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Data Visualisation: A Game of Decisions

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Andy Kirk
Andy Kirk

Talk by Andy Kirk, delivered during a Sage webinar on Friday 1st July.

Design
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DATA VISUALISATION A GAME OF DECISIONS Andy Kirk andy@visualisingdata.com www.visualisingdata.com @visualisingdata
IMPERFECTIONS COMPLEXITIES
The visual representation and presentation of data to facilitate understanding A game of decisions: There’s no such thing as perfect
A game of decisions: There’s no such thing as perfect Perceiving Interpreting Comprehending What does it mean? Is it good or bad? Meaningful or insignificant? Unusual or expected? What does it show? What’s plotted? How do things compare? What relationships exist? What does it mean to me? What are the main messages? What have I learnt? Any actions to take? CREATOR’S RESPONSIBILITY CONSUMER’S RESPONSIBILITY
What colour shall we make the axis lines? How thick should the lines be? How long should the lines be? How will we label them? Should we label them? Do we want tick marks as well? Do we even need the lines? It depends. A game of decisions: Complex more than complicated
To make the best decisions you need to be familiar with all your options and aware of the things that will influence your choices. A game of decisions: Complex more than complicated THINGS YOU COULD DO THINGS YOU WILL DO “IT DEPENDS”
Workflow A framework for optimising your critical thinking
Effective visualisation is TRUSTWORTHY Effective visualisation is ACCESSIBLE Effective visualisation is ELEGANT Design workflow: Effective decisions, efficiently made Do I have believe that what I see is faithful to the data and the subject? Am I able to understand this work with a proportionate amount of effort? Does the way this work is presented inspire me to engage with it?
Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution
Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution What’s the curiosity? What are the project conditions? What’s the purpose?
http://filmographics.visualisingdata.com/ “What is the pattern of success or failure in the movie careers of a range of notable actors/directors?” What’s the curiosity? “An eagerness to understand something”
What are the conditions? The factors and requirements https://github.com/propublica/weepeople
What are the conditions? The factors and requirements http://chartmaker.visualisingdata.com/
What’s the purpose? How will understanding be facilitated? https://www.bbc.co.uk/weather Explanatory Exploratory Exhibitory
Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution Stage 2 Working with data Data acquisition, examination, transformation, and exploration
Working with data: Understanding its properties and qualities Qualitative (Textual) Bolt quote: “It wasn't perfect today, but I got it done and I’m pretty proud of what I've achieved. Nobody else has done it or even attempted it” Categorical (Nominal) The athletics event: Men's 100m Categorical (Ordinal) The medal category: Gold Quantitative (Interval) The estimated temperature at track level during the Men's 100m: 28℃ Quantitative (Ratio) Usain Bolt’s winning time: 9.81 seconds
HEADING SUMMARY STATS CREDITS LOGO 63 matches = 8 x 8 grid Working with data: Understanding its properties and qualities http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/
Working with data: Understanding its properties and qualities http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/ X-axis = 0 to 120 minutes Y-axis = -3 to +6 goal difference
Working with data: Understanding its properties and qualities
Working with data: Understanding its properties and qualities
Working with data: Understanding its properties and qualities WHO? WHAT? HOW MUCH?
Working with data: Understanding its properties and qualities
Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 4 Developing your design solution Stage 2 Working with data Stage 3 Establishing your editorial thinking What questions are you trying to answer in support of the overriding curiosity?
Editorial: Which angle(s) of analysis are relevant/interesting? How good was my run? What distance did I run? What time/pace did I run it in? What were my main achievements? What was the route elevation? What were my 1km splits?
Editorial: Which angle(s) of analysis are relevant/interesting? How good was my run?
Editorial: How will you frame your data (include vs. exclude)?
Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution Making data representation, interactivity, annotation, colour, and composition choices
Data representation: A recipe of marks and attributes Shape Line Form Point Size Position Angle Pattern Quantity Containment Connection Symbol Colour Visual placeholders to represent data items Visual properties to represent data values Direction
Data representation: A recipe of marks and attributes Size Colour Line
Data representation: A recipe of marks and attributes Shape Colour Size
Data representation: How to show what you want to say? CATEGORICAL Comparing categories and distributions of quantitative values TEMPORAL Showing trends and activities over time HIERARCHICAL Charting part-to-whole relationships and hierarchies SPATIAL Mapping spatial patterns through overlays and distortions RELATIONAL Graphing relationships to explore correlations and connections
Data representation: How to show what you want to say?
Interactivity: Controlling what and how your data is presented http://www.visualisingdata.com/olympics2016/
Annotation: Judging the right level of assistance http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/
Annotation: Judging the right level of assistance
Colour: Colouring all your chart and project contents http://filmographics.visualisingdata.com/
Colour: Colouring all your chart and project contents Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1
Colour: Colouring all your chart and project contents Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1 Colour blindness simulator colororacle.org
Colour: Colouring all your chart and project contents
BAR CHART UNIVARIATE BUBBLE PLOT BUBBLE PLOT SLOPE GRAPH MATRIX CHART Composition: Making layout, sizing and positioning decisions TITLE ABOUT THE DATA HEADLINES ABOUT THE SUBJECT SECTIONS & COMMENTARY
Composition: Making layout, sizing and positioning decisions Visualisation by Andy Kirk http://www.visualisingdata.com/olympics2016/
Composition: Making layout, sizing and positioning decisions WHO? WHAT? HOW MUCH?
Composition: Making layout, sizing and positioning decisions
Composition: Making layout, sizing and positioning decisions
Demonstration A framework for optimising your critical thinking
The importance of critical thinking to improve visual sophistication
The importance of critical thinking to improve visual sophistication
The importance of critical thinking to improve visual sophistication
Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution
Single slide overview to present analysis that shows “how staff feel about working here” to key stakeholders Formulating the brief: Requirements
Formulating the brief: Tool constraints
Working with data: Understanding its properties and qualities SURVEY RESPONSES 8 x question categories about work issues 5 x response categories for scale of feelings 40 x question-response quantities (%, 100% total per question) RESPONDENT DEMOGRAPHICS 4 x gender categories, 4 x quantities (% and abs. numbers) 3 x employment categories, 3 x quantities (% and abs. numbers) 6 x service length categories, 6 x quantities (% and abs. numbers)
1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown? Editorial thinking: What questions are you trying to answer?
Data representation: How to show what you want to say? CATEGORICAL Comparing categories and distributions of quantitative values TEMPORAL Showing trends and activities over time HIERARCHICAL Charting part-to-whole relationships and hierarchies SPATIAL Mapping spatial patterns through overlays and distortions RELATIONAL Graphing relationships to explore correlations and connections 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
Chart types: How to show what you want to say? Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employee.. Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Category Strongly Agree Agree Disagree Strongly Disagree No opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
Chart types: How to show what you want to say? 0 10 20 30 40 50 60 70 80 90 100 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Category No opinion Strongly Disagree Disagree Agree Strongly Agree Strongly Agree Agree Disagree Strongly Disagree No opinion 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Category Strongly Agree Agree Disagree Strongly Disagree No opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
Chart types: How to show what you want to say? -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? 0 10 Agreement Disagreement No-opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
Chart types: How to show what you want to say? -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Agreement Disagreement No-opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
Chart types: How to show what you want to say? Gender Female Male Other No response Employment Status Full-Time Part-Time No response Length of Service Less than 1 year Between 1 and 3 years Between 3 and 5 years Between 5 and 10 years Over 10 years No response Female Male Other No response 0 20 40 60 80 100 120 140 Gender Full-Time Part-Time No response 0 20 40 60 80 100 120 140 160 Employment Status Less than 1 year Between 1 and 3 years Between 3 and 5 years Between 5 and 10 years Over 10 years No response 0 10 20 30 40 50 60 70 80 90 Length of Service 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
Chart types: How to show what you want to say? Back-to-back bar chart Bar chartBubble chart 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
Interactivity: Controlling what and how your data is presented Q3. Strongly Agree = 45% More info | Download data | Contact Results filtered for female respondents
Annotation: Judging the right level of assistance Main observations verbalised
Colour: Colouring all your chart and project contents
Colour: Colouring all your chart and project contents
Colour: Colouring all your chart and project contents Response categories Demographic bars Background shading Title text Section title text Chart axis and value labels
Colour: Colouring all your chart and project contents
Composition: Defining all size and position decisions Survey results breakdown Demographic breakdown Title
Composition: Defining all size and position decisions
Developing your design solution
Developing your design solution
Developing your design solution Demonstrate the value of your work, its accuracy, and be transparent Show the most relevant things in the most appropriate form that minimises obstructions Optimise the aesthetic presentation to seduce and sustain an audience’s attention Effective visualisation is TRUSTWORTHY Effective visualisation is ACCESSIBLE Effective visualisation is ELEGANT
Learn more! ‘Introduction to Data Visualisation’ online course https://campus.sagepub.com/introduction-to-data-visualisation
DATA VISUALISATION A GAME OF DECISIONS Andy Kirk andy@visualisingdata.com www.visualisingdata.com @visualisingdata
Data Visualisation: A Game of Decisions
Data Visualisation: A Game of Decisions
Data Visualisation: A Game of Decisions

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Data Visualisation: A Game of Decisions

  • 1. DATA VISUALISATION A GAME OF DECISIONS Andy Kirk andy@visualisingdata.com www.visualisingdata.com @visualisingdata
  • 3. The visual representation and presentation of data to facilitate understanding A game of decisions: There’s no such thing as perfect
  • 4. A game of decisions: There’s no such thing as perfect Perceiving Interpreting Comprehending What does it mean? Is it good or bad? Meaningful or insignificant? Unusual or expected? What does it show? What’s plotted? How do things compare? What relationships exist? What does it mean to me? What are the main messages? What have I learnt? Any actions to take? CREATOR’S RESPONSIBILITY CONSUMER’S RESPONSIBILITY
  • 5. What colour shall we make the axis lines? How thick should the lines be? How long should the lines be? How will we label them? Should we label them? Do we want tick marks as well? Do we even need the lines? It depends. A game of decisions: Complex more than complicated
  • 6. To make the best decisions you need to be familiar with all your options and aware of the things that will influence your choices. A game of decisions: Complex more than complicated THINGS YOU COULD DO THINGS YOU WILL DO “IT DEPENDS”
  • 7. Workflow A framework for optimising your critical thinking
  • 8. Effective visualisation is TRUSTWORTHY Effective visualisation is ACCESSIBLE Effective visualisation is ELEGANT Design workflow: Effective decisions, efficiently made Do I have believe that what I see is faithful to the data and the subject? Am I able to understand this work with a proportionate amount of effort? Does the way this work is presented inspire me to engage with it?
  • 9. Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution
  • 10. Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution What’s the curiosity? What are the project conditions? What’s the purpose?
  • 11. http://filmographics.visualisingdata.com/ “What is the pattern of success or failure in the movie careers of a range of notable actors/directors?” What’s the curiosity? “An eagerness to understand something”
  • 12. What are the conditions? The factors and requirements https://github.com/propublica/weepeople
  • 13. What are the conditions? The factors and requirements http://chartmaker.visualisingdata.com/
  • 14. What’s the purpose? How will understanding be facilitated? https://www.bbc.co.uk/weather Explanatory Exploratory Exhibitory
  • 15. Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution Stage 2 Working with data Data acquisition, examination, transformation, and exploration
  • 16. Working with data: Understanding its properties and qualities Qualitative (Textual) Bolt quote: “It wasn't perfect today, but I got it done and I’m pretty proud of what I've achieved. Nobody else has done it or even attempted it” Categorical (Nominal) The athletics event: Men's 100m Categorical (Ordinal) The medal category: Gold Quantitative (Interval) The estimated temperature at track level during the Men's 100m: 28℃ Quantitative (Ratio) Usain Bolt’s winning time: 9.81 seconds
  • 17. HEADING SUMMARY STATS CREDITS LOGO 63 matches = 8 x 8 grid Working with data: Understanding its properties and qualities http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/
  • 18. Working with data: Understanding its properties and qualities http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/ X-axis = 0 to 120 minutes Y-axis = -3 to +6 goal difference
  • 19. Working with data: Understanding its properties and qualities
  • 20. Working with data: Understanding its properties and qualities
  • 21. Working with data: Understanding its properties and qualities WHO? WHAT? HOW MUCH?
  • 22. Working with data: Understanding its properties and qualities
  • 23. Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 4 Developing your design solution Stage 2 Working with data Stage 3 Establishing your editorial thinking What questions are you trying to answer in support of the overriding curiosity?
  • 24. Editorial: Which angle(s) of analysis are relevant/interesting? How good was my run? What distance did I run? What time/pace did I run it in? What were my main achievements? What was the route elevation? What were my 1km splits?
  • 25. Editorial: Which angle(s) of analysis are relevant/interesting? How good was my run?
  • 26. Editorial: How will you frame your data (include vs. exclude)?
  • 27. Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution Making data representation, interactivity, annotation, colour, and composition choices
  • 28. Data representation: A recipe of marks and attributes Shape Line Form Point Size Position Angle Pattern Quantity Containment Connection Symbol Colour Visual placeholders to represent data items Visual properties to represent data values Direction
  • 29. Data representation: A recipe of marks and attributes Size Colour Line
  • 30. Data representation: A recipe of marks and attributes Shape Colour Size
  • 31. Data representation: How to show what you want to say? CATEGORICAL Comparing categories and distributions of quantitative values TEMPORAL Showing trends and activities over time HIERARCHICAL Charting part-to-whole relationships and hierarchies SPATIAL Mapping spatial patterns through overlays and distortions RELATIONAL Graphing relationships to explore correlations and connections
  • 32. Data representation: How to show what you want to say?
  • 33. Interactivity: Controlling what and how your data is presented http://www.visualisingdata.com/olympics2016/
  • 34. Annotation: Judging the right level of assistance http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/
  • 35. Annotation: Judging the right level of assistance
  • 36. Colour: Colouring all your chart and project contents http://filmographics.visualisingdata.com/
  • 37. Colour: Colouring all your chart and project contents Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1
  • 38. Colour: Colouring all your chart and project contents Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1 Colour blindness simulator colororacle.org
  • 39. Colour: Colouring all your chart and project contents
  • 40. BAR CHART UNIVARIATE BUBBLE PLOT BUBBLE PLOT SLOPE GRAPH MATRIX CHART Composition: Making layout, sizing and positioning decisions TITLE ABOUT THE DATA HEADLINES ABOUT THE SUBJECT SECTIONS & COMMENTARY
  • 41. Composition: Making layout, sizing and positioning decisions Visualisation by Andy Kirk http://www.visualisingdata.com/olympics2016/
  • 42. Composition: Making layout, sizing and positioning decisions WHO? WHAT? HOW MUCH?
  • 43. Composition: Making layout, sizing and positioning decisions
  • 44. Composition: Making layout, sizing and positioning decisions
  • 45. Demonstration A framework for optimising your critical thinking
  • 46. The importance of critical thinking to improve visual sophistication
  • 47. The importance of critical thinking to improve visual sophistication
  • 48. The importance of critical thinking to improve visual sophistication
  • 49. Design workflow: Effective decisions, efficiently made Stage 1 Formulating your brief Stage 2 Working with data Stage 3 Establishing your editorial thinking Stage 4 Developing your design solution
  • 50. Single slide overview to present analysis that shows “how staff feel about working here” to key stakeholders Formulating the brief: Requirements
  • 51. Formulating the brief: Tool constraints
  • 52. Working with data: Understanding its properties and qualities SURVEY RESPONSES 8 x question categories about work issues 5 x response categories for scale of feelings 40 x question-response quantities (%, 100% total per question) RESPONDENT DEMOGRAPHICS 4 x gender categories, 4 x quantities (% and abs. numbers) 3 x employment categories, 3 x quantities (% and abs. numbers) 6 x service length categories, 6 x quantities (% and abs. numbers)
  • 53. 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown? Editorial thinking: What questions are you trying to answer?
  • 54. Data representation: How to show what you want to say? CATEGORICAL Comparing categories and distributions of quantitative values TEMPORAL Showing trends and activities over time HIERARCHICAL Charting part-to-whole relationships and hierarchies SPATIAL Mapping spatial patterns through overlays and distortions RELATIONAL Graphing relationships to explore correlations and connections 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
  • 55. Chart types: How to show what you want to say? Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employee.. Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Category Strongly Agree Agree Disagree Strongly Disagree No opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
  • 56. Chart types: How to show what you want to say? 0 10 20 30 40 50 60 70 80 90 100 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Category No opinion Strongly Disagree Disagree Agree Strongly Agree Strongly Agree Agree Disagree Strongly Disagree No opinion 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Category Strongly Agree Agree Disagree Strongly Disagree No opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
  • 57. Chart types: How to show what you want to say? -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? 0 10 Agreement Disagreement No-opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
  • 58. Chart types: How to show what you want to say? -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 Q1. Do you feel appreciated in your role? Q2. Are you sa sfied with your personal performance? Q3. Are you sa sfied with your team's performance? Q4. Is poor-performance effec vely managed by your manager? Q5. Does the organisa on allow you to raise issues of unfairness? Q6. Do you think the organisa on gets the most out of its talented employees? Q7. Do you consider this to be a learning organisa on? Q8. Does this organisa on's leader mo vate and inspire you? Agreement Disagreement No-opinion 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
  • 59. Chart types: How to show what you want to say? Gender Female Male Other No response Employment Status Full-Time Part-Time No response Length of Service Less than 1 year Between 1 and 3 years Between 3 and 5 years Between 5 and 10 years Over 10 years No response Female Male Other No response 0 20 40 60 80 100 120 140 Gender Full-Time Part-Time No response 0 20 40 60 80 100 120 140 160 Employment Status Less than 1 year Between 1 and 3 years Between 3 and 5 years Between 5 and 10 years Over 10 years No response 0 10 20 30 40 50 60 70 80 90 Length of Service 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
  • 60. Chart types: How to show what you want to say? Back-to-back bar chart Bar chartBubble chart 1. For each statement, what is the proportion of response sentiment? 2. For each respondent demographic group, what is the breakdown?
  • 61. Interactivity: Controlling what and how your data is presented Q3. Strongly Agree = 45% More info | Download data | Contact Results filtered for female respondents
  • 62. Annotation: Judging the right level of assistance Main observations verbalised
  • 63. Colour: Colouring all your chart and project contents
  • 64. Colour: Colouring all your chart and project contents
  • 65. Colour: Colouring all your chart and project contents Response categories Demographic bars Background shading Title text Section title text Chart axis and value labels
  • 66. Colour: Colouring all your chart and project contents
  • 67. Composition: Defining all size and position decisions Survey results breakdown Demographic breakdown Title
  • 68. Composition: Defining all size and position decisions
  • 71. Developing your design solution Demonstrate the value of your work, its accuracy, and be transparent Show the most relevant things in the most appropriate form that minimises obstructions Optimise the aesthetic presentation to seduce and sustain an audience’s attention Effective visualisation is TRUSTWORTHY Effective visualisation is ACCESSIBLE Effective visualisation is ELEGANT
  • 72. Learn more! ‘Introduction to Data Visualisation’ online course https://campus.sagepub.com/introduction-to-data-visualisation
  • 73. DATA VISUALISATION A GAME OF DECISIONS Andy Kirk andy@visualisingdata.com www.visualisingdata.com @visualisingdata