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Data & Design Like PB&J

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.

C. Todd Lombardo

February 15, 2018
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  1. DATA & DESIGN LIKE PB & J C. TODD LOMBARDO

    — @IAMCTODD HEAD OF PRODUCT & EXPERIENCE @ WORKBAR [email protected]
  2. “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
  3. “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
  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 99.00 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
  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 99.00 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
  6. 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?
  7. 3 2 1 PICK RANDOM DATA POINTS MAKE CLUSTERS OF

    NEAREST DISTANCE FIND NEAREST POINTS
  8. 1 3 2 PICK RANDOM DATA POINTS MAKE CLUSTERS OF

    NEAREST DISTANCE FIND NEAREST POINTS FIND NEW CENTER OF CLUSTER
  9. 1 3 2 PICK RANDOM DATA POINTS MAKE CLUSTERS OF

    NEAREST DISTANCE FIND NEAREST POINTS FIND NEW CENTER OF CLUSTER CALCULATE NEW CLUSTERS
  10. 1 3 2 PICK RANDOM DATA POINTS MAKE CLUSTERS OF

    NEAREST DISTANCE FIND NEAREST POINTS FIND NEW CENTER OF CLUSTER CALCULATE NEW CLUSTERS
  11. 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
  12. USE SOLVER OBJECTIVE: MINIMIZE DISTANCE TO CLUSTER CENTERS DECISION VARIABLES:

    DEAL VALUES OF EACH ROW CONSTRAINTS: CLUSTER CENTERS BETWEEN 0 AND 1 SOURCE: DATASMART
  13. CLUSTER 2 TOP DEALS WHO LOVES A GOOD DEAL? WHO’S

    NOT BUYING BIG? SOURCE: DATASMART
  14. WHAT’S THE OBJECTIVE? HOW DOES THE CURRENT DESIGN REACH THAT

    OBJECTIVE? WHAT WAYS CAN WE BETTER REACH THAT OBJECTIVE?
  15. 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
  16. 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
  17. INCONVENIENT TRUTHS OF DATA SCIENCE ‣ Data is never clean.

    SOURCE: KAMIL BARTOCHA (LASTMINUTE.COM)
  18. 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)
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. 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)
  24. 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)
  25. 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)
  26. 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)
  27. 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?
  28. PROBLEM SOLUTION Water on the floor Mop WHY? Leaky pipe

    Replace pipe WHY? Too much pressure Lower pressure THANKS: W. BRÜNING
  29. 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
  30. 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
  31. 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
  32. “WHEN WE DON’T WORK WITH REAL DATA, WE DECEIVE OURSELVES.”

    Josh Puckett, Design Partner @ Combine VC