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Data Visualization dataviz superpower

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Data Visualization dataviz superpower! Guidelines on using best practice data visualization principles for Power BI, Excel, SSRS, Tableau and other great tools!

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Data Visualization dataviz superpower

  1. 1. Data Visualization Super Power Jen Stirrup Data Whisperer Data Relish Ltd Level: Novice
  2. 2. JenStirrup • Boutique Consultancy Owner of Data Relish • Postgraduate degrees in Artificial Intelligence and Cognitive Science • Twenty year career in industry • Author JenStirrup.com DataRelish.co m http://bit.ly/JenStirrupRD http://bit.ly/JenStirrupLinkedI n http://bit.ly/JenStirrupMVP http://bit.ly/JenStirrupTwitter
  3. 3. Jen Stirrup • Boutique Consultancy Owner of Data Relish • Postgraduate degrees in Artificial Intelligence and Cognitive Science • Twenty year career in industry • Author • http://bit.ly/JenStirrupRD • http://bit.ly/JenStirrupLinked In • http://bit.ly/JenStirrupMVP • http://bit.ly/JenStirrupTwitter
  4. 4. • As a general rule, the most successful man in life is the man who has the best information. (Disraeli, 19th Century)
  5. 5. Parkinson’s Law • ‘Whatever information capacity you give to humans, they will use up’ • Structured data grows by about 30% each year
  6. 6. Data Proliferation
  7. 7. Solutions
  8. 8. • The endless cycle of idea and action, Endless invention, endless experiment, Brings knowledge of motion, but not of stillness; Knowledge of speech, but not of silence; .. Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information? Excerpt from The Rock by TS Eliot (1934)
  9. 9. Secret Sauce?
  10. 10. Genius depends upon the data within its reach. Ernest Dimnet
  11. 11. You have to start with the truth. The truth is the only way that we can get anywhere. Because any decision- making that is based upon lies or ignorance can't lead to a good conclusion. Julian Assange, Wikileaks
  12. 12. You have to start with the truth. The truth is the only way that we can get anywhere. Because any decision- making that is based upon lies or ignorance can't lead to a good conclusion. Julian Assange, Wikileaks
  13. 13. Objectives
  14. 14. Objectives
  15. 15. What?
  16. 16. Objectives
  17. 17. Use of colour: dark colours are considered to have higher values
  18. 18. What?
  19. 19. Objectives
  20. 20. Inaccurate
  21. 21. 3 0
  22. 22. Bad Dataviz Examples
  23. 23. Bad Dataviz Examples
  24. 24. Bad Dataviz Examples
  25. 25. Lost Finale: Mins Breakdown Filler Adverts Questions Answered
  26. 26. Chartjunk Example
  27. 27. Linear vs Quadratic
  28. 28. 12/5/2018 42
  29. 29. Chartjunk: unintended
  30. 30. Designing visualizations that communicate clearly doesn’t have to be complicated.
  31. 31. Keep it simple.
  32. 32. 46 Data where you want it 46
  33. 33. Data Visualisation Background 47 We have the tools. All we’ve got to do is imagine what could be. We can reinvent the present; we can transform the world around us.
  34. 34. 48 Almost 50% of your brain is dedicated to visual processing. David van Essen About 70% of your sensory receptors are in your eyes. Researchers found that colour visuals increase the willingness to read by 80%
  35. 35. Why is Data Visualisation Important?• It’s clearly a budget. It has a lot of numbers in it. George W Bush The different branches of Arithmetic - Ambition, Distraction, Uglification, and Derision. (Lewis Carroll)
  36. 36. • The use of computer- supported, interactive, visual representations of data to amplify cognition. (Stu Card, Jock Mackinlay & Ben Shneiderman)
  37. 37. • Computer-based visualization systems provide visual representations of datasets intended to help people carry out some task more effectively. (Tamara Munzner)
  38. 38. Challenger
  39. 39. Challenger
  40. 40. Challenger
  41. 41. Why are we doing this?
  42. 42. Anscombe’s Quartet mean(X) = 9, var(X) = 11, mean(Y) = 7.5, var(Y) = 4.12, cor(X,Y) = 0.816, linear regression line Y = 3 + 0.5*X
  43. 43. Data Visualisation
  44. 44. BusinessFocus business intelligence to win the race businessFocusednobusinessFocused strategictactical Innovating Despite Business •Cool gadgets •Buzz Word BI •Not Actionable Winning the Race •Differentiation •Listening to Customers •Data Aware •Actionable Knowledge “Ticking along” •Minimum Maintenance •No New BI Functionality •Low Adoption Running on the Spot •Regurgitation of the same •Focus on only known metrics •Standing Still
  45. 45. Why not just tables? Zimbabwean inflation rates (official) since independence Date Rate Date Rate Date Rate Date Rate Date Rate Date Rate 1980 7% 1981 14% 1982 15% 1983 19% 1984 10% 1985 10% 1986 15% 1987 10% 1988 8% 1989 14% 1990 17% 1991 48% 1992 40% 1993 20% 1994 25% 1995 28% 1996 16% 1997 20% 1998 48% 1999 56.9% 2000 55.22% 2001 112.1% 2002 198.93 % 2003 598.75 % 2004 132.75 % 2005 585.84 % 2006 1,281.1 1% 2007 66,212. 3% 2008 231,15 0,888.8 7% (July)
  46. 46. Thinking with your Eyes
  47. 47. Translated into picture…
  48. 48. Why Data Vis 12/5/2018 Footer Text 6 Computers have promised us a fountain of wisdom but delivered a flood of data (Frawley, 1992)
  49. 49. Why is Data Visualisation Important? • Computers have promised us a fountain of wisdom but delivered a flood of data (Frawley, 1992) • Challenging to understand data on its own • Computers as anti-Faraday machines
  50. 50. Why is Data Visualisation Important? • Networks allow us unprecedented access to data • Creative Thinking about data • See relationships better • Visual literacy
  51. 51. Data First Data
  52. 52. Data First Data Tabular Spatial
  53. 53. Data First Data Tabular Categorical Ordered Spatial
  54. 54. Abstract Data First Data Tabular Categorical Ordered Quantitative Ordinal Relational Spatial
  55. 55. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  56. 56. Visual Building Blocks Points Lines Shapes
  57. 57. Perceptual Patterns Attribute Example Assumption Spatial Position 2D Grouping 2D Position Sloping to the right = Greater Form Length Width Orientation Size Longer = Greater Higher = Greater Colour Hue Intensity Brighter = Greater Darker = Greater
  58. 58. Perceptual Patterns Attribute Example Graph Type Spatial Position 2D Grouping 2D Position Line Graph Form Length Width Orientation Size Bar Chart Colour Hue Intensity Scatter Chart
  59. 59. Visual Building Blocks
  60. 60. Quantitative Ordinal Nominal Position Position Position Length Gray gradient Shape Angle Colour gradient Colour hue Area Colour hue Gray gradient Gray gradient Length Colour gradient Colour gradient Angle Length Colour hue Area Angle Shape Shape Area
  61. 61. Guidelines • white space • data/ink • chartjunk • Context e.g. titles etc
  62. 62. Mobilising Visual Integration – Affordance • Highlighting – bright colours • Increasing Intensity = Increasing Values – Eye Tracking Studies • Eye Path going from cluster to legend, and back again (Ratwani, 2008)
  63. 63. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  64. 64. Information Seeking Mantra • Ben Schneiderman Summary Zoom and Filter Details on Demand
  65. 65. Information Seeking Mantra • Ben Schneiderman Summary Zoom and Filter Details on Demand
  66. 66. Information Seeking Mantra • Ben Schneiderman Summary Zoom and Filter Details on Demand
  67. 67. Visualising Big Data 87 Self-Service Insights Actions
  68. 68. 88 Different Tools for Different Jobs 88 • Power View • Power Map ▪ Highly Visual Design Experience ▪ Power View is an interactive, ad hoc, query and visualization experience. ▪ It is for business question ‘mystery’ solving ▪ Power Map is a new 3D visualization add-in for Excel helping you to analyse geographical and temporal data – Mapping – Exploring – Interacting
  69. 69. 12/5/2018 Copper Blue Business Intelligence Ltd 89
  70. 70. Sploms 12/5/2018 Footer Text 90
  71. 71. Back to the Royal Road • Questions? 12/5/2018 Footer Text 91
  72. 72. Revealing Patterns • Patternicity – Finding meaningful patterns in noise – This can be seen as an error in cognition – Brain as belief systems
  73. 73. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  74. 74. Pre-attentive Attributes Attribute Example Assumption Spatial Position 2D Grouping 2D Position Sloping to the right = Greater Form Length Width Orientation Size Longer = Greater Higher = Greater Colour Hue Intensity Brighter = Greater Darker = Greater
  75. 75. Pre-attentive Attributes Attribute Example Graph Type Spatial Position 2D Grouping 2D Position Line Graph Form Length Width Orientation Size Bar Chart Colour Hue Intensity Scatter Chart
  76. 76. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  77. 77. Visual Integration • Chartjunk • Data/Ink Ratio
  78. 78. Mobilising Visual Integration – Affordance • Highlighting – bright colours • Increasing Intensity = Increasing Values – Eye Tracking Studies • Eye Path going from cluster to legend, and back again (Ratwani, 2008)
  79. 79. Mobilising Visual Integration – Sequential Palettes – Diverging Palettes – Qualitative Palettes
  80. 80. Visual Integration
  81. 81. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  82. 82. Cognitive Integration • Building an understanding of the graph • Eye Path going from cluster to cluster, rather than cluster to legend (Ratwani, 2008)
  83. 83. Cognitive Integration • Summary first • Zoom and filter • Then details ‘on-demand’ » (Schneiderman, 1999)
  84. 84. Cognitive Integration • Comparison • Sorting • Bookmarks – analytical view of browsing
  85. 85. Mobilising Cognitive Integration • Humans are not good at judging: – 2D Area – Angles – 3D pie chart
  86. 86. Mobilising Cognitive Integration • Humans are not good at judging: – 2D Area – Angles • Pie Charts and Gauges rely on these characteristics…
  87. 87. Find Patterns in your data • Demo – Sparklines • What did we learn? • Making patterns in small spaces Session Code | Session Title 107
  88. 88. Tables Tables work best when the data presentation: • Is used to look up individual values • Is used to compare individual values • Requires precise values • Values involve multiple units of measure.
  89. 89. – Sequential Palettes – Diverging Palettes – Qualitative Palettes
  90. 90. Moire Illusion
  91. 91. Mobilising Cognitive Integration• Humans are not good at judging: – 2D Area – Angles
  92. 92. Mobilising Cognitive Integration• Humans are not good at judging: – 2D Area – Angles • Pie Charts and Gauges rely on these characteristics…
  93. 93. Summary • SSRS can help businesses to implement business performance management – Based on sound Business Intelligence principles – SSRS provides data visualisation components that are consistent with best practice – However, some components are not • There are different types of Dashboards, to cover different purposes
  94. 94. Reporting Services
  95. 95. IT Oriented Structured Reporting Business Oriented Click as you Think AnalysisGuided Analysis Reporting Services PerformancePoint Services Report Builder Power View Excel PowerPivot
  96. 96. Colour • 2D representation is better (Few, 2009) • brighter and darker colours = higher values Colour usage: • to highlight • to encode quantity • grouping items as well
  97. 97. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  98. 98. Cognitive Integration • Building an understanding of the graph – Eye Tracking Studies • Eye Path going from cluster to cluster, rather than cluster to legend (Ratwani, 2008)
  99. 99. 70% 30%
  100. 100. DATA RELATIONSHIPS NOMINAL COMPARISON DEVIATION . TIME-SERIES DISTRIBUTION CORRELATION PART-TO-WHOLE RELATIONSHIPS RANKING 4 RELATIONSHIPS
  101. 101. We’re not the only ones who are overwhelmed Everyone is trying to make sense of the data deluge (“big data”)
  102. 102. Choose your metrics wisely.
  103. 103. Make your Big Data Sing
  104. 104. Demo: Hortonworks Sandbox,Tableau, PowerBI
  105. 105. Balance depth with big picture
  106. 106. Balance depth with big picture
  107. 107. Balance depth with big picture
  108. 108. Balance depth with big picture
  109. 109. Q & A
  110. 110. Back to the Royal Road • Questions? 12/5/2018 Footer Text 133

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