Shilpa Balan and Joseph Otto. 2017. Business Intelligence in Healthcare with IBM Watson Analytics. CreateSpace.

18. Visual Focus
Source: Shilpa Balan and Joseph Otto. 2017. Business Intelligence in Healthcare with IBM Watson Analytics. CreateSpace.

19. Use a
Limited
Color
Palette
Figure Source: https://www.tableau.com/about/blog/2016/7/colors-upgrade-tableau-10-56782

20. Color Scale Options
Source: Nathan Yau. 2013. Data Points: Visualization That Means Something. p212. Wiley.

21. Color Blindness
Source: Nathan Yau. 2013. Data Points. Visualization That Means Something. p103. Wiley.

22. Example of a Bad Chart
Source: Scott Berinato, 2016. Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review.

23. Examples of Bad Pie Charts
Source: Rebecca Wolfe, 2014. Data Visualisation: A practical guide to producing effective visualisations for research communication. RESYST.

24. Scale to Zero
Source: Rebecca Wolfe, 2014. Data Visualisation: A practical guide to producing effective visualisations for research communication. RESYST.

25. Do not use 3D Charts
Source: Andy Kirk. Visualising Data. 2018. http://www.visualisingdata.com/2015/03/when-3d-works/
3D charts distort
the data by
giving
prominence to
the section
closest to the
front

26. 2018 Major
League
Baseball
Figure Source: https://trends.google.com/trends/story/US_cu_pnJPqmQBAACNWM_en

27. Narrative Commentary
Data
visualizations
can often be
enhanced by
narrative
commentary.
Source: Stanford Studies Data Storytelling, Techniques, and Design. Marisa Krystian.2016.
https://infogram.com/blog/stanford-studies-data-storytelling-techniques-and-design/

29. Steve Jobs
famously
remarked,
“Design is not
how it looks and
feels, but how it
works.”
Source: Beautiful Visualization. 2010. Edited by Julie Steele and Noah Iliinsky. Chapter 13. p224. Moritz Stefaner. O’reilly.

30. The Good Chart Matrix
Source: Scott Berinato, 2016. Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review.
High contextual
awareness and
excellent design
execution lead
to good charts.

31. THANK
YOU!