Data & Design Like PB&J

7cbd3f5f922d798cc312eaf596de7ae6?s=47 C. Todd Lombardo
February 15, 2018

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.

7cbd3f5f922d798cc312eaf596de7ae6?s=128

C. Todd Lombardo

February 15, 2018
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Transcript

  1. DATA & DESIGN LIKE PB & J C. TODD LOMBARDO

    — @IAMCTODD HEAD OF PRODUCT & EXPERIENCE @ WORKBAR HELLO@CTODD.COM
  2. WHO WORKS WITH DATA TODAY?

  3. FAST COMPANY

  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
  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
  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
  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
  8. None
  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?
  10. 1 WHO ARE MY USERS?

  11. K-MEANS CLUSTERING

  12. K-WHAT??

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

    DATA.
  14. None
  15. 3 2 1 PICK RANDOM DATA POINTS

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

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

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

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

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

    NEAREST DISTANCE FIND NEAREST POINTS FIND NEW CENTER OF CLUSTER CALCULATE NEW CLUSTERS
  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
  22. None
  23. LISTING OF OFFERS SOURCE: DATASMART

  24. CUSTOMERS WHO HAVE PURCHASED OFFERS SOURCE: DATASMART

  25. PIVOT TABLES SOURCE: DATASMART

  26. MATRIX SOURCE: DATASMART

  27. DISTANCE TO CLUSTER CENTERS SOURCE: DATASMART

  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
  29. TOP DEALS PER CLUSTER

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

  31. CLUSTER 2 TOP DEALS WHO LOVES A GOOD DEAL? WHO’S

    NOT BUYING BIG? SOURCE: DATASMART
  32. CLUSTER 3 TOP DEALS HMMM…..? HOLIDAY CHAMPAGNE? SOURCE: DATASMART

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

  34. PINOT LOVERS DEALHUNTERS SEASONAL BUYER SUMMER CHAMPAGNE LOVER

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

  36. WHAT ARE THEY DOING?

  37. None
  38. None
  39. None
  40. WHAT WILL THEY DO?

  41. IMAGE: ESHAAN KAUL

  42. IMAGE: ESHAAN KAUL

  43. DATA NOT REAL, FOR EXAMPLE ONLY

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

    24%
  45. None
  46. WHAT’S THE OBJECTIVE?

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

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

    OBJECTIVE? WHAT WAYS CAN WE BETTER REACH THAT OBJECTIVE?
  49. None
  50. 3 WHAT ARE THE LIMITS OF THE DATA?

  51. DATA CAN MISLEAD

  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
  53. None
  54. None
  55. CORRELATION ≠ CAUSATION

  56. YOU ARE BIASED AND SO IS THE DATA

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

  59. INCONVENIENT TRUTHS OF DATA SCIENCE ‣ Data is never clean.

    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
  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)
  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)
  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)
  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)
  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)
  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)
  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)
  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)
  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)
  69. 4 WHAT CAN I DO AS A DESIGNER?

  70. UNDERSTAND THE PROBLEM: ASK MORE QUESTIONS!

  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?
  72. PROBLEM SOLUTION THANKS: W. BRÜNING

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

  74. PROBLEM SOLUTION Water on the floor Mop WHY? Leaky pipe

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

    Replace pipe WHY? Too much pressure Lower pressure THANKS: W. BRÜNING
  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
  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
  78. None
  79. None
  80. BRING DATA INTO YOUR DESIGN PROCESS

  81. None
  82. None
  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
  84. “WHEN WE DON’T WORK WITH REAL DATA, WE DECEIVE OURSELVES.”

    Josh Puckett, Design Partner @ Combine VC
  85. REFRESH YOUR MINDSET

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

  87. DESIGNTIST

  88. DESIGN / UX PRODUCT DEVELOPMENT

  89. DATA DESIGN / UX PRODUCT DEVELOPMENT DATA

  90. ¡GRACIAS! C. TODD LOMBARDO — @IAMCTODD HEAD OF PRODUCT &

    EXPERIENCE @ WORKBAR HELLO@CTODD.COM