Andy Kirk Malofiej 20 Presentation

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Andy Kirk Malofiej 20 Presentation

  1. 1. My name is Andy Kirk
  2. 2. Visualisation Consultanthttp://www.quoteaustininsurance.com/images/Consultant.jpg
  3. 3. Visualisation Designerhttp://gizmodo.com/5792960/paul-allen-dishes-out-gossip-on-bill-gates-and-his-yacht-on-60-minutes
  4. 4. Visualisation Trainerhttp://cathybretag.blogspot.com/2010/10/first-time-out-reflection-on-my-first.html
  5. 5. Hebden Bridge London2.5 hours
  6. 6. Hebden Bridge2.5 mins London
  7. 7. Curse of KnowledgeAbsence of Knowledge
  8. 8. Surprise the novice,get the expert to nod Mirko Lorenz
  9. 9. Showcase of data visualisationtechniques for thriving in the age of big data
  10. 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. 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. 12. What is Big Data?Why does it matter to you?
  13. 13. Context Google Insights: “Infographic”http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
  14. 14. Context Google Insights: “Big Data”http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
  15. 15. Context Google Insights: “Big Data”http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
  16. 16. We are capturing, creating and mobilisingunbelievable amounts of data at an unbelievable rate. And it is increasing.
  17. 17. Volume Variety Velocityhttp://radar.oreilly.com/2012/01/what-is-big-data.html
  18. 18. Yahoo! C.O.R.E. Data Visualization | Periscopic http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/
  19. 19. http://www.zimbio.com/Ted+Danson/articles/13/TV+DVD+Cheers+Final+Season+4+DVD+Set
  20. 20. Running the Numbers II: Portraits of global mass culture | Chris Jordan http://www.chrisjordan.com/gallery/rtn2/#gyre2
  21. 21. Running the Numbers II: Portraits of global mass culture | Chris Jordan http://www.chrisjordan.com/gallery/rtn2/#gyre2
  22. 22. Visualisation should berecognised as a discovery tool. Manuel Lima http://www.visualcomplexity.com/vc/blog/?p=644
  23. 23. Peer review wars | Nigel Hawtinhttp://www.flickr.com/photos/nhawtin/5243787538/in/photostream/lightbox/
  24. 24. http://starwarsaficionado.blogspot.com/2011/12/classic-image-its-worse.html
  25. 25. http://v2.centralstory.com/about/squiggle/
  26. 26. 1. Be clear about the visualisation’s purpose and parameters
  27. 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. 28. Jet Tracker | Wall Street Journal http://projects.wsj.com/jettracker/
  29. 29. Jet Tracker | Wall Street Journal http://projects.wsj.com/jettracker/
  30. 30. Jet Tracker | Wall Street Journal http://projects.wsj.com/jettracker/
  31. 31. So many parameters!
  32. 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. 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. 34. 2. Identify and develop questions about the problem context
  35. 35. What questions are you hoping toanswer through this visualisation?What stories should users/readers be able to derive from this visualisation?
  36. 36. Just Landed | Jer Thorphttp://blog.blprnt.com/blog/blprnt/just-landed-processing-twitter-metacarta-hidden-data
  37. 37. 3. Acquire, prepare and exploreyour data to begin familiarisation
  38. 38. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation System download API Web scrape Scanned documents
  39. 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. 40. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation Missing values Erroneous values Duplicates Uncommon characters Freak outliers?
  41. 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. 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. 43. Yahoo! Mail Data Visualization | Periscopichttp://www.flickr.com/photos/visualizeyahoo/sets/72157627722660160/with/6235510547/
  44. 44. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation What other data do I need?
  45. 45. The United States of 2012 for Esquire Magazine | Stamen http://content.stamen.com/united_states_of_2012
  46. 46. Transforming Transforming Pre-prod.Acquisition Examination Consolidating for quality for purpose visualisation Patterns Relationships Range and distribution Outliers
  47. 47. World Nuclear Reactor Sites | Nigel Hawtin/Peter Aldhoushttp://public.tableausoftware.com/views/WorldNuclearReactorSites2/NorthAmerica?:embed=y
  48. 48. 4. Conceive your visualisation design solution
  49. 49. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  50. 50. http://www.informationisbeautifulawards.com/2011/10/napkin-shortlist-for-the-1st-challenge/
  51. 51. 138 years of popular science | Jer Thorp and Mark Hansen http://www.flickr.com/photos/blprnt/6281316931/sizes/o/in/photostream/
  52. 52. My working process is riddledwith dead-ends, messy errors and bad decisions JerThorp
  53. 53. 138 years of popular science | Jer Thorp and Mark Hansen http://blog.blprnt.com/blog/blprnt/138-years-of-popular-science
  54. 54. 138 years of popular science | Jer Thorp and Mark Hansen http://blog.blprnt.com/blog/blprnt/138-years-of-popular-science
  55. 55. Space Junk | Jen Christiansen and Jan Willem Tulp Scientific American, April 2012
  56. 56. Space Junk | Jen Christiansen and Jan Willem Tulp Scientific American, April 2012
  57. 57. Space Junk | Jen Christiansen and Jan Willem Tulp Scientific American, April 2012
  58. 58. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  59. 59. http://projects.nytimes.com/census/2010/explorer
  60. 60. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  61. 61. Posted: Visualizing US expansion through post offices | Derek Watkins http://derekwatkins.wordpress.com/2011/08/06/posted/
  62. 62. Posted: Visualizing US expansion through post offices | Derek Watkins http://derekwatkins.wordpress.com/2011/08/06/posted/
  63. 63. Posted: Visualizing US expansion through post offices | Derek Watkins http://derekwatkins.wordpress.com/2011/08/06/posted/
  64. 64. Max Planck Research Networks | Moritz Stefaner and Christopher Warnow http://max-planck-research-networks.net/
  65. 65. Max Planck Research Networks | Moritz Stefaner and Christopher Warnow http://max-planck-research-networks.net/
  66. 66. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  67. 67. Data Theft | Jen Christiansen Scientific American, October 2011
  68. 68. The 5 layers of a visualisation...Data representationColour and backgroundAnimation and interactionLayout, placement and apparatusThe annotation layer
  69. 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. 70. http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  71. 71. http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  72. 72. 5. Construct, launch and evaluate your visualisation solution
  73. 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. 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. 75. Sense of Patterns | Mahir M. Yavuz http://casualdata.com/senseofpatterns/
  76. 76. Sense of Patterns | Mahir M. Yavuzhttp://www.visualizing.org/full-screen/32596/embedlaunch
  77. 77. Sense of Patterns | Mahir M. Yavuzhttp://www.visualizing.org/full-screen/32596/embedlaunch
  78. 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. 79. www.visualisingdata.comandy@visualisingdata.com @visualisingdata

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