Myths About Designing with Data

6 Myths About

DATA  &    DESIGN  

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1  DATA  =  NUMBERS  m

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Think of it like  ARCHEOLOGY  

It’s incomplete TRACES  

…about PEOPLE  

2  DATA  =  OBJECTIVE  TRUTH  myt

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Big  data  can  have  SIGNAL  BIAS  

Big  data  can  have  ALGORITHMIC  BIAS  

Be  a  bit  Bayesian  –  Updates beliefs –  Iterative –  Incorporates multiple sources –  Yes, and... –  Never 100%

DATA  WITH  A  SOUL  Don’t collect it without purpose Use only what is needed Use data with empathy

3  BIGGER  =  BETTER  myt

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BIG  DATA   THICK  DATA  

Big  Data  •  What, where, when, how •  Multi-structured •  Collected by machines •  Broad •  Behaviors & actions of many people •  Collected as people do what they do •  People are not highly aware of data

being collected •  Analysis uses statistical methods

ü  Transactional data ü Customer service logs ü Analytics ü A/B tests ü  Social media posts ü  Sensor data

Li:le  Data  •  It’s big data for one •  Focused on personal goals •  Individuals grant access to it,

rather than companies

Thick  Data  •  How and why •  Description •  Collected by people •  In-depth •  Behaviors, actions, emotions,

intentions, motivations of a few •  Collected as part of a study •  People are highly aware of data

being collected •  Analysis includes developing

codes, summaries, and themes                

ü  Usability tests ü Contextual research ü  Interviews ü  Diaries ü Any study

Strive for BALANCE  

4  DATA  IS  FOR  MANAGERS  

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poverty rainfall global happiness copyright value culture fit

attitudes employee performance DESIGN automobile safety

emotion economic growth engagement size of an atom corruption

healthcare outcomes learning intelligence emotional intelligence

potential output social media ROI risk dolphin population love

innovation national security reputation public influence

customer satisfaction team productivity violence in households

cooperation air pollution asset value of advertising trust pH

length of Saturn’s day online readership stress level accountability

MEASURE  WITH  MEANING  Personal well-being Collective well-being Markets and money

5  DATA  KILLS  INNOVATION  myt

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The key is  PAIRING  

Data & DISCOVERY  

Public datasets Interviews

Behavioral analytics Observations

Social media data Lean research

Competitive data Ideation

DISCOVERY  PAIRINGS  

Emotional analytics Diaries

Data & IMPROVING  

A/B tests Usability tests

Surveys Intercept interviews

Customer service data Interviews

Analytics Usability tests

IMPROVEMENT  PAIRINGS  

6  THERE  IS  ONE  RIGHT  WAY  

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THERE  IS  NO  PERFECT  METHOD  

Decide on MEANINGFUL  MEASURES  

Choose the right SIGNALS  

Be sensitive to COMPLEXITY  

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