UXPA 2013 Annual Conference Friday July 12, 2013 by Thomas Watkins Unconference session

1. Why Dashboard Design
should be (but almost never is)
based on Cognitive Science
Thomas Watkins

2. 0
1
2
3
4
5
6
Catalog Telebriz Partner Internet Direct
US Dollars
(millions)
Revenue by Sales Channels
A
B

3. Operating Costs
Revenue
11
12
13
14
15
Jan Feb Mar Apr May
USD
(millions) Costs vs. RevenueA
B

4. $0 $50,000 $100,000 $150,000
South
East
Central
West
Revenue by Region
A
B

5. Problem
• Most dashboards are far less effective than they
should be.
• Is it the responsibility of the UX community to fix
this?
• It is a difficult problem to solve.
– Proper graph construction is not taught in school
– Data visualization of business intelligence is an
extremely small niche area, even within UX!
– There are usually many organizational and
institutional obstacles to doing things the right way.

6. Why is this important?
• Business intelligence is critical to the
operation of virtually all modern institutions
• Visualization is the perhaps best way to
process data
• Dashboards are a common tool; that tool is
often broken; and it needs to be fixed.

12. What approach she we take?
• Two opposing approaches to making graphs
– Thinking like an artist?
• Striving to express oneself, using the data
– Thinking like a translator?
• Striving to translate the data from one language to
another language
– The mathematical language of the data
– The language of the human sensory, perceptual and cognitive
systems

13. Ready?

14. Find all the 5’s
987349790275647927137581201618129342095618203948947
471039438018732626102856637491928126932872910837426
396180293108815029186181893596609384716246940982621
211493093847102947582923471928301472837216234682901
987349790275647927137581201618129342095618203948947
471039438018732626102856637491928126932872910837426
396180293108815029186181893596609384716246940982621
211493093847102947582923471928301472837216234682901

15. Ready?

16. red blue orange purple orange blue
orange blue green red blue purple
green red orange blue red green
red orange purple orange blue green
green purple orange blue red orange

17. Ready?

18. red blue orange purple orange blue
orange blue green red blue purple
green red orange blue red green
red orange purple orange blue green
green purple orange blue red orange

19. • Color can help; color can hurt!

20. Ready?

21. A B

22. A B

23. A B

24. Ready?

25. A B

26. Conclusion from the 3 Demonstrations
– Color attributes can help performance
– Color attributes can hurt performance
– Humans are good at judging length, and bad at
judging area of objects
– These perceptual phenomena are universal (so why
don’t we use them!)

27. • Know the language of the human
sensory, perceptual and visual systems

28. “Language” for Human Quantitative Judgment
Category Attribute Quantitative
Color Hue No
Intensity Yes, but limited
Form 2-D position Yes
Orientation No
Line length Yes
Line width Yes, but limited
Size Yes, but limited
Intensity Yes, but limited
Shape No
Motion Flicker Yes, based on speed but limited

29. What does this mean for us Uxers?
• The UX practitioner community should stay informed
on the best practices for data visualization.
• Perhaps we should aim to steer data visualization
design efforts in our own organizations
• Perhaps we should also aim to be the thought leaders
in this area as the field continues to develop over time.
• Please take the handout sheet (up front), which is a basic
guide on how to choose appropriate graphs for effective data
visualization.

30. Study list
• Most useful & reputable authors in the field:
– Edward Tufte
– William Cleveland
– Stephen Few
.

31. High visual
impact
Low visual
impact
Sparse data Rich data