The hype around Web 2.0 continues to increase to the point of absurdity. We hear all about a rich web of data, but what can we learn from these trends to actually apply to our designs? You&#8217;ll take a tour through the past, present, and future of the web to answer these questions and more: <ul> <li>What can we learn from the rich history of data visualization to inform our designs today?</li> <li>How can we do amazing work while battle the constant constraints we find ourselves up against?</li> <li>How do we <em>really</em> incorporate users into our practice of user experience? </li> </ul>

1. veen: future

11. 1974

22. $100,000 per Gigabyte

24. Tools for Scale of
participation data

25. 4.35 3.17 3.06 1.37 0.19 0.11 0.03 0.05 0.20 1.22 2.86 3.09
5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 3.42
2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83
4.35 3.17 3.06 1.37 0.19 0.11 0.03 0.05 0.20 1.22 2.86 3.09
5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 3.42
2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83
4.35 3.17 3.06 1.37 0.19 0.11 0.03 0.05 0.20 1.22 2.86 3.09
5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 3.42
2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83

26. 4.35 3.17 3.06 1.37 0.19 0.11 0.03 0.05 0.20 1.22 2.86 3.09
5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 3.42
2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83

27. AVERAGE RAINFALL (INCHES/MONTH)
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
San Fran 4.35 3.17 3.06 1.37 0.19 0.11 0.03 0.05 0.20 1.22 2.86 3.09
Seattle 5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
Chicago 1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
New York 3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 3.42
Miami 2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83

28. Average Rainfall (inches/month)
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
San Francisco 4.35 3.17 3.06 1.37 0.19 0.03 0.06 0.05 0.20 1.22 2.86 3.09
Seattle 5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
Chicago 1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
New York 3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 4.42
Miami 2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83

29. Average Rainfall (inches/month)
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
San Francisco 4.35 3.17 3.06 1.37 0.19 0.03 0.06 0.05 0.20 1.22 2.86 3.09
Seattle 5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
Chicago 1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
New York 3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 4.42
Miami 2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83

30. Average Rainfall (inches/month)
4+ 2-3 1-2 <1
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
San Francisco
Seattle
Chicago
New York City
Miami

33. Percentage of chart that looks like PacMan

34. Average Rainfall (inches/month)
4+ 2-3 1-2 <1
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
San Francisco
Seattle
Chicago
New York City
Miami

36. John Snow

43. Charles Joseph Minard

44. quot; The aim of my carte figurative is ...
to convey promptly to the eye the
relation not given quickly by numbers
requiring mental calculation.quot;

45. Don’t make me think

48. Harry Beck
Harry Beck

54. Choose date: today | Last 7 days | Last 30 days

59. $2,000,353,186.72 in pennies

65. Megapenny.com

66. Chris Jordon

69. Two million plastic bottles
used in the US every five minutes

70. “Statistics can feel abstract and
anesthetizing, making it difficult to
connect with and make meaning.”
chrisjordon.com

71. Find a story in the data

72. Assign different visual cues to
each dimension of the data

73. Remove everything that
isn't telling the story

81. control

82. Average Rainfall (inches/month)
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
San Francisco 4.35 3.17 3.06 1.37 0.19 0.03 0.06 0.05 0.20 1.22 2.86 3.09
Seattle 5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
Chicago 1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
New York 3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 4.42
Miami 2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83

83. Average Rainfall (inches/month)
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
San Francisco 4.35 3.17 3.06 1.37 0.19 0.03 0.06 0.05 0.20 1.22 2.86 3.09
Seattle 5.35 4.03 3.77 2.51 1.84 1.59 0.85 1.22 1.94 3.25 5.65 6.00
Chicago 1.53 1.36 2.69 3.64 3.32 3.78 3.66 4.22 3.82 2.41 2.92 2.47
New York 3.17 3.02 3.59 3.90 3.80 3.65 3.80 3.41 3.30 2.88 3.65 4.42
Miami 2.01 2.08 2.39 2.85 6.21 9.33 5.70 7.58 7.63 5.64 2.66 1.83

84. Average Rainfall (inches/month)
4+ 2-3 1-2 <1
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
San Francisco
Seattle
Chicago
New York City
Miami

85. Average Rainfall (inches/month) Choose cities...
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
Seattle
Chicago
New York
San Francisco
Miami

86. Enable people to find their stories

92. Create tools to let them
manipulate their data

98. Provide filters to enable clarity

101. Account
Overview
2.0.0 Traffic
1.0.0 Visitor 3.0.0 Content
Sources 4.0.0 Goals
Overview Overview
Overview
# of Visitors new and
Top Content % of Goals Completed
retruning % visitors came
2.1.0 Direct Traffic
N directly Overview
Aver. Length of
N # of visitors N Visit Goal Conversion? 4.1.0 Goal
Total Direct Trafic
N Pages/Visit N Aver. Depth of Visit
N Total Completion
1.2.0 Total # of visitors that
N New vs. Retruning Pageviews
N came directly N Aver. Time on Page
4.2.0 Rev
N Conversion %
Goals
S Direct Visitors
Aver. N Total Value
Funnel Visua
Pageviews/ 1.1.0 Average PV/ % Bounce
Visite visit
T Top Content N Average Value
Pageviews Pages/Visits
# of visits new 1.3.0 New vs. Avg. Time on Site
Uniq. Views
N Abandoned Funnel
and returning
returning Pageviews
% First Visit To Site 4.3.0 Funne
Loyality 3.2.0 Average Time
Ave. Time on Page
% Goal 1
Recency 1.4.0 Loyalty % Exit Funnel Visua
% Goal 2
$ Index Make quot;Entranc
Segmentation quot;Exitquot; numbers
% Goal 3 3.1.0 Content 3.1.3 Site Overlay
until asked for
1.5.0 Recency Detail Detail
User Defined Keyword user.
% Goal 4 Entrance &
T Bounce
Content Campaign # of Transactions Total Visitors for
Uniq. Views Page
Total Revenue
Pageviews 3.3.0 Average
Design Criteria N # of Visitors
Bounce Rate
# of Products
Source
Ave. Time
Medium]
Campaign Aver. Time on
Page
% Exit
2.2.0 Referring 2.2.1 Referring 3.4.1 Percent who
Keyword Content % visitors came 2.2.2 Link Detail Aver. Bounce
started
N from other links Sources Source Detail $ Index
% who started
Country Region
Visitors from
Total Referral Trafic Visitors from Link
City
Network Source T Exit
Traffic Source 3.1.1 All Navigation
3.1.2 Initial
Location Navigation
Initial
Browser N # of sources N # of visitors N # of visitors Uniq. Views
T Referring source T
All Navigation
T Navigation
Language (came from)
popularity ranking popularity ranking (Starting Page)
N # of visitors N and % of visitors N and % of visitors
Pageviews
Visits Visits Visits
Connection
Platform
Speed iff source has one link points to content
T Referring sources referral: N page
Ave. Time
Goal/Visit Goal/Visit Goal/Visit
Screen
points to content % Exit
Resolution Colors
Source Domain Name N page S
Visitors from this
source
T/Visit T/Visit T/Visit
else: $ Index
Java Visits % Bounce $/Visit $/Visit $/Visit
Flash
Source's Link
T Referrals
Bounce Rate Pages/Visits
URL
Pages/Visits Avg. Time on Site
Visits
Time on Site % First Visit To Site
Bounce Rate
First Visit To Site % Goal 1
Pages/Visits Overlay
Goal 1 % Goal 2
Time on Site
Goal 2 % Goal 3
First Visit To Site
Goal 3 % Goal 4
Goal 1
Goal 4 # of Transactions
Goal 2
Transactions Total Revenue
Goal 3
Revenue # of Products
Goal 4
link to geo map for this
segment
Transactions
link to language list for
this segment
Revenue
Visitors from this
S source
% Bounce
Pages/Visits
Avg. Time on Site
% First Visit To Site
% Goal 1
% Goal 2
% Goal 3
% Goal 4
# of Transactions
Total Revenue
# of Products
link to geo map for this
segment
link to language list for
this segment

103. Storytelling Discovery
Visual cues Interactivity
Editing Filtering

104. Tools for Scale of
participation data

105. weathr BETA
Average Rainfall (inches/month) Choose cities...
Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec
Seattle
Chicago
New York
San Francisco
Miami

118. “Everything I’ve built
has come from the
frustration that it
didn’t yet exist.”

119. “New ideas come
from your heart,
not your wallet”

120. “When you start
looking at a problem
and it seems really
simple with all these
simple solutions, you
don’t really understand
the complexity of the
problem. And your
solutions are way too
oversimplified, and
they don’t work.”

121. “…Then you get into
the problem, and you
see it’s really comp-
licated. And you come
up with all these
convoluted solutions.
That’s sort of the
middle, and that’s
where most people
stop, and the solutions
tend to work for a
while…”

122. “…But the really great
person will keep on
going and find the
key, underlying
principle of the
problem, and come
up with a beautiful
elegant solution that
works.”
—Steve Jobs

132. Gotham

137. thank you
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