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Data Viz for Products

Data Viz for Products

Presented at ProductCamp Boston 2016

C. Todd Lombardo

April 09, 2016
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  1. D ATA V I S U A L I Z

    AT I O N I N P R O D U C T P R O D U C T C A M P B O S T O N 2 0 1 6 C T O D D L O M B A R D O C H I E F D E S I G N S T R A T E G I S T - F R E S H T I L L E D S O I L @ I A M C T O D D
  2. W H AT D O Y O U S E

    E ?
  3. W H AT D O Y O U S E

    E ?
  4. I II III IV x y x y x y

    x y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89 99.0 82.51 99.0 82.51 99.0 82.5 99.0 82.51 9.00 7.50 9.00 7.50 9.00 7.50 9.00 7.50 3.32 2.03 3.32 2.03 3.32 2.03 3.32 2.03
  5. I II III IV x y x y x y

    x y 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89 99.0 82.51 99.0 82.51 99.0 82.5 99.0 82.51 9.00 7.50 9.00 7.50 9.00 7.50 9.00 7.50 3.32 2.03 3.32 2.03 3.32 2.03 3.32 2.03
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  12. E X A M P L E S D A

    S H B O A R D
  13. None
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  18. S O U R C E : H T T

    P : / / S Q L - J O I N S . L E O PA R D . I N . U A /
  19. W H AT A B O U T M O

    B I L E ?
  20. S O U R C E : B R I

    G H T P O I N T I N C . C O M
  21. None
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  25. W H E R E D O Y O U

    S TA R T ? H O W - T O
  26. D ATA C O N S U M E R

  27. D ATA E N C O D E C O

    N S U M E R
  28. D ATA E N C O D E C O

    N S U M E R V I S U A L I Z AT I O N
  29. D ATA E N C O D E D E

    C O D E C O N S U M E R V I S U A L I Z AT I O N
  30. T H E R E A R E M A

    N Y A P P R O A C H E S …
  31. T H E R E A R E M A

    N Y A P P R O A C H E S … A C Q U I R E PA R S E F I LT E R M I N E R E P R E S E N T R E F I N E I N T E R A C T
  32. T H E R E A R E M A

    N Y A P P R O A C H E S … A C Q U I R E PA R S E F I LT E R M I N E R E P R E S E N T R E F I N E I N T E R A C T C L A R I F Y W H AT & W H Y E X P L O R E & S K E T C H D E F I N E & P R O D U C E M A I N TA I N & A N A LY Z E
  33. T H E R E A R E M A

    N Y A P P R O A C H E S … A C Q U I R E PA R S E F I LT E R M I N E R E P R E S E N T R E F I N E I N T E R A C T C L A R I F Y W H AT & W H Y E X P L O R E & S K E T C H D E F I N E & P R O D U C E M A I N TA I N & A N A LY Z E A C Q U I R E C L E A N I N T E G R AT E V I S U A L I Z E M O D E L P R E S E N T D I S S E M I N AT E
  34. T H E R E A R E M A

    N Y A P P R O A C H E S … A C Q U I R E PA R S E F I LT E R M I N E R E P R E S E N T R E F I N E I N T E R A C T C L A R I F Y W H AT & W H Y E X P L O R E & S K E T C H D E F I N E & P R O D U C E M A I N TA I N & A N A LY Z E A C Q U I R E C L E A N I N T E G R AT E V I S U A L I Z E M O D E L P R E S E N T D I S S E M I N AT E E S TA B L I S H C O N T E X T A C Q U I R E & P R E PA R E D ATA E D I T O R I A L F O C U S D E S I G N C O N S T R U C T & E VA L U AT E
  35. T H E R E A R E M A

    N Y A P P R O A C H E S … A C Q U I R E PA R S E F I LT E R M I N E R E P R E S E N T R E F I N E I N T E R A C T C L A R I F Y W H AT & W H Y E X P L O R E & S K E T C H D E F I N E & P R O D U C E M A I N TA I N & A N A LY Z E A C Q U I R E C L E A N I N T E G R AT E V I S U A L I Z E M O D E L P R E S E N T D I S S E M I N AT E E S TA B L I S H C O N T E X T A C Q U I R E & P R E PA R E D ATA E D I T O R I A L F O C U S D E S I G N C O N S T R U C T & E VA L U AT E K N O W T H E A U D I E N C E K N O W T H E D ATA U N D E R S TA N D C O N T E X T C O M M U N I C AT E S I M P LY C O M M U N I C AT E C L E A R LY
  36. K N O W T H E A U D

    I E N C E K N O W T H E D ATA U N D E R S TA N D C O N T E X T C O M M U N I C AT E S I M P LY C O M M U N I C AT E C L E A R LY P R O D U C T V I S U A L I Z AT I O N D E S I G N A P P R O A C H
  37. V I S U A L I Z AT I

    O N D E S I G N P R I N C I P L E S
  38. V I S U A L I Z AT I

    O N D E S I G N P R I N C I P L E S • Elegant • Truthful • Accessible • Justified
  39. 1. Know the audience 2. Know the data 3. Know

    the story 4. Communicate simply 5. Communicate clearly V I S U A L I Z AT I O N D E S I G N P R I N C I P L E S • Elegant • Truthful • Accessible • Justified
  40. 1 . K N O W T H E A

    U D I E N C E
  41. 1 . K N O W T H E A

    U D I E N C E • What questions do they have?
  42. 1 . K N O W T H E A

    U D I E N C E • What questions do they have? • How will they use this information to make decisions?
  43. 1 . K N O W T H E A

    U D I E N C E • What questions do they have? • How will they use this information to make decisions? • What level of familiarity do they have with the source data?
  44. 1 . K N O W T H E A

    U D I E N C E • What questions do they have? • How will they use this information to make decisions? • What level of familiarity do they have with the source data? • How often will they be using this information?
  45. 1 . K N O W T H E A

    U D I E N C E • What questions do they have? • How will they use this information to make decisions? • What level of familiarity do they have with the source data? • How often will they be using this information? • What level of sophistication do they with analytics/statistics?
  46. 1 . K N O W T H E A

    U D I E N C E • What questions do they have? • How will they use this information to make decisions? • What level of familiarity do they have with the source data? • How often will they be using this information? • What level of sophistication do they with analytics/statistics? • Is your visualization more exploratory or explanatory?
  47. A U D I E N C E A S

    K S Q U E S T I O N , S E E S D ATA , G E T S A N S W E R A U D I E N C E S E E S D ATA , A S K Q U E S T I O N S , E X P L O R E S D ATA , G E T S A N S W E R
  48. 2 . K N O W T H E D

    ATA
  49. 2 . K N O W T H E D

    ATA • What is the quality of the data? Can you trust it?
  50. 2 . K N O W T H E D

    ATA • What is the quality of the data? Can you trust it? • What questions can the data answer?
  51. 2 . K N O W T H E D

    ATA • What is the quality of the data? Can you trust it? • What questions can the data answer? • How old/new is the data?
  52. 2 . K N O W T H E D

    ATA • What is the quality of the data? Can you trust it? • What questions can the data answer? • How old/new is the data? • What are the inherent relationships between values?
  53. 2 . K N O W T H E D

    ATA • What is the quality of the data? Can you trust it? • What questions can the data answer? • How old/new is the data? • What are the inherent relationships between values? • What other data can we combine with it?
  54. 2 . K N O W T H E D

    ATA • What is the quality of the data? Can you trust it? • What questions can the data answer? • How old/new is the data? • What are the inherent relationships between values? • What other data can we combine with it? • What assumptions will you have to make?
  55. 2 . K N O W T H E D

    ATA • What is the quality of the data? Can you trust it? • What questions can the data answer? • How old/new is the data? • What are the inherent relationships between values? • What other data can we combine with it? • What assumptions will you have to make? • Does a data dictionary exist?
  56. None
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  61. C O R R E L AT I O N

    ≠ C A U S AT I O N
  62. H T T P : / / W W W.

    T Y L E R V I G E N . C O M / S P U R I O U S - C O R R E L AT I O N S
  63. 3 . U N D E R S TA N

    D T H E C O N T E X T
  64. 3 . U N D E R S TA N

    D T H E C O N T E X T • What does the data tell you?
  65. 3 . U N D E R S TA N

    D T H E C O N T E X T • What does the data tell you? • How is the data insightful or interesting to your user?
  66. 3 . U N D E R S TA N

    D T H E C O N T E X T • What does the data tell you? • How is the data insightful or interesting to your user? • Why is your finding occurring or not occurring?
  67. 3 . U N D E R S TA N

    D T H E C O N T E X T • What does the data tell you? • How is the data insightful or interesting to your user? • Why is your finding occurring or not occurring? • How will this help your users?
  68. 3 . U N D E R S TA N

    D T H E C O N T E X T • What does the data tell you? • How is the data insightful or interesting to your user? • Why is your finding occurring or not occurring? • How will this help your users? • What decisions will they need to make?
  69. 3 . U N D E R S TA N

    D T H E C O N T E X T • What does the data tell you? • How is the data insightful or interesting to your user? • Why is your finding occurring or not occurring? • How will this help your users? • What decisions will they need to make? • What are their next steps?
  70. None
  71. Things that make you go “Hmmm…”

  72. 4 . C O M M U N I C

    AT E S I M P LY
  73. 4 . C O M M U N I C

    AT E S I M P LY • How long does it take for your audience to arrive at your conclusion?
  74. 4 . C O M M U N I C

    AT E S I M P LY • How long does it take for your audience to arrive at your conclusion? • Is the message delivered accurately & consistently?
  75. 4 . C O M M U N I C

    AT E S I M P LY • How long does it take for your audience to arrive at your conclusion? • Is the message delivered accurately & consistently? • [Interactive] How many clicks to find the answer to their question?
  76. 4 . C O M M U N I C

    AT E S I M P LY • How long does it take for your audience to arrive at your conclusion? • Is the message delivered accurately & consistently? • [Interactive] How many clicks to find the answer to their question? • Where do they go in your app next?
  77. O H D E A R . . B E

    C A U S E Y O U C A N . . S H O U L D Y O U ?
  78. None
  79. None
  80. 5 . C O M M U N I C

    AT E C L E A R LY
  81. 5 . C O M M U N I C

    AT E C L E A R LY • What are the key findings & messages?
  82. 5 . C O M M U N I C

    AT E C L E A R LY • What are the key findings & messages? • What is the right way to visualize the findings?
  83. 5 . C O M M U N I C

    AT E C L E A R LY • What are the key findings & messages? • What is the right way to visualize the findings? • How do they interpret the data?
  84. 5 . C O M M U N I C

    AT E C L E A R LY • What are the key findings & messages? • What is the right way to visualize the findings? • How do they interpret the data? • What decisions will they make from this visualization?
  85. 5 . C O M M U N I C

    AT E C L E A R LY • What are the key findings & messages? • What is the right way to visualize the findings? • How do they interpret the data? • What decisions will they make from this visualization? • Is it easy to understand the findings?
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  89. A D A M M C C A N N

    , TA B L E A U Z E N M A S T E R If the meaning of the data is not conveyed, the visualization is a failure.
  90. D ATA : I N K R AT I O

  91. None
  92. S O U R C E : B R I

    G H T P O I N T I N C . C O M
  93. None
  94. D A S H B O A R D S

  95. — S H N E I D E R M

    A N “Overview First, Zoom and Filter, Then Details-on-Demand”
  96. T H E S E V E N TA S

    K S O F I N F O V I Z U S E R S • Overview: Gain an overview of the entire collection. • Zoom : Zoom in on items of interest • Filter: Filter out uninteresting items. • Details-on-demand: Select an item or group and get details when needed • Relate: View relationships among items. • History: Keep a history of actions to support undo, replay, and query parameters. • Extract: Allow extraction of sub-collections and of the progressive refinement.
  97. G O O D E X A M P L

    E S
  98. None
  99. D ATA V I S U A L I Z

    AT I O N T H A N K S !