veen: future
1974
$100,000 per Gigabyte
Tools for       Scale of
participation      data
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 ...
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 ...
AVERAGE RAINFALL (INCHES/MONTH)

           JAN    FEB    MAR    APR    MAY    JUN    JUL    AUG    SEP    OCT    NOV    D...
Average Rainfall (inches/month)
                Jan    Feb    Mar    Apr    May    June   July   Aug    Sep    Oct    Nov ...
Average Rainfall (inches/month)
                Jan    Feb    Mar    Apr    May    June   July   Aug    Sep    Oct    Nov ...
Average Rainfall (inches/month)
                                                                      4+    2-3   1-2   <1...
Percentage of chart that looks like PacMan
Average Rainfall (inches/month)
                                                                      4+    2-3   1-2   <1...
John Snow
Charles Joseph Minard
quot; The aim of my carte figurative is ...
  to convey promptly to the eye the
  relation not given quickly by numbers
  ...
Don’t make me think
Harry Beck
Harry Beck
Choose date: today | Last 7 days | Last 30 days
$2,000,353,186.72 in pennies
Megapenny.com
Chris Jordon
Two million plastic bottles
used in the US every five minutes
“Statistics can feel abstract and
 anesthetizing, making it difficult to
 connect with and make meaning.”




            ...
Find a story in the data
Assign different visual cues to
 each dimension of the data
Remove everything that
 isn't telling the story
control
Average Rainfall (inches/month)
                Jan    Feb    Mar    Apr    May    June   July   Aug    Sep    Oct    Nov ...
Average Rainfall (inches/month)
                Jan    Feb    Mar    Apr    May    June   July   Aug    Sep    Oct    Nov ...
Average Rainfall (inches/month)
                                                                      4+    2-3   1-2   <1...
Average Rainfall (inches/month)                                Choose cities...



 Jan   Feb   Mar   Apr   May   June   J...
Enable people to find their stories
Create tools to let them
 manipulate their data
Provide filters to enable clarity
Account
          Overview




                                                            2.0.0 Traffic
          1.0.0 V...
Storytelling   Discovery


Visual cues    Interactivity


Editing        Filtering
Tools for       Scale of
participation      data
weathr             BETA
Average Rainfall (inches/month)                                 Choose cities...



 Jan   Feb   M...
“Everything I’ve built
 has come from the
 frustration that it
 didn’t yet exist.”
“New ideas come
 from your heart,
 not your wallet”
“When you start
looking at a problem
and it seems really
simple with all these
simple solutions, you
don’t really understa...
“…Then you get into
the problem, and you
see it’s really comp-
licated. And you come
up with all these
convoluted solution...
“…But the really great
person will keep on
going and find the
key, underlying
principle of the
problem, and come
up with a...
Gotham
thank you




 1 3 4
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
Jeffrey Veen - Designing our way through data
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Jeffrey Veen - Designing our way through data

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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>

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Jeffrey Veen - Designing our way through data

  1. 1. veen: future
  2. 2. 1974
  3. 3. $100,000 per Gigabyte
  4. 4. Tools for Scale of participation data
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. Percentage of chart that looks like PacMan
  12. 12. 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
  13. 13. John Snow
  14. 14. Charles Joseph Minard
  15. 15. 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;
  16. 16. Don’t make me think
  17. 17. Harry Beck Harry Beck
  18. 18. Choose date: today | Last 7 days | Last 30 days
  19. 19. $2,000,353,186.72 in pennies
  20. 20. Megapenny.com
  21. 21. Chris Jordon
  22. 22. Two million plastic bottles used in the US every five minutes
  23. 23. “Statistics can feel abstract and anesthetizing, making it difficult to connect with and make meaning.” chrisjordon.com
  24. 24. Find a story in the data
  25. 25. Assign different visual cues to each dimension of the data
  26. 26. Remove everything that isn't telling the story
  27. 27. control
  28. 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. 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. 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
  31. 31. 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
  32. 32. Enable people to find their stories
  33. 33. Create tools to let them manipulate their data
  34. 34. Provide filters to enable clarity
  35. 35. 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
  36. 36. Storytelling Discovery Visual cues Interactivity Editing Filtering
  37. 37. Tools for Scale of participation data
  38. 38. 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
  39. 39. “Everything I’ve built has come from the frustration that it didn’t yet exist.”
  40. 40. “New ideas come from your heart, not your wallet”
  41. 41. “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.”
  42. 42. “…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…”
  43. 43. “…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
  44. 44. Gotham
  45. 45. thank you 1 3 4

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