Data Viz for Products

Presented at ProductCamp Boston 2016

Transcript

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
12. E X A M P L E S D A S H B O A R D
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
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?
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?
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 ?
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?
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
92. S O U R C E : B R I G H T P O I N T I N C . C O M
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
99. D ATA V I S U A L I Z AT I O N T H A N K S !