why dashboard design should be (but usually never is) based on cognitive science unconference

Why Dashboard Designshould be (but almost never is)

based on Cognitive Science

Thomas Watkins

Catalog Telebriz Partner Internet Direct0

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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.

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.

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

Ready?

Find all the 5’s

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Ready?

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Ready?

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• Color can help; color can hurt!

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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!)

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

“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 NoMotion Flicker Yes, based on speed but limited

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.

Study list

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

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High visual impact

Low visual impact

Sparse data Rich data