These are the slides from the talk given by Andy Kirk (@visualisingdata) on a webinar hosted by Tableau Software on 20th July 2016. The title is 'Bringing Method to the Madness' and concerns a demonstration of a data visualisation design workflow.

1. BRINGING METHOD
TO THE MADNESS
Andy Kirk
www.visualisingdata.com
@visualisingdata

2. New book! ‘Data Visualisation: A Handbook for Data Driven Design’

3. PART A: Foundations
Ch 1. Definingdata visualisation
Ch 2. Visualisation workflow
PART B: The Hidden Thinking
Ch 3. Formulating your brief
Ch 4. Working with data
Ch 5. Establishingeditorialthinking
PART C: Developing your Design Solution
Ch 6. Data representation Ch 7. Interactivity
Ch 8. Annotation Ch 9. Colour
Ch 10. Composition
PART D: Developing your Capabilities
Ch 11. Visualisation literacy
Book structure and contents

4. FINISHSTART
Visualisation is a game of decisions
DECISIONS

5. To make the best decisions you need to be familiar with all your
options and aware of the things that will influence your choices.
GOOD visualisation is about making GOOD decisions
THINGS YOU
COULD DO THINGS YOU
WILL DO

6. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
4. Developing your
design solution

7. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUSTWORTHY
4. Developing your
design solution

8. Good data visualisations are TRUSTWORTHY
Lots of different ways of ‘lying’,intentionally or otherwise

9. “Communicatingwith numbers is, in many ways,
just like communicatingwith words. You make
decisions about what to emphasize and what to
downplay, and about how to convey a full
understanding of the subject at hand.”
Christopher Ingraham,The Washington Post
Quote from: https://www.washingtonpost.com/news/wonk/wp/2016/04/11/the-dirty-little-secret-that-data-journalists-arent-telling-you/ | Visualisation by FT https://twitter.com/sampoaxelsson/status/742617156060348416
Good data visualisations are TRUSTWORTHY
Numbers carry a veneer of authority and objectivity

10. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUSTWORTHY
ACCESSIBLE
4. Developing your
design solution

11. Visualisations from http://www.wsj.com/articles/who-wins-the-stanley-cup-of-playoff-beards-1431899011
Good data visualisations are ACCESSIBLE
Some subjects/analysis/techniques are simple...

12. Good data visualisations are ACCESSIBLE
Some subjects/analysis/techniques are complex...
Visualisation by FT https://twitter.com/theboysmithy/status/705323516711804928

13. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUSTWORTHY
ACCESSIBLE
ELEGANT
4. Developing your
design solution

14. Good data visualisations are ELEGANT
You noticeelegance morewhen it is missing

15. Visualisation by Hyperakt http://hyperakt.com/work-detail/338
Good data visualisations are ELEGANT
Visual harmony through good editing and holistic thinking

16. CASE STUDY: ‘FILMOGRAPHICS’
filmographics.visualisingdata.com

17. Criteria: (1) New project (2) Non-client work (3) Neutral subject

18. The visualisation design workflow: Stage 1
1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
4. Developing your
design solution

19. Curiosity #1: What makes a big movie?
Looking at different measurements that shape the notion of a movie’s size
Curiosity #2: Anatomy of a movie’s costs?
Comparing the anatomy of costs for prominent movies through time
Curiosity #3: What is the shape of different movie star careers?
Comparing the ebb and flow of success/failure
CONTEXT: Curiosity, Purpose & Circumstances

20. CONTEXT: Curiosity, Purpose & Circumstances
“What is the pattern of success or failure in the
movie careers of a range of notable actors?”

21. CONTEXT: Curiosity, Purpose & Circumstances
“To enlighten movie fans by showing them new insights about
the different career patterns of notable actors.”

22. PEOPLE
Stakeholders: Who is ultimate customer? Who are the influencers, interferers?
Audience: Informed or layperson? Captivated or indifferent?
CONSTRAINTS
Pressures: Timescales? Financial? Marketinfluence – emulate/distinguish?
Rules: Requirements about layout/size, style (colour,type, logo), technical compatibility?
CONSUMPTION
Frequency: One-off or replicable? Live or regular?
Setting: Rapid or prolonged? Remote or live?
DELIVERABLES
Size: How much work, how many things?
Format: Outputfor (1) print, (2) web, presentation, video, tool, physical? All?
RESOURCES
Creators: (1) Individual or (2) team? What capabilities?
Technical: What software, hardware, infrastructureis available?
CONTEXT: Curiosity, Purpose & Circumstances

23. CONTEXT: Curiosity, Purpose & Circumstances
Image from: https://www.kickstarter.com/projects/geniscarreras/philographics-big-ideas-in-simple-shapes
1 2

24. VISION: Purpose map, Ideas

25. VISION: Purpose map, Ideas

26. VISION: Purpose map, Ideas

27. VISION: Purpose map, Ideas

28. VISION: Purpose map, Ideas
Visualisations from: http://www.gavi.org/data-vis/, http://infobawards.s3.amazonaws.com/SPOTLIGHT-ON-PROFITABILITY_Krisztina-Szucs.png and http://graphics-info.blogspot.co.uk/2013/03/picassos-paintings.html

29. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
4. Developing your
design solution
The visualisation design workflow: Stage 2

30. Actor name
Gender
Actor DOB
Movie title
Movie release date (US)
Movie genre
Movie score/rating (critics and audiences)
Movie finances (budget, gross, US domestic and worldwide)
Awards
ACQUISITION: Shopping list

31. ACQUISITION: Research (sources)

32. ACQUISITION: Research (sources)

33. ACQUISITION: Research (actors)
Contemporary – Male (5)
Contemporary - Female (5)
2000s - Male (5)
2000s - Female (5)
1990s - Male (5)
1990s - Female (5)
1980s - Male (5)
1980s - Female (5)
Veterans - Male (5)
Veterans - Female (5)
Assorted - Directors (5)
Assorted - Comedy (5)

34. ACQUISITION: Collection method

35. EXAMINATION: Physical properties

36. TRANSFORMATION: Cleaning, creating, converting, consolidating

37. TRANSFORMATION: Cleaning, creating, converting, consolidating

38. EXPLORATION: Exploratory visual analysis

39. EXPLORATION: Exploratory visual analysis

40. EXPLORATION: Exploratory visual analysis

41. EXPLORATION: Exploratory visual analysis

42. EXPLORATION: Exploratory visual analysis

43. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
4. Developing your
design solution
The visualisation design workflow: Stage 3

44. Angle (1): How have the quantitativemeasures
of success (defined by adjusted global box
office takings and criticratings) for each actor
changed over time (date of release)?
Angle (2): How have the quantitativemeasures
of success (defined by adjusted global box
office takings and criticratings) for each actor
changed over time (age at release)?
Angle (3): What is the distribution of movies for
a given actor broken down by release year
(summarised across 6 interval groups)?
EDITORIAL: Defined perspectives(Angle, Framing, Focus)
Angle (4): What is the distribution of movies for a
given actor broken down by age at release
(summarised across 6 interval groups)?
Angle (5): What is the distribution ofmovies for a
given actor broken down by adjusted worldwide
box office takings (summarised across 6 interval
groups)?
Angle (6): How many Oscar nominationsand
awards have each actor achieved?

45. Framing: The inclusion criteria would be...
A hand-picked selection of actors (and some directors)
Only movies where credit involved acting/directing/voiceartist roles
Only theatrical releases
Only movies released from 1965 to the end of 2015
Focus: Emphasise selected movies and linked values in other charts
EDITORIAL: Defined perspectives(Angle, Framing, Focus)

46. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
4. Developing your
design solution
The visualisation design workflow: Stage 4

47. DATA REPRESENTATION: Encodings for Angles 1 & 2

48. DATA REPRESENTATION: Encodings for Angles 3, 4, 5 & 6

49. DATA REPRESENTATION: Mobile vs. desktop - smallify or simplify?

50. INTERACTIVITY: Early concept sketch

51. INTERACTIVITY: Features for ‘Data adjustments’ and ‘Presentation adjustments’

52. ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’

53. ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’

54. ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’

55. ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’

56. ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’

57. ANNOTATION: Features for ‘Project annotation’ and ‘Chart annotation’

58. COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’

59. COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’

60. COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’

61. COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’

62. COLOUR: Features of ‘Data legibility’, ‘Editorial salience’ and ‘Functional Harmony’

63. COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’

64. FILMOGRAPHICS
INTRODUCTION + more info
STATIC GRAPHIC IMAGE FILES (PNG) SIZED AND LOADED IN THIS
SPACE, ONE FOR EACH ACTOR
6 CATEGORIES
10 x ACTOR SELETIONS
Select
Select
NEED TITLE, IMAGES FOR CATEGORIES, IMAGES FOR ACTORS
IMAGES DEFAULT TO B&W, COLOUR REVEALED ON MOUSEOVER?
COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’

65. COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’

66. COMPOSITION: Features of ‘Project composition’ and ‘Chart composition’

67. FINAL WORK: Analysis (The ‘Curiosity Three’)

68. FINAL WORK: Analysis (The ‘Curiosity Three’)

69. FINAL WORK: Analysis (The ‘Curiosity Three’)

70. FINAL WORK: Analysis (Success machines)

71. FINAL WORK: Analysis (Success machines)

72. FINAL WORK: Analysis (Extremes)

73. FINAL WORK: Analysis (Extremes)

74. FINAL WORK: Analysis (Interesting gaps)

75. FINAL WORK: Analysis (Interesting gaps)

76. FINAL WORK: Analysis (Early career success)

77. FINAL WORK: Analysis (Late career success)

78. FINAL WORK: Analysis (Give it up, Bobby)

79. 1. Formulating your brief
2. Working with data
3. Establishing your
editorial thinking
Workflow: Effective decisions, efficiently made, clearly informed
TRUSTWORTHY
ACCESSIBLE
ELEGANT
4. Developing your
design solution

83. BRINGING METHOD
TO THE MADNESS
Andy Kirk
www.visualisingdata.com
@visualisingdata