Creating Powerful Data Visualizations Corvelle Drives Concepts to

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Creating Powerful Data Visualizations Corvelle Drives Concepts to Completion 1

Creating Powerful Data Visualizations Corvelle Drives Concepts to Completion 1

Yogi Schulz Biography q Corvelle Consulting q Information technology related management consulting q ITWorld

Yogi Schulz Biography q Corvelle Consulting q Information technology related management consulting q ITWorld Canada columnist & CBC Radio guest q PPDM Association board member q Industry presenter: – Project World - 6 years – PMI – SAC - 10 years – CIPS – many years – PPDM Association - several years Corvelle Drives Concepts to Completion 2

Vast Data Visualization Choice Corvelle Drives Concepts to Completion 3

Vast Data Visualization Choice Corvelle Drives Concepts to Completion 3

Presentation Outline q Introduction q Learning objectives q Powerful data visualizations: – Understand visualizations

Presentation Outline q Introduction q Learning objectives q Powerful data visualizations: – Understand visualizations – Create visualizations – Refine visualizations – Practice and present visualizations q Recommendations & actions Corvelle Drives Concepts to Completion 4

Learning Objectives q Understand design considerations that lead to powerful data visualizations q Understand

Learning Objectives q Understand design considerations that lead to powerful data visualizations q Understand effective techniques to create data visualizations q Understand best practice tips for presenting data visualizations Corvelle Drives Concepts to Completion 5

Understand Visualizations A Brief History of Data Visualization When a Chart hits our Eyes

Understand Visualizations A Brief History of Data Visualization When a Chart hits our Eyes Corvelle Drives Concepts to Completion 6

Whirlwind Tour of the History of Visualization Charles Minard 1861 William Playfair 1786 Tables

Whirlwind Tour of the History of Visualization Charles Minard 1861 William Playfair 1786 Tables & Ledgers 1700’s Cave drawings BCE Corvelle Drives Concepts to Completion 7

Florence Nightingale's 'Coxcombs‘ 1858 q Pioneer hospital sanitation q Meticulously gathered data q Pioneer

Florence Nightingale's 'Coxcombs‘ 1858 q Pioneer hospital sanitation q Meticulously gathered data q Pioneer in applied statistics and visualization q Nurse Corvelle Drives Concepts to Completion 8

Willard C. Brinton, 1914 First business book about visualization q Rules for presenting data

Willard C. Brinton, 1914 First business book about visualization q Rules for presenting data q American consulting engineer Corvelle Drives Concepts to Completion 9

Mary Eleanor Spear 1952, 1969 q Common-sense advice q Invented box plot q Worked

Mary Eleanor Spear 1952, 1969 q Common-sense advice q Invented box plot q Worked for various US government agencies Corvelle Drives Concepts to Completion 10

Jacques Bertin 1967 q Principle of expressiveness: – Say everything you want to say

Jacques Bertin 1967 q Principle of expressiveness: – Say everything you want to say — no more, no less – Don’t mislead q Principle of effectiveness: – Use the best method available for showing your data q Cartographer Corvelle Drives Concepts to Completion 11

Jacques Bertin Seven Visual Variables q Position q Size q Shape q Color q

Jacques Bertin Seven Visual Variables q Position q Size q Shape q Color q Brightness q Orientation q Texture Corvelle Drives Concepts to Completion 12

Edward Tufte 1983 q Disciplined design principles q Minimalist approach q Professor emeritus at

Edward Tufte 1983 q Disciplined design principles q Minimalist approach q Professor emeritus at Yale University Corvelle Drives Concepts to Completion 13

Jock Mackinlay 1986 q Automatically encode data with software q Enable people to focus

Jock Mackinlay 1986 q Automatically encode data with software q Enable people to focus on ideas, concepts q Added eighth variable to Bertin’s list: motion q VP of Research and Design, Tableau Software Corvelle Drives Concepts to Completion 14

When a Chart hits our Eyes 1. Visuals aren’t read in a predictable, linear

When a Chart hits our Eyes 1. Visuals aren’t read in a predictable, linear way – Create charts spatially, from the visual outward 2. We see first what stands out – Whatever stands out should support idea 3. We see only a few visuals at once – Plot as few visual elements as possible 4. We seek meaning and make connection – Relate visual elements in a meaningful way 5. We rely on conventions and metaphors – Embrace deeply ingrained conventions Corvelle Drives Concepts to Completion 15

Example: USA Energy Resources Corvelle Drives Concepts to Completion 16

Example: USA Energy Resources Corvelle Drives Concepts to Completion 16

Alternative Charts Corvelle Drives Concepts to Completion 17

Alternative Charts Corvelle Drives Concepts to Completion 17

Create Visualizations What kind of visual communication do you want to create? Better Charts

Create Visualizations What kind of visual communication do you want to create? Better Charts in an Hour Corvelle Drives Concepts to Completion 18

What kind of visual communication do you want to create? 1. Is my information

What kind of visual communication do you want to create? 1. Is my information conceptual or data-driven? – Conceptual information is qualitative – Data-driven information is quantitative 2. Are my visuals meant to be declarative or exploratory? – A declarative purpose is to make a statement – An exploratory purpose is to look for new ideas Corvelle Drives Concepts to Completion 19

Four Types of Data Visualizations Declarative Idea illustration Data-Driven Conceptual Idea generation Exploratory Corvelle

Four Types of Data Visualizations Declarative Idea illustration Data-Driven Conceptual Idea generation Exploratory Corvelle Drives Concepts to Completion 20

Four Types of Data Visualizations Declarative Idea illustration Everyday dataviz Data-Driven Conceptual Idea generation

Four Types of Data Visualizations Declarative Idea illustration Everyday dataviz Data-Driven Conceptual Idea generation Visual discovery Exploratory Corvelle Drives Concepts to Completion 21

1. Better Charts in an Hour Preparation: 5 minutes q Create a workspace q

1. Better Charts in an Hour Preparation: 5 minutes q Create a workspace q Put aside your data q Write down basics as constant reminders Corvelle Drives Concepts to Completion 22

2. Better Charts in an Hour Talk and listen: 15 minutes q Enlist a

2. Better Charts in an Hour Talk and listen: 15 minutes q Enlist a colleague q Write down words, phrases, and statements Corvelle Drives Concepts to Completion 23

3. Better Charts in an Hour Sketch: 20 minutes q Match keywords to chart

3. Better Charts in an Hour Sketch: 20 minutes q Match keywords to chart types q Start sketching, try out multiple visuals Corvelle Drives Concepts to Completion 24

Decision Trees for Chart Types Corvelle Drives Concepts to Completion 25

Decision Trees for Chart Types Corvelle Drives Concepts to Completion 25

4. Better Charts in an Hour Prototype: 20 minutes q Prototype approach Corvelle Drives

4. Better Charts in an Hour Prototype: 20 minutes q Prototype approach Corvelle Drives Concepts to Completion 26

Example: Capital Exposure and Risk Corvelle Drives Concepts to Completion 27

Example: Capital Exposure and Risk Corvelle Drives Concepts to Completion 27

I don’t have anything useful to say so I made this pie chart. Corvelle

I don’t have anything useful to say so I made this pie chart. Corvelle Drives Concepts to Completion Identify a Valuable Message 28

Refine Visualizations Refine to Impress Refine to Persuade Persuasion or Manipulation? Corvelle Drives Concepts

Refine Visualizations Refine to Impress Refine to Persuade Persuasion or Manipulation? Corvelle Drives Concepts to Completion 29

Refine to Impress Creating that sense of good design 1. Focus on design structure

Refine to Impress Creating that sense of good design 1. Focus on design structure and hierarchy: – Include: title, subtitle, visual field, source line – Align elements 2. Focus on design Title clarity – Make all elements support visual Visual – Remove ambiguity – Use conventions and metaphors field 3. Focus on design simplicity – Show only what’s needed Source – Minimize the number of colors American Fruit Growers Corvelle Drives Concepts to Completion 30 line

Refine to Persuade Making an accurate chart not enough 1. Hone the main idea

Refine to Persuade Making an accurate chart not enough 1. Hone the main idea – Start by saying I need to convince the audience that. . 2. Make main idea stand out – Use simple design techniques to reinforce your main idea – Emphasize main idea 3. Adjust what’s around main idea – Manipulate variables that complement main point – Eliminate data that distracts or dilutes – Add data to expose hidden context Corvelle Drives Concepts to Completion 31

Persuasion or Manipulation? 1. Truncated Y-axis – A chart removes valid value ranges from

Persuasion or Manipulation? 1. Truncated Y-axis – A chart removes valid value ranges from the yaxis, thereby removing data from the visual field 2. Double Y-axis – A chart includes two vertical scales for different data sets in the visual field 3. Map – A map uses geographical boundaries to encode values related to that location Corvelle Drives Concepts to Completion 32

Example: Charting the Wrong Variable Corvelle Drives Concepts to Completion 33

Example: Charting the Wrong Variable Corvelle Drives Concepts to Completion 33

Not so Effective Design Corvelle Drives Concepts to Completion 34

Not so Effective Design Corvelle Drives Concepts to Completion 34

Present and Practice Visualizations Present to Persuade Visual Critique Corvelle Drives Concepts to Completion

Present and Practice Visualizations Present to Persuade Visual Critique Corvelle Drives Concepts to Completion 35

Present to Persuade Presentation Tips q Show the chart and stop talking q Talk

Present to Persuade Presentation Tips q Show the chart and stop talking q Talk about the ideas in the chart q Guide the audience for unusual visual forms q Use reference charts q Turn off your chart when you have something important to say q Show something simple Corvelle Drives Concepts to Completion 36

Present to Persuade Engagement Tips q Create tension q Use time q Zoom in

Present to Persuade Engagement Tips q Create tension q Use time q Zoom in or out q Bait and switch q Deconstruct and reconstruct q Tell stories Corvelle Drives Concepts to Completion 37

Corvelle Drives Concepts to Completion 38

Corvelle Drives Concepts to Completion 38

Don’t Bore your Audience I still have 37 more slides to go! Corvelle Drives

Don’t Bore your Audience I still have 37 more slides to go! Corvelle Drives Concepts to Completion 39

Recommendations q Understand visualizations – Enhance your understanding of visualization q Create Visualizations –

Recommendations q Understand visualizations – Enhance your understanding of visualization q Create Visualizations – Experiment in the design of visualizations q Refine visualizations – Never be satisfied with the first version of a visualization q Present and practice visualizations – Invest time to practice the presentation Corvelle Drives Concepts to Completion 40

Questions & Discussion Please fill out evaluation form Can you help us create powerful

Questions & Discussion Please fill out evaluation form Can you help us create powerful data visualizations? Corvelle Drives Concepts to Completion 41

Creating Powerful Data Visualizations Yogi Schulz Corvelle Consulting Information technology related management consulting Microsoft

Creating Powerful Data Visualizations Yogi Schulz Corvelle Consulting Information technology related management consulting Microsoft Canada columnist & CBC Radio host Industry presenter Former PPDM Association board member Corvelle Drives Concepts to Completion Corvelle Consulting 300, 400 - 5 Ave. S. W. Calgary, Alberta T 2 P 0 L 6 Phone: (403) 860 -5348 E-mail: Yogi. Schulz@corvelle. com Web: www. corvelle. com 42

Corvelle Bibliography -1 q Analytics time has come, so learn how your business can

Corvelle Bibliography -1 q Analytics time has come, so learn how your business can unlock the value – http: //www. itworldcanada. com/blog/analytics-time-has-come-so-learn-how-your-businesscan-unlock-the-value/394348 q Analytics trends for 2016 – https: //www. corvelle. com/analytics-trends-for-2016/ q Big data is useless without visual analytics – http: //www. itworldcanada. com/blog/big-data-is-useless-without-visual-analytics/386943 q Business Intelligence – experiencing more hype than value? – http: //www. corvelle. com/business-intelligence-experiencing-more-hype-than-value/ q Business value of data modeling – http: //www. itworldcanada. com/blog/business-value-of-data-modeling/380574 q Can visual analytics be the savior of the oil and gas industry? – https: //www. corvelle. com/can-visual-analytics-be-the-savior-of-the-oil-and-gas-industry/ q Channeling the cynicism of BI practitioners – http: //www. itworldcanada. com/blog/channeling-the-cynicism-of-bi-practitioners/389882 Corvelle Drives Concepts to Completion 43

Corvelle Bibliography -2 q Do you need big data big results? – http: //www.

Corvelle Bibliography -2 q Do you need big data big results? – http: //www. corvelle. com/do-you-need-big-data-for-big-results/ q How Project Management is Shaping the Future Of Visual Analytics – https: //www. corvelle. com/how-project-management-is-shaping-the-future-of -visual-analytics/ q Is data modelling really dead? – http: //www. corvelle. com/is-data-modelling-really-dead/ q Is your company data-driven? – http: //www. itworldcanada. com/blog/is-your-company-data-driven/385732 q What data can’t be expected to do – http: //www. itworldcanada. com/blog/what-data-cant-be-expected-todo/389498 q Why you need visual analytics – http: //www. corvelle. com/resources/articles/it-world-canada/why-you-needvisual-analytics/ Corvelle Drives Concepts to Completion 44

Bibliography q A Good Example of Misleading Visualization – http: //spatial. ly/2009/09/a-good-example-of-misleading-visualization/ q A

Bibliography q A Good Example of Misleading Visualization – http: //spatial. ly/2009/09/a-good-example-of-misleading-visualization/ q A quick guide for better data visualizations – https: //www. tableau. com/good-to-great q The analysis of visual variables for use in the cartographic design of point symbols for mobile Augmented Reality applications – Łukasz Halik, Adam Mickiewicz University Poznan – http: //www. iag-aig. org/attach/30 dee 1 f 85 f 7 bd 479367 f 1 f 933 d 48 b 701/V 61 N 1_2 FT. pdf q The Benefits and Future of Data Visualization – Stat. Silk founder Frank van Cappelle – https: //www. statsilk. com/blog/benefits-and-future-data-visualization q Charting Statistics – Mary Eleanor Spear – https: //archive. org/details/Charting. Statistics q Color Brewer – http: //colorbrewer 2. org/#type=sequential&scheme=Bu. Gn&n=3 Corvelle Drives Concepts to Completion 45

Bibliography q Data: The language of modern business leaders – Steve Proctor, March 17,

Bibliography q Data: The language of modern business leaders – Steve Proctor, March 17, 2017 – http: //www. itbusiness. ca/sponsored/data-the-language-of-modern-business-leaders q Data Visualization: The Best Infographic Tools For 2017 – Bernard Marr, October 10, 2017 – https: //www. huffingtonpost. com/entry/data-visualization-the-best-infographic-toolsfor_us_59 ca 128 fe 4 b 0 f 2 df 5 e 83 b 134 q Data Visualization: The future of data visualization – Will Towler, January/February 2015 – http: //analytics-magazine. org/data-visualization-the-future-of-data-visualization q Data Visualization 101: How to Choose the Right Chart or Graph for Your Data – Jami Oetting – https: //blog. hubspot. com/marketing/types-of-graphs-for-data-visualization q Data-Driven Design: Dare to Wield the Sword of Data – Part I – Brent Dykes, December 4, 2012 – http: //www. analyticshero. com/2012/12/04/data-driven-design-dare-to-wield-the-sword-ofdata-part-i/ q Datavis. ca – http: //www. datavis. ca/index. php Corvelle Drives Concepts to Completion 46

Bibliography q Diverging color schemes: Showing good data isn't enough; you need to show

Bibliography q Diverging color schemes: Showing good data isn't enough; you need to show it well – Alberto Cairo, June 26, 2016 – http: //www. thefunctionalart. com/2016/06/diverging-color-schemes-showinggood_26. html q 8 Horrible Data Visualizations That Make No Sense – Eric Limer, September 02, 2013 – http: //gizmodo. com/8 -horrible-data-visualizations-that-make-no-sense 1228022038 q 55 Striking Data Visualization and Infographic Poster Designs – Igor Ovsyannykov, May 16, 2011 – http: //inspirationfeed. com/inspiration/infographics/55 -striking-datavisualization-and-infographic-poster-designs/ q 4 Tips for Promoting Predictive Analytics in Your Organization – Fern Halper, September 26, 2017 – https: //tdwi. org/articles/2017/09/26/ADV-ALL-4 -Tips-for-Promoting. Predictive-Analytics. aspx Corvelle Drives Concepts to Completion 47

Bibliography q The Future of Data Visualization, According to a Computer Science Professor –

Bibliography q The Future of Data Visualization, According to a Computer Science Professor – March 26, 2015 – https: //visage. co/the-future-of-data-visualization-according-to-a-computer-science-professor/ q Future of visualization – Jeffrey Heer, computer science professor and co-founder of Trifacta – https: //flowingdata. com/2015/03/23/future-of-visualization-2/ q Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations – Scott Berinato, 2016 – https: //hbr. org/product/good-charts-the-hbr-guide-to-making-smarter-more-persuasive-datavisualizations/15005 -PBK-ENG q Graphic Methods for Presenting Facts – Willard C. Brinton, 1914, First business book about visualization – http: //www. aviz. fr/wiki/uploads/Bertifier/brinton-graphic. Methods-1914. pdf q Grid lines: chart junk or visual aids? – Shilpi Choudhury, June 19, 2014 – https: //www. fusioncharts. com/charts/ – https: //www. fusioncharts. com/blog/grid-lines-chart-junk-or-visual-aids/ Corvelle Drives Concepts to Completion 48

Bibliography q Histomap: Visualizing the 4, 000 Year History of Global Power – Nick

Bibliography q Histomap: Visualizing the 4, 000 Year History of Global Power – Nick Routley, December 2, 2017 – https: //www. visualcapitalist. com/histomap/ q How The Rainbow Color Map Misleads – Robert Kosara, July 7, 2013 – https: //eagereyes. org/basics/rainbow-color-map q How To Choose The Right Chart Type For Your Data – Shafique, March 14, 2018 – https: //www. fusioncharts. com/blog/choose-right-chart-type-data/ q How to Lie With Data Visualization – http: //gizmodo. com/how-to-lie-with-data-visualization-1563576606 q How to Lie with Maps – Mark Monmonier – https: //www. amazon. com/How-Lie-Maps-2 nd-Edition/dp/0226534219 Corvelle Drives Concepts to Completion 49

Bibliography q Improving data visualisation for the public sector – http: //www. improving-visualisation. org/

Bibliography q Improving data visualisation for the public sector – http: //www. improving-visualisation. org/ q Infographics Lie. Here’s How To Spot The B. S. – Infographics are all over the place nowadays. How do you know which ones to trust? Follow these three easy steps to save yourself from getting duped. – https: //www. fastcodesign. com/3024273/infographics-lie-hereshow-to-spot-the-bs q Interactive Timeline of the most Iconic Infographics – https: //www. infowetrust. com/scroll/ q Jessica Hagy – jessicahagy. info – http: //thisisindexed. com/ Corvelle Drives Concepts to Completion 50

Bibliography q Lane Harrison – Assistant Professor, Department of Computer Science, Worcester Polytechnic Institute

Bibliography q Lane Harrison – Assistant Professor, Department of Computer Science, Worcester Polytechnic Institute – http: //web. cs. wpi. edu/~ltharrison/ – http: //codementum. org/ q A Leader's Guide to Storytelling with Data – Paul Andrew Smith, January 30, 2018 – https: //www. linkedin. com/pulse/leaders-guide-storytelling-data-paulandrew-smith/ q Misleading graph – https: //en. wikipedia. org/wiki/Misleading_graph q Misleading Language Maps on the Internet – Martin W. Lewis, July 10, 2012 – http: //www. geocurrents. info/cultural-geography/linguisticgeography/misleading-language-maps-on-the-internet Corvelle Drives Concepts to Completion 51

Bibliography q Misleading with pictures: The pitfalls of data visualization – Ian C. Campbell

Bibliography q Misleading with pictures: The pitfalls of data visualization – Ian C. Campbell – https: //figureoneblog. wordpress. com/2014/03/12/misleading-with-pictures-the-pitfalls-ofdata-visualization/ q Prioritize Which Data Skills Your Company Needs with This 2× 2 Matrix – Chris Littlewood, October 18, 2018 – https: //hbr. org/2018/10/prioritize-which-data-skills-your-company-needs-with-this-2 x 2 matrix q Publication-quality Graphing for Scientists and Engineers – http: //www. originlab. com/ q Remove Your Rose Tinted Glasses: Data Visualizations Designed to Mislead – Agata Kwapien in Data Visualization, December 2, 2015 – http: //www. datapine. com/blog/misleading-data-visualization-examples/ q Ronald Rensink – Associate Professor, Departments of Psychology and Computer Science, UBC – http: //psych. ubc. ca/persons/ronald-rensink/ q Spurious Correlations – http: //www. tylervigen. com/spurious-correlations Corvelle Drives Concepts to Completion 52

Bibliography q Tableau - Good enough to great – – q Teaching Students to

Bibliography q Tableau - Good enough to great – – q Teaching Students to Lie, Manipulate, and Mislead with Information Visualizations – – q Heiko Tröster in Data Visualization, August 23, 2016 http: //www. datapine. com/blog/top-12 -data-visualization-books/ 12 Websites & Blogs Every Data Analyst Should Follow – – q Lisa Dzera, April 19, 2016 https: //www. linkedin. com/pulse/10 -ways-create-powerful-infographics-data-lisa-dzera The 12 Data Visualization Books That Should Be on Your Bookshelf – – q Libby Hemphill, September 27, 2014 https: //www. slideshare. net/libbyh/teaching-students-to-lie-manipulate-and-mislead-with-informationvisualizations 10 Ways To Create Powerful Infographics & Data Visualizations – – q A quick guide for better data visualizations https: //www. tableau. com/good-to-great#KEDl. Q 3 d 1 SM 3 ZMm 3 t. 99 Gur Tirosh, October 23 rd, 2017 https: //www. sisense. com/blog/12 -websites-every-data-analyst-should-follow Understanding Uncertainty – – – Winton programme for the public understanding of risk Statistical Laboratory in the University of Cambridge https: //understandinguncertainty. org/ Corvelle Drives Concepts to Completion 53

Bibliography q Visual Design with Data – Seth Familian, Big Data Automation + Marketing

Bibliography q Visual Design with Data – Seth Familian, Big Data Automation + Marketing Strategist – https: //www. slideshare. net/sfamilian/visual-design-with-data-feb-2017/ q Visual Variables – http: //www. infovis-wiki. net/index. php? title=Visual_Variables q Visualizing Data – http: //www. visualisingdata. com/ q When Infographics Go Bad Or How Not To Design Data Visualization – http: //www. designyourway. net/blog/inspiration/when-infographics-go-bad-or-how-not-todesign-data-visualization/ q When Maps Lie – Andrew Wiseman, June 25, 2015 – http: //www. citylab. com/design/2015/06/when-maps-lie/396761/ q Why “Simple” Websites Are Scientifically Better – May 8, 2017 – https: //conversionxl. com/blog/why-simple-websites-are-scientifically-better/ Corvelle Drives Concepts to Completion 54

Corvelle Drives Concepts to Completion 55

Corvelle Drives Concepts to Completion 55

Map of Cholera Deaths Corvelle Drives Concepts to Completion 56

Map of Cholera Deaths Corvelle Drives Concepts to Completion 56