Data & Design Like PB&J

A 2015 PwC survey of 1,300 CEOs in 77 countries, ranked data mining and analytics as the second most important digital technology and organizational capability. What does this mean for designers? How can designers be “data literate?” Designers who understand data will be the designers who make a bigger impact with their work. Design solves problems, we know this well. Data helps inform the choices you make to solve those problems. Taking this a step further into product and experience design, this talk higlights how data can be used to help make design choices that produce better experiences for our users. We’ll present an approach to follow along with examples in the field to draw inspiration for your work.

Transcript

1. DATA & DESIGN
   LIKE PB & J
   C. TODD LOMBARDO — @IAMCTODD
   HEAD OF PRODUCT & EXPERIENCE @ WORKBAR
   HELLO@CTODD.COM
   

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2. WHO WORKS WITH
   DATA TODAY?
   

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3. FAST COMPANY
   

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4. “DATA SCIENCE IS AN ACT OF
   INTERPRETATION — WE TRANSLATE THE
   CUSTOMER’S ‘VOICE’ INTO A LANGUAGE
   MORE SUITABLE FOR DECISION-MAKING.”
   Riley Newman, Head of Data Science @ Airbnb
   

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5. “DATA SCIENCE IS AN ACT OF
   INTERPRETATION — WE TRANSLATE THE
   CUSTOMER’S ‘VOICE’ INTO A LANGUAGE
   MORE SUITABLE FOR DECISION-MAKING.”
   Riley Newman, Head of Data Science @ Airbnb
   DESIGN
   NEEDS
   TAKING ACTIONS
   

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6. 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
   99.00 82.51 99.00 82.51 99.00 82.5 99.00 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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7. 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
   99.00 82.51 99.00 82.51 99.00 82.5 99.00 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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9. 1) WHO ARE MY USERS?
   2) WHAT ARE THEY DOING? WHAT WILL THEY DO?
   3) WHAT ARE THE LIMITATIONS TO THE DATA?
   4) WTF SHOULD I DO?
   

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10. 1 WHO ARE MY USERS?
    

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11. K-MEANS
    CLUSTERING
    

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12. K-WHAT??
    

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13. FIND GROUPS WHICH HAVE NOT BEEN
    EXPLICITLY LABELED IN THE DATA.
    

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15. 3
    2
    1
    PICK RANDOM DATA POINTS
    

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16. 3
    2
    1
    PICK RANDOM DATA POINTS
    FIND NEAREST POINTS
    

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17. 3
    2
    1
    PICK RANDOM DATA POINTS
    MAKE CLUSTERS OF NEAREST DISTANCE
    FIND NEAREST POINTS
    

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18. 1
    3
    2
    PICK RANDOM DATA POINTS
    MAKE CLUSTERS OF NEAREST DISTANCE
    FIND NEAREST POINTS
    FIND NEW CENTER OF CLUSTER
    

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19. 1
    3
    2
    PICK RANDOM DATA POINTS
    MAKE CLUSTERS OF NEAREST DISTANCE
    FIND NEAREST POINTS
    FIND NEW CENTER OF CLUSTER
    CALCULATE NEW CLUSTERS
    

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20. 1
    3
    2
    PICK RANDOM DATA POINTS
    MAKE CLUSTERS OF NEAREST DISTANCE
    FIND NEAREST POINTS
    FIND NEW CENTER OF CLUSTER
    CALCULATE NEW CLUSTERS
    

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21. 1
    3
    2
    PICK RANDOM DATA POINTS
    MAKE CLUSTERS OF NEAREST DISTANCE
    FIND NEAREST POINTS
    FIND NEW CENTER OF CLUSTER
    CALCULATE NEW CLUSTERS
    STOP WHEN POINTS DON’T CHANGE CLUSTERS
    

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23. LISTING OF OFFERS
    SOURCE: DATASMART
    

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24. CUSTOMERS WHO HAVE
    PURCHASED OFFERS
    SOURCE: DATASMART
    

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25. PIVOT
    TABLES
    SOURCE: DATASMART
    

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26. MATRIX
    SOURCE: DATASMART
    

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27. DISTANCE
    TO CLUSTER
    CENTERS
    SOURCE: DATASMART
    

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28. USE SOLVER
    OBJECTIVE: MINIMIZE DISTANCE TO CLUSTER CENTERS
    DECISION VARIABLES: DEAL VALUES OF EACH ROW
    CONSTRAINTS: CLUSTER CENTERS BETWEEN 0 AND 1
    SOURCE: DATASMART
    

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29. TOP DEALS PER CLUSTER
    

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30. CLUSTER 1
    TOP DEALS
    WHO LOVES PINOT NOIR?
    SOURCE: DATASMART
    

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31. CLUSTER 2
    TOP DEALS
    WHO LOVES A GOOD DEAL?
    WHO’S NOT BUYING BIG?
    SOURCE: DATASMART
    

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32. CLUSTER 3
    TOP DEALS
    HMMM…..?
    HOLIDAY CHAMPAGNE?
    SOURCE: DATASMART
    

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33. CLUSTER 4
    TOP DEALS
    SUMMER CHAMPAGNE LOVERS!
    SOURCE: DATASMART
    

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34. PINOT LOVERS DEALHUNTERS SEASONAL
    BUYER
    SUMMER
    CHAMPAGNE LOVER
    

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35. 2 WHAT ARE THEY
    DOING? WILL THEY DO?
    

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36. WHAT ARE THEY
    DOING?
    

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40. WHAT WILL THEY DO?
    

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41. IMAGE: ESHAAN KAUL
    

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42. IMAGE: ESHAAN KAUL
    

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43. DATA NOT REAL, FOR EXAMPLE ONLY
    

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44. 27% 43% 10% 6%
    DATA NOT REAL, FOR EXAMPLE ONLY
    24%
    

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46. WHAT’S THE OBJECTIVE?
    

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47. WHAT’S THE OBJECTIVE?
    HOW DOES THE CURRENT DESIGN REACH THAT OBJECTIVE?
    

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48. WHAT’S THE OBJECTIVE?
    HOW DOES THE CURRENT DESIGN REACH THAT OBJECTIVE?
    WHAT WAYS CAN WE BETTER REACH THAT OBJECTIVE?
    

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50. 3 WHAT ARE THE
    LIMITS OF THE DATA?
    

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51. DATA CAN MISLEAD
    

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52. 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
    

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55. CORRELATION
    ≠
    CAUSATION
    

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56. YOU ARE BIASED
    AND SO IS THE DATA
    

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57. A N C H O R I N G
    S TAT U S Q U O
    S E L E C T I O N
    N E G AT I V E
    C O N F I R M AT I O N
    I N - G R O U P
    P R O B A B I L I T Y
    R AT I O N A L I Z E
    G A M B L E R ’ S
    B A N D WA G O N
    P R O J E C T I O N
    C U R R E N T
    M O M E N T
    

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58. INCONVENIENT TRUTHS OF DATA SCIENCE
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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59. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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60. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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61. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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62. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    ‣ In 90% of cases generalized linear regression will do the trick.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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63. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    ‣ In 90% of cases generalized linear regression will do the trick.
    ‣ You should embrace the Bayesian approach.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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64. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    ‣ In 90% of cases generalized linear regression will do the trick.
    ‣ You should embrace the Bayesian approach.
    ‣ No one cares how you did it.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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65. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    ‣ In 90% of cases generalized linear regression will do the trick.
    ‣ You should embrace the Bayesian approach.
    ‣ No one cares how you did it.
    ‣ Academia and business are two different worlds.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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66. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    ‣ In 90% of cases generalized linear regression will do the trick.
    ‣ You should embrace the Bayesian approach.
    ‣ No one cares how you did it.
    ‣ Academia and business are two different worlds.
    ‣ Presentation is critical. Context makes the story
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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67. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    ‣ In 90% of cases generalized linear regression will do the trick.
    ‣ You should embrace the Bayesian approach.
    ‣ No one cares how you did it.
    ‣ Academia and business are two different worlds.
    ‣ Presentation is critical. Context makes the story
    ‣ All models are false, but some are useful.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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68. INCONVENIENT TRUTHS OF DATA SCIENCE
    ‣ Data is never clean.
    ‣ You will spend most of your time cleaning and preparing data.
    ‣ 95% of tasks do not require deep learning.
    ‣ In 90% of cases generalized linear regression will do the trick.
    ‣ You should embrace the Bayesian approach.
    ‣ No one cares how you did it.
    ‣ Academia and business are two different worlds.
    ‣ Presentation is critical. Context makes the story
    ‣ All models are false, but some are useful.
    ‣ There is no fully automated Data Science. You need to get your hands dirty.
    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
    

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69. 4 WHAT CAN I DO AS
    A DESIGNER?
    

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70. UNDERSTAND THE
    PROBLEM:
    ASK MORE QUESTIONS!
    

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71. WHAT ARE WE TRYING TO ACCOMPLISH?
    WHAT DO WE KNOW TODAY?
    WHAT DO WE WANT TO KNOW?
    WHAT DATA DO WE HAVE?
    WHAT DATA DO WE NEED?
    

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72. PROBLEM SOLUTION
    THANKS: W. BRÜNING
    

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73. PROBLEM SOLUTION
    Water on the floor Mop
    THANKS: W. BRÜNING
    

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74. PROBLEM SOLUTION
    Water on the floor Mop
    WHY? Leaky pipe Replace pipe
    THANKS: W. BRÜNING
    

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75. PROBLEM SOLUTION
    Water on the floor Mop
    WHY? Leaky pipe Replace pipe
    WHY? Too much pressure Lower pressure
    THANKS: W. BRÜNING
    

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76. PROBLEM SOLUTION
    Water on the floor Mop
    WHY? Leaky pipe Replace pipe
    WHY? Too much pressure Lower pressure
    WHY? Pressure regulator Replace regulator
    THANKS: W. BRÜNING
    

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77. PROBLEM SOLUTION
    Water on the floor Mop
    WHY? Leaky pipe Replace pipe
    WHY? Too much pressure Lower pressure
    WHY? Pressure regulator Replace regulator
    WHY? Maintenance schedule More frequent inspection
    THANKS: W. BRÜNING
    

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80. BRING DATA INTO YOUR
    DESIGN PROCESS
    

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83. 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
    D E S I G N
    S O L U T I O N
    E VA L U AT E
    

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84. “WHEN WE DON’T WORK WITH REAL
    DATA, WE DECEIVE OURSELVES.”
    Josh Puckett, Design Partner @ Combine VC
    

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85. REFRESH YOUR
    MINDSET
    

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86. DESIGNER SCIENTIST
    EMPATHIC
    MAKER
    FORWARD LOOKING
    RIGOROUS
    EXPERIMENTAL
    HYPOTHESIS DRIVEN
    

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87. DESIGNTIST
    

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88. DESIGN / UX PRODUCT
    DEVELOPMENT
    

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89. DATA
    DESIGN / UX PRODUCT
    DEVELOPMENT DATA
    

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90. ¡GRACIAS!
    C. TODD LOMBARDO — @IAMCTODD
    HEAD OF PRODUCT & EXPERIENCE @ WORKBAR
    HELLO@CTODD.COM
    

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