The Science of Data Visualization

Transcription

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2 Welcome

3 # T C 1 8 The Science of Data Visualization Larry Silverstein Strategic Sales Consultant Tableau

4 Start Your (Visualization) Engines

5 Agenda The science of data visualization Best practices for building dashboards and visualizations

6 The Science of Data Visualization

7 Share of Worldwide Urban Population Growth

8 Share of Worldwide Urban Population Growth

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14 I II III IV x y x y x y x y

15 I II III IV x y x y x y x y Property Mean of x in each case Variance of x in each case Mean of y in each case Variance of y in each case Correlation between x and y in each case Linear regression line in each case 9 (exact) 11 (exact) Value 7.50 (to 2 decimal places) or (to 3 decimal places) (to 3 decimal places) y = x (to 2 and 3 decimal places, respectively)

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17 Thinking is Slow. Seeing is FAST. Cerebral Cortex Thinking Visual Cortex Seeing

18 Preattentive Attributes

19 Memory Limits Have we gained or lost customers over the last 4 years? Which city has grown the fastest? Total # of Registered Customers New York Seattle Boston Austin Total

20 Visual Representation vs. Table How do each of our locations compare?

21 Memory Limits 16 Numbers 4 Patterns

22 Overcoming Memory Limits Use familiar chart types Don t make people remember views Avoid large legends Use intuitive colors and shapes

23 Familiar Chart Type? Intuitive Encoding?

24 Familiar Chart Type? Intuitive Encoding?

25 Familiar Chart Type. Intuitive Encoding.

26 Visual Interruptions Make People Slow

27 Visual Interruptions Make People Slow

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34 Color, Data Types, Chart Types and Layouts

35 Best Practices for Color

36 Color Vision Deficiency (CVD)

37 Bars and Colors Caution! Be thoughtful when applying color to bars

38 Bars and Colors Caution! Be thoughtful when applying color to bars This is OK

39 Color Perception Color perception is relative, not absolute

40 Color Perception Provide a consistent background

41 Too Many Colors Humans can only distinguish ~8 colors This is not helpful.

42 Right Number of Colors Humans can only distinguish ~8 colors This is helpful.

43 Types of Data Qualitative (nominal) Arizona, New York, 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%)

44 How Do Humans Like Their Data? Position More important Color Size Shape Less important

45 1. Position

46 1. Position

47 What Goes Where? Emphasized Neither emphasized nor de-emphasized Emphasized Neither emphasized nor de-emphasized De-emphasized

48 What Goes Where? Research

49 Best Practices for Building Dashboards and Visualizations

50 Tables

51 Graphs > Tables for Spotting Trends

52 Chart Type Generalizations Time: on an x-axis Location: on a map Comparing values: bar chart Exploring relationships: scatter plot Relative proportions: treemap

53 ShowMe: Visual Best Practices - Automatically

54 Pies vs. Bars (An Unfair Fight, IMO)

55 A Tip for Using Stacked Bars

56 Bullet Chart > Gauges Background fill colors that encode qualitative ranges like bad, satisfactory and good Text Label Bar that encodes the performance measure Quantitative Scale Symbol marker that encodes the comparative measure

57 Bars vs. Lines Use line graphs to encode trends over time

58 Avoid Food Graphs

59 Tastier Solutions Part 1

60 Tastier Solutions Part 1

61 Tastier Solutions Part 2

62 Tastier Solutions Part 2

63 Tastier Solutions Part 3

64 Tastier Solutions Part 3

65 Making a Point About Lines

66 Making a Point About Lines

67 Making a Point About Lines

68 Be Careful Using Dual Axes

69 3D?!? Make Mine Small Multiples!

70 Mapping to Insight Use maps when location is relevant

71 Choropleth Maps

72 Choropleth Maps

73 Symbol Maps

74 A Strike Zone is a Map?!?

75 Tell the Truth!

76 So This Is What Really Happened

77 Sometimes, the Default isn t Best

78 Experiment You Won t Break your Data

79 Let s Talk About Dashboards

80 Are All Dashboards the Same?

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83 Iraq: Deaths on the decline

84 Which One to Use?

85 The Same Goals Make better, datadriven decisions Make changes for the better

86 The 5 Second Test

87 Dashboarding for the 5 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

88 Dashboarding for the 5 Second Test Use your words! Titles Axes & Units Key facts and figures Remove extra digits in numbers Great tooltips

89 Dashboarding for the 5 Second Test

90 Dashboarding for the 5 Second Test

91 Is Beautiful Design Important?

92 Recommended Resources

93 #TC18 Thank you! Larry Silverstein

94 Please complete the session survey from the My Evaluations menu in your TC18 app

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