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  • 1. My name is Andy Kirk
  • 2. Visualisation Consultanthttp://www.quoteaustininsurance.com/images/Consultant.jpg
  • 3. Visualisation Designerhttp://gizmodo.com/5792960/paul-allen-dishes-out-gossip-on-bill-gates-and-his-yacht-on-60-minutes
  • 4. Visualisation Trainerhttp://cathybretag.blogspot.com/2010/10/first-time-out-reflection-on-my-first.html
  • 5. Hebden Bridge London2.5 hours
  • 6. Hebden Bridge2.5 mins London
  • 7. Curse of KnowledgeAbsence of Knowledge
  • 8. Surprise the novice,get the expert to nod Mirko Lorenz
  • 9. Showcase of data visualisationtechniques for thriving in the age of big data
  • 10. Showcase of data visualisation techniques for thriving in the age of data that has thousands ofrecords and is quite complex and makes life difficult
  • 11. It’s not a technology problem; it’s a people problem. Aron Pilhofer (on data journalism) Editor of Interactive News, New York Times
  • 12. What is Big Data?Why does it matter to you?
  • 13. Context Google Insights: “Infographic”http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
  • 14. Context Google Insights: “Big Data”http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
  • 15. Context Google Insights: “Big Data”http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
  • 16. We are capturing, creating and mobilisingunbelievable amounts of data at an unbelievable rate. And it is increasing.
  • 17. Volume Variety Velocityhttp://radar.oreilly.com/2012/01/what-is-big-data.html
  • 18. Yahoo! C.O.R.E. Data Visualization | Periscopic http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/
  • 19. http://www.zimbio.com/Ted+Danson/articles/13/TV+DVD+Cheers+Final+Season+4+DVD+Set
  • 20. Running the Numbers II: Portraits of global mass culture | Chris Jordan http://www.chrisjordan.com/gallery/rtn2/#gyre2
  • 21. Running the Numbers II: Portraits of global mass culture | Chris Jordan http://www.chrisjordan.com/gallery/rtn2/#gyre2
  • 22. Visualisation should berecognised as a discovery tool. Manuel Lima http://www.visualcomplexity.com/vc/blog/?p=644
  • 23. Peer review wars | Nigel Hawtinhttp://www.flickr.com/photos/nhawtin/5243787538/in/photostream/lightbox/
  • 24. http://starwarsaficionado.blogspot.com/2011/12/classic-image-its-worse.html
  • 25. http://v2.centralstory.com/about/squiggle/
  • 26. 1. Be clear about the visualisation’s purpose and parameters
  • 27. EXPLORE: facilitate reasoning of data EXPLAIN: convey information to others Analysis Monitor/Signals Familiarise with data Answer questions/Inform Support graphical calculation Learn/Increase knowledge Find patterns/Find no patterns Contextualise data Discover questions Present arguments Interact Assist with decisions Shape opinion/Persuade Emphasize issues Tell a story Inspire Shock/Make an impact Enlighten Change behaviour Entertain/fun Art/Aesthetic pleasure
  • 28. Jet Tracker | Wall Street Journal http://projects.wsj.com/jettracker/
  • 29. Jet Tracker | Wall Street Journal http://projects.wsj.com/jettracker/
  • 30. Jet Tracker | Wall Street Journal http://projects.wsj.com/jettracker/
  • 31. So many parameters!
  • 32. Brief? Open, strict, helpful, unhelpfulFormat? Static, interactive, videoPressures? Timescales, editorialAudience size? One, group, wwwAudience type? Domain experts, generalResolution? Headlines, clusters, look-upRules? Structure, layout, style, colourCapabilities? Design, technical, technologyPeople? Individual, team, collaboration
  • 33. Analyst Politician Computer scientist Journalist Researcher Designer Cognitive scientist http://www.jasonnazar.com/2008/09/23/10-lessons-startups-can-learn-from-superheros/
  • 34. 2. Identify and develop questions about the problem context
  • 35. What questions are you hoping toanswer through this visualisation?What stories should users/readers be able to derive from this visualisation?
  • 36. Just Landed | Jer Thorphttp://blog.blprnt.com/blog/blprnt/just-landed-processing-twitter-metacarta-hidden-data
  • 37. 3. Acquire, prepare and exploreyour data to begin familiarisation
  • 38. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation System download API Web scrape Scanned documents
  • 39. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation Is it fit for purpose? Is it complete? Identify data types
  • 40. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation Missing values Erroneous values Duplicates Uncommon characters Freak outliers?
  • 41. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation Parsing Merging Normalisation Conversion eg. Codify free-text Inspired by Kim Rees’ talk at 2011 Wolfram Summit - http://www.wolframdatasummit.org/2011/attendee/presentations/Rees.pptx
  • 42. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation Full resolution Filter/Exclude (records & variables) Aggregate/Roll-up Sample Statistics Inspired by Kim Rees’ talk at 2011 Wolfram Summit - http://www.wolframdatasummit.org/2011/attendee/presentations/Rees.pptx
  • 43. Yahoo! Mail Data Visualization | Periscopichttp://www.flickr.com/photos/visualizeyahoo/sets/72157627722660160/with/6235510547/
  • 44. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation What other data do I need?
  • 45. The United States of 2012 for Esquire Magazine | Stamen http://content.stamen.com/united_states_of_2012
  • 46. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation Patterns Relationships Range and distribution Outliers
  • 47. World Nuclear Reactor Sites | Nigel Hawtin/Peter Aldhoushttp://public.tableausoftware.com/views/WorldNuclearReactorSites2/NorthAmerica?:embed=y
  • 48. 4. Conceive your visualisation design solution
  • 49. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  • 50. http://www.informationisbeautifulawards.com/2011/10/napkin-shortlist-for-the-1st-challenge/
  • 51. 138 years of popular science | Jer Thorp and Mark Hansen http://www.flickr.com/photos/blprnt/6281316931/sizes/o/in/photostream/
  • 52. My working process is riddledwith dead-ends, messy errors and bad decisions JerThorp
  • 53. 138 years of popular science | Jer Thorp and Mark Hansen http://blog.blprnt.com/blog/blprnt/138-years-of-popular-science
  • 54. 138 years of popular science | Jer Thorp and Mark Hansen http://blog.blprnt.com/blog/blprnt/138-years-of-popular-science
  • 55. Space Junk | Jen Christiansen and Jan Willem Tulp Scientific American, April 2012
  • 56. Space Junk | Jen Christiansen and Jan Willem Tulp Scientific American, April 2012
  • 57. Space Junk | Jen Christiansen and Jan Willem Tulp Scientific American, April 2012
  • 58. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  • 59. http://projects.nytimes.com/census/2010/explorer
  • 60. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  • 61. Posted: Visualizing US expansion through post offices | Derek Watkins http://derekwatkins.wordpress.com/2011/08/06/posted/
  • 62. Posted: Visualizing US expansion through post offices | Derek Watkins http://derekwatkins.wordpress.com/2011/08/06/posted/
  • 63. Posted: Visualizing US expansion through post offices | Derek Watkins http://derekwatkins.wordpress.com/2011/08/06/posted/
  • 64. Max Planck Research Networks | Moritz Stefaner and Christopher Warnow http://max-planck-research-networks.net/
  • 65. Max Planck Research Networks | Moritz Stefaner and Christopher Warnow http://max-planck-research-networks.net/
  • 66. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  • 67. Data Theft | Jen Christiansen Scientific American, October 2011
  • 68. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  • 69. The annotation layer is themost important thing we do... Otherwise it’s a case of here it is, you go figure it out. Amanda Cox Graphics Editor, New York Times http://eyeofestival.com/speaker/amanda-cox/
  • 70. http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  • 71. http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  • 72. 5. Construct, launch and evaluate your visualisation solution
  • 73. ...you’ve started playing with thevisualization instead of debugging ... you hit some level of engagement and it becomes really interesting Martin Wattenberg, "Big Picture" data visualization group, Google http://queue.acm.org/detail.cfm?id=1744741
  • 74. You know you’ve achievedperfection in design, not when you have nothing more to add, but when you have nothing more to take away Antoine de Saint-Exupery
  • 75. Sense of Patterns | Mahir M. Yavuz http://casualdata.com/senseofpatterns/
  • 76. Sense of Patterns | Mahir M. Yavuzhttp://www.visualizing.org/full-screen/32596/embedlaunch
  • 77. Sense of Patterns | Mahir M. Yavuzhttp://www.visualizing.org/full-screen/32596/embedlaunch
  • 78. Thank you to… Nigel Hawtin Jen Christiansen Moritz Stefaner Alberto Cairo Sarah Slobin Derek Watkins Kim Rees Mahir M Yavuz Jer Thorp Stamen
  • 79. www.visualisingdata.comandy@visualisingdata.com @visualisingdata