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DATA & DESIGN LIKE PB & J C. TODD LOMBARDO — @IAMCTODD HEAD OF PRODUCT & EXPERIENCE @ WORKBAR [email protected]

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WHO WORKS WITH DATA TODAY?

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FAST COMPANY

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“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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“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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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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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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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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1 WHO ARE MY USERS?

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K-MEANS CLUSTERING

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K-WHAT??

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

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3 2 1 PICK RANDOM DATA POINTS

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3 2 1 PICK RANDOM DATA POINTS FIND NEAREST POINTS

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

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

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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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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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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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LISTING OF OFFERS SOURCE: DATASMART

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CUSTOMERS WHO HAVE PURCHASED OFFERS SOURCE: DATASMART

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PIVOT TABLES SOURCE: DATASMART

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MATRIX SOURCE: DATASMART

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DISTANCE TO CLUSTER CENTERS SOURCE: DATASMART

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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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TOP DEALS PER CLUSTER

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

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

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

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CLUSTER 4 TOP DEALS SUMMER CHAMPAGNE LOVERS! SOURCE: DATASMART

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PINOT LOVERS DEALHUNTERS SEASONAL BUYER SUMMER CHAMPAGNE LOVER

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2 WHAT ARE THEY DOING? WILL THEY DO?

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WHAT ARE THEY DOING?

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WHAT WILL THEY DO?

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IMAGE: ESHAAN KAUL

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IMAGE: ESHAAN KAUL

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DATA NOT REAL, FOR EXAMPLE ONLY

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

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WHAT’S THE OBJECTIVE?

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

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

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3 WHAT ARE THE LIMITS OF THE DATA?

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DATA CAN MISLEAD

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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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CORRELATION ≠ CAUSATION

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YOU ARE BIASED AND SO IS THE DATA

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

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

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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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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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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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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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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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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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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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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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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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4 WHAT CAN I DO AS A DESIGNER?

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UNDERSTAND THE PROBLEM: ASK MORE QUESTIONS!

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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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PROBLEM SOLUTION THANKS: W. BRÜNING

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

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

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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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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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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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BRING DATA INTO YOUR DESIGN PROCESS

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

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REFRESH YOUR MINDSET

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DESIGNER SCIENTIST EMPATHIC MAKER FORWARD LOOKING RIGOROUS EXPERIMENTAL HYPOTHESIS DRIVEN

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DESIGNTIST

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DESIGN / UX PRODUCT DEVELOPMENT

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DATA DESIGN / UX PRODUCT DEVELOPMENT DATA

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¡GRACIAS! C. TODD LOMBARDO — @IAMCTODD HEAD OF PRODUCT & EXPERIENCE @ WORKBAR [email protected]