1. My name is Andy Kirk

2. Visualisation
Consultant
http://www.quoteaustininsurance.com/images/Consultant.jpg

3. Visualisation
Designer
http://gizmodo.com/5792960/paul-allen-dishes-out-gossip-on-bill-gates-and-his-yacht-on-60-minutes

4. Visualisation
Trainer
http://cathybretag.blogspot.com/2010/10/first-time-out-reflection-on-my-first.html

6. Hebden Bridge
London
2.5 hours

7. Hebden Bridge
2.5 mins
London

8. Curse of Knowledge
Absence of Knowledge

9. Surprise the novice,
get the expert to nod
Mirko Lorenz

10. Showcase of
data visualisation
techniques for thriving
in the age of
big data

11. Showcase of
data visualisation
techniques for thriving
in the age of
data that has thousands of
records and is quite complex
and makes life difficult

12. It’s not a technology problem;
it’s a people problem.
Aron Pilhofer (on data journalism)
Editor of Interactive News, New York Times

14. What is Big Data?
Why does it matter to you?

15. Context
Google Insights: “Infographic”
http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q

16. Context
Google Insights: “Big Data”
http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q

17. Context
Google Insights: “Big Data”
http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q

18. We are capturing,
creating and mobilising
unbelievable amounts of data
at an unbelievable rate.
And it is increasing.

21. Volume
Variety
Velocity
http://radar.oreilly.com/2012/01/what-is-big-data.html

22. Yahoo! C.O.R.E. Data Visualization | Periscopic
http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/

24. http://www.zimbio.com/Ted+Danson/articles/13/TV+DVD+Cheers+Final+Season+4+DVD+Set

25. Running the Numbers II: Portraits of global mass culture | Chris Jordan
http://www.chrisjordan.com/gallery/rtn2/#gyre2

26. Running the Numbers II: Portraits of global mass culture | Chris Jordan
http://www.chrisjordan.com/gallery/rtn2/#gyre2

27. Visualisation should be
recognised as a discovery tool.
Manuel Lima
http://www.visualcomplexity.com/vc/blog/?p=644

29. Peer review wars | Nigel Hawtin
http://www.flickr.com/photos/nhawtin/5243787538/in/photostream/lightbox/

31. http://starwarsaficionado.blogspot.com/2011/12/classic-image-its-worse.html

32. http://v2.centralstory.com/about/squiggle/

33. 1. Be clear about the visualisation’s
purpose and parameters

34. 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

35. Jet Tracker | Wall Street Journal
http://projects.wsj.com/jettracker/

36. Jet Tracker | Wall Street Journal
http://projects.wsj.com/jettracker/

37. Jet Tracker | Wall Street Journal
http://projects.wsj.com/jettracker/

38. So many parameters!

39. Brief? Open, strict, helpful, unhelpful
Format? Static, interactive, video
Pressures? Timescales, editorial
Audience size? One, group, www
Audience type? Domain experts, general
Resolution? Headlines, clusters, look-up
Rules? Structure, layout, style, colour
Capabilities? Design, technical, technology
People? Individual, team, collaboration

40. Analyst Politician Computer scientist Journalist
Researcher Designer Cognitive scientist
http://www.jasonnazar.com/2008/09/23/10-lessons-startups-can-learn-from-superheros/

41. 2. Identify and develop questions
about the problem context

42. What questions are you hoping to
answer through this visualisation?
What stories should users/readers
be able to derive from this
visualisation?

43. Just Landed | Jer Thorp
http://blog.blprnt.com/blog/blprnt/just-landed-processing-twitter-metacarta-hidden-data

44. 3. Acquire, prepare and explore
your data to begin familiarisation

45. Transforming Transforming Pre-prod.
Acquisition Examination Consolidating
for quality for purpose visualisation
System download
API
Web scrape
Scanned documents

46. Transforming Transforming Pre-prod.
Acquisition Examination Consolidating
for quality for purpose visualisation
Is it fit for purpose?
Is it complete?
Identify data types

47. Transforming Transforming Pre-prod.
Acquisition Examination Consolidating
for quality for purpose visualisation
Missing values
Erroneous values
Duplicates
Uncommon characters
Freak outliers?

48. 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

49. 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

50. Yahoo! Mail Data Visualization | Periscopic
http://www.flickr.com/photos/visualizeyahoo/sets/72157627722660160/with/6235510547/

51. Transforming Transforming Pre-prod.
Acquisition Examination Consolidating
for quality for purpose visualisation
What other data do I need?

52. The United States of 2012 for Esquire Magazine | Stamen
http://content.stamen.com/united_states_of_2012

53. Transforming Transforming Pre-prod.
Acquisition Examination Consolidating
for quality for purpose visualisation
Patterns
Relationships
Range and distribution
Outliers

54. World Nuclear Reactor Sites | Nigel Hawtin/Peter Aldhous
http://public.tableausoftware.com/views/WorldNuclearReactorSites2/NorthAmerica?:embed=y

55. 4. Conceive your visualisation
design solution

56. The 5 layers of a visualisation...
Data representation
Colour and background
Animation and interaction
Layout, placement and apparatus
The annotation layer

57. http://www.informationisbeautifulawards.com/2011/10/napkin-shortlist-for-the-1st-challenge/

58. 138 years of popular science | Jer Thorp and Mark Hansen
http://www.flickr.com/photos/blprnt/6281316931/sizes/o/in/photostream/

59. My working process is riddled
with dead-ends, messy errors
and bad decisions
JerThorp

60. 138 years of popular science | Jer Thorp and Mark Hansen
http://blog.blprnt.com/blog/blprnt/138-years-of-popular-science

61. 138 years of popular science | Jer Thorp and Mark Hansen
http://blog.blprnt.com/blog/blprnt/138-years-of-popular-science

62. Space Junk | Jen Christiansen and Jan Willem Tulp
Scientific American, April 2012

63. Space Junk | Jen Christiansen and Jan Willem Tulp
Scientific American, April 2012

64. Space Junk | Jen Christiansen and Jan Willem Tulp
Scientific American, April 2012

65. The 5 layers of a visualisation...
Data representation
Colour and background
Animation and interaction
Layout, placement and apparatus
The annotation layer

66. http://projects.nytimes.com/census/2010/explorer

67. The 5 layers of a visualisation...
Data representation
Colour and background
Animation and interaction
Layout, placement and apparatus
The annotation layer

68. Posted: Visualizing US expansion through post offices | Derek Watkins
http://derekwatkins.wordpress.com/2011/08/06/posted/

69. Posted: Visualizing US expansion through post offices | Derek Watkins
http://derekwatkins.wordpress.com/2011/08/06/posted/

70. Posted: Visualizing US expansion through post offices | Derek Watkins
http://derekwatkins.wordpress.com/2011/08/06/posted/

71. Max Planck Research Networks | Moritz Stefaner and Christopher Warnow
http://max-planck-research-networks.net/

72. Max Planck Research Networks | Moritz Stefaner and Christopher Warnow
http://max-planck-research-networks.net/

73. The 5 layers of a visualisation...
Data representation
Colour and background
Animation and interaction
Layout, placement and apparatus
The annotation layer

74. Data Theft | Jen Christiansen
Scientific American, October 2011

75. The 5 layers of a visualisation...
Data representation
Colour and background
Animation and interaction
Layout, placement and apparatus
The annotation layer

76. The annotation layer is the
most 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/

77. http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers

78. http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers

79. 5. Construct, launch and evaluate
your visualisation solution

82. ...you’ve started playing with the
visualization 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

83. You know you’ve achieved
perfection in design, not when you
have nothing more to add,
but when you have
nothing more to take away
Antoine de Saint-Exupery

84. Sense of Patterns | Mahir M. Yavuz
http://casualdata.com/senseofpatterns/

85. Sense of Patterns | Mahir M. Yavuz
http://www.visualizing.org/full-screen/32596/embedlaunch

86. Sense of Patterns | Mahir M. Yavuz
http://www.visualizing.org/full-screen/32596/embedlaunch

88. Thank you to…
Nigel Hawtin
Jen Christiansen
Moritz Stefaner
Alberto Cairo
Sarah Slobin
Derek Watkins
Kim Rees
Mahir M Yavuz
Jer Thorp
Stamen

89. www.visualisingdata.com
andy@visualisingdata.com
@visualisingdata