Why dashboard design should be (but usually never is) based on cognitive science unconference
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The document discusses how dashboard design is often not based on principles of cognitive science, which results in dashboards being less effective than they could be. It advocates applying knowledge of human visual perception and quantitative judgment to dashboard design by thinking like a translator to communicate data in a way the human brain can easily understand. The document provides examples of how color, size, and motion influence human perception differently and suggests dashboard designers consider these factors to improve comprehension of data visualizations.
Why dashboard design should be (but usually never is) based on cognitive science unconference
- 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
Editor's Notes
- #3: BOTH graphs are saying the same thing. Use one of them to answer the following question:Imagine you’re Sr. Sales Director evaluating the 5 different channels through which your company brings in revenue. Comparing 2 of the channels, how does the revenue for ‘Telebriz’ compare to the revenue for ‘Partner’?
- #4: Same thing here, 2 graphs showing the same information:Imagine you’re a CIO taking a look at your company’s profit margin indicators. What is more stable over time, our revenue or our operating costs?
- #5: Simple question: who is doing better, the East Coast or the West Coast?