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Best Practices for Killer Data Visualization
The webinar discusses visual analytics, focusing on its importance in data representation and cognitive improvement. Key points include the role of human perception in analyzing data, best practices for visualization, and the significance of dashboards in presenting data effectively. The session features speakers from Qualtrics and Tableau, along with guidance on submitting questions via chat and using Twitter for engagement.
In this document
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Introduction and housekeeping details for the presentation, including recording availability and agenda topics.
Introduction of webinar speakers: Sasha Pasulka and Shane Evans.
Discussion on the partnership between Qualtrics and Tableau, emphasizing the benefits of their collaboration.
Definition of visual analytics and its significance in enhancing cognitive processing.
Presentation of data through examples, including statistical metrics on x and y values.
Insights on human cognition and perception in relation to visual data representation.
Guidelines on effective data visualization, including data types, color schemes, mapping, and dashboard design.
Principles for creating effective dashboards that are functional and viewer-friendly.
Session opened for questions & answers, followed by concluding remarks.
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Best Practices for Killer Data Visualization
- 2. Housekeeping o The recordingand slides for today’s presentation will be made available within the next 48 hours. o Please use the chat window to submit questions throughout the webinar. We will have time designated at the end for Q&A. o Join the conversation on Twitter by tweeting @Qualtrics using #visualdata
- 3. o Qualtrics &Tableau Partnership o What is Visual Analytics? o Human Perception and Cognition o The Cycle of Visual Analysis o Visualization Best Practices o Q&A Today’s Agenda
- 4. Webinar Speakers Sasha Pasulka Director,Product Marketing Shane Evans Director, Strategic Alliances & Partnerships
- 5. o Innovation Exchange o Why Tableau? o Qualtrics + Tableau = Better Together Qualtrics Ecosystem
- 6. Visual Analytics BestPractices Sasha Pasulka Director, Product Marketing [email protected]
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- 10. “Visual analytics isthe representation and presentation of data that exploits our visual perception abilities in order to amplify cognition.” - Andy Kirk, author of “DataVisualization: a successful design process”
- 11. Let’s Look atSome Data I II III IV x y x y x y x y 10 8.04 10 9.14 10 7.46 8 6.58 8 6.95 8 8.14 8 6.77 8 5.76 13 7.58 13 8.74 13 12.74 8 7.71 9 8.81 9 8.77 9 7.11 8 8.84 11 8.33 11 9.26 11 7.81 8 8.47 14 9.96 14 8.1 14 8.84 8 7.04 6 7.24 6 6.13 6 6.08 8 5.25 4 4.26 4 3.1 4 5.39 19 12.5 12 10.84 12 9.13 12 8.15 8 5.56 7 4.82 7 7.26 7 6.42 8 7.91 5 5.68 5 4.74 5 5.73 8 6.89
- 12. I II IIIIV x y x y x y x y 10 8.04 10 9.14 10 7.46 8 6.58 8 6.95 8 8.14 8 6.77 8 5.76 13 7.58 13 8.74 13 12.74 8 7.71 9 8.81 9 8.77 9 7.11 8 8.84 11 8.33 11 9.26 11 7.81 8 8.47 14 9.96 14 8.1 14 8.84 8 7.04 6 7.24 6 6.13 6 6.08 8 5.25 4 4.26 4 3.1 4 5.39 19 12.5 12 10.84 12 9.13 12 8.15 8 5.56 7 4.82 7 7.26 7 6.42 8 7.91 5 5.68 5 4.74 5 5.73 8 6.89 Let’s Look at Some Data Property Value Mean of x in each case 9 (exact) Variance of x in each case 11 (exact) Mean of y in each case 7.50 (to 2 decimal places) Variance of y in each case 4.122 or 4.127 (to 3 decimal places) Correlation between x and y i n each case 0.816 (to 3 decimal places) Linear regression line in each case y = 3.00 + 0.500x (to 2 and 3 decimal places, respectively)
- 13. Let’s Look atSome Data …Visually “Anscombe’s Quartet” Source:Wikipedia
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- 15. We’re Faster WhenWe Can “See” Data
- 16. We’re Faster WhenWe Can “See” Data
- 17. We’re Faster WhenWe Can “See” Data
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- 19. Visual Interruptions MakePeople Slow
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- 21. Best Practices Overview 1. Representing data for humans 2. Color 3. Maps 4. Creating dashboards
- 22. Types of Data • Qualitative (nominal) • Arizona, NewYork,Texas • Sarah, John, Maria • Coors, Bud Light, Stella Artois • Qualitative (ordinal) • Gold, silver, bronze • Excellent health, good health, poor health • Love it, like it, hate it • Quantitative • Weight (10 lbs, 20 lbs, 5000 lbs) • Cost ($50, $100, $0.05) • Discount (5%, 10%, 12.8%)
- 23. How Do HumansLike Their Data?
- 24. How Do HumansLike Their Data? Position Color Size Shape More important Less important
- 25. How Do HumansLike Their Data? • Time: on an x-axis • Location: on a map • Comparing values: bar chart • Exploring relationships: scatter plot • Relative proportions: treemap
- 26. How Do HumansLike Their Data? Orient data so people can read it easily Better Good
- 27. Color Me Impressed Colorperception is relative, not absolute
- 28. Color Me Impressed Providea consistent background
- 29. Color Me Impressed Humanscan only distinguish ~8 colors
- 30. Color Me Impressed Humanscan only distinguish ~8 colors
- 31. Color Me Impressed Forquantitative data, color intensity and diverging color palettes work well
- 32. Mapping to Insight Usemaps when location is relevant “Where do forest fires occur?”
- 33. Mapping to Insight Don’tuse maps just because you can
- 34. Mapping to Insight Usefilled maps (“choropleths”) for defined areas and only ONE measure
- 35. Mapping to Insight Filledmaps won’t work for multiple measures
- 36. Mapping to Insight Mapsdon’t have to be geographic
- 37. Mapping to Insight Mapsdon’t have to be geographic
- 38. Dashboards Dashboards bring togethermultiple views
- 39. Dashboards Dashboards should passthe 5-second test
- 40. Dashboarding for the5-second Test • Most important view goes on top or top-left • Legends go near their views • Avoid using multiple color schemes on a single dashboard • Use 5 views or fewer in dashboards • Provide interactivity
- 41. UseYour Words! • Titles • Axes • Key facts and figures • Units • Remove extra digits in numbers
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