strata preview 2014: design thinking for dummies (data scientists)

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@deanmalmgren @mstringer @laurieskelly 2014 february strata preview design thinking for dummies (data scientists) tuesday, february 11, 9:00 a.m.

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Data scientists often face ambiguous challenges and, as a group, should use and make use of the design process to address these challenges. These slides briefly make the case for using the design process. Interested in more, reach out!

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Page 1: Strata preview 2014: Design thinking for dummies (data scientists)

@deanmalmgren @mstringer

@laurieskelly

2014 february strata preview

design thinking for dummies (data scientists)tuesday, february 11, 9:00 a.m.

Page 2: Strata preview 2014: Design thinking for dummies (data scientists)

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

@deanmalmgren | bit.ly/design-data

Page 3: Strata preview 2014: Design thinking for dummies (data scientists)

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = b

@deanmalmgren | bit.ly/design-data

Page 4: Strata preview 2014: Design thinking for dummies (data scientists)

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = boptimize A x = b

subject to f(x) > 0

@deanmalmgren | bit.ly/design-data

Page 5: Strata preview 2014: Design thinking for dummies (data scientists)

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = b optimize f(x)

optimize A x = b

subject to f(x) > 0

@deanmalmgren | bit.ly/design-data

Page 6: Strata preview 2014: Design thinking for dummies (data scientists)

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = b optimize f(x)

optimize A x = b

subject to f(x) > 0

optimize “our profitability”

@deanmalmgren | bit.ly/design-data

Page 7: Strata preview 2014: Design thinking for dummies (data scientists)

origins of ambiguitymany feasible approaches

@deanmalmgren | bit.ly/design-data

Page 8: Strata preview 2014: Design thinking for dummies (data scientists)

origins of ambiguityunclear problems

@deanmalmgren | bit.ly/design-data

identify the best locations to plant new trees

Page 9: Strata preview 2014: Design thinking for dummies (data scientists)

origins of ambiguityunclear problems

@deanmalmgren | bit.ly/design-data

identify the best locations to plant new treeshow many?

what kinds of trees? move old trees?

replace old trees?

Page 10: Strata preview 2014: Design thinking for dummies (data scientists)

origins of ambiguityunclear problems

identify the best locations to plant new treeshow many?

what kinds of trees? move old trees?

replace old trees?

aesthetically pleasing? maximize growth? increase folliage? offset CO2 emissions?

@deanmalmgren | bit.ly/design-data

Page 11: Strata preview 2014: Design thinking for dummies (data scientists)

@deanmalmgren | bit.ly/design-data

“design process” is used everywhereanticipate failure

generate hypotheses

build prototype

evaluate feedback

1-4 week iterations

Page 12: Strata preview 2014: Design thinking for dummies (data scientists)

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

surveys, interviews, focus groups split testing, A/B testing QA; requirements churn

personas, scenarios, use cases business/product requirements story/user cards

build device prototypes minimum viable product write code

human-centered design lean startup agile programming

“design process” is used everywhereanticipate failure

1-4 week iterations

Page 13: Strata preview 2014: Design thinking for dummies (data scientists)

@deanmalmgren | bit.ly/design-data

design and data sciencechallenges in practice

generate hypotheses

build prototype

evaluate feedback

problem lost in translation

1-4 week iterations

Page 14: Strata preview 2014: Design thinking for dummies (data scientists)

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

problem lost in translation

takes a long time to collect data, analyze, and build visualization

design and data sciencechallenges in practice

1-4 week iterations

Page 15: Strata preview 2014: Design thinking for dummies (data scientists)

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

proof is in the pudding

problem lost in translation

takes a long time to collect data, analyze, and build visualization

design and data sciencechallenges in practice

1-4 week iterations

Page 16: Strata preview 2014: Design thinking for dummies (data scientists)

http://bit.ly/design-data !

@deanmalmgren [email protected]

solve ambiguous problems with an iterative approach