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
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
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.
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
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?
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%
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?
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
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
80. BRING DATA INTO YOUR DESIGN PROCESS
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