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

@deanmalmgren
@mstringer
@laurieskelly
2014 february
strata preview
data scientists thrive with ambiguity
solve for x

project evolution

x=5+2

@deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity
solve for x

Ax=b

project evolution

x=5+2

@deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity
solve for x

Ax=b

project evolution

x=5+2

optimize
Ax=b
subject to
f(x) > 0

@deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity
solve for x

Ax=b

optimize
f(x)

project evolution

x=5+2

optimize
Ax=b
subject to
f(x) > 0

@deanmalmgren | bit.ly/design-data
data scientists thrive with ambiguity
solve for x

Ax=b

optimize
f(x)

optimize
“our profitability”

project evolution

x=5+2

optimize
Ax=b
subject to
f(x) > 0

@deanmalmgren | bit.ly/design-data
origins of ambiguity
many feasible approaches

@deanmalmgren | bit.ly/design-data
origins of ambiguity
unclear problems

identify the best locations to plant new trees

@deanmalmgren | bit.ly/design-data
origins of ambiguity
unclear problems

identify the best locations to plant new trees
how many?
what kinds of trees?
move old trees?
replace old trees?
@deanmalmgren | bit.ly/design-data
origins of ambiguity
unclear problems

identify the best locations to plant new trees
aesthetically pleasing?
maximize growth?
increase folliage?
offset CO2 emissions?

how many?
what kinds of trees?
move old trees?
replace old trees?
@deanmalmgren | bit.ly/design-data
“design process” is used everywhere
anticipate failure
generate
hypotheses

evaluate
feedback

1-4 week
iterations

build
prototype

@deanmalmgren | bit.ly/design-data
“design process” is used everywhere
anticipate failure
human-centered design
lean startup
agile programming

evaluate
feedback

generate
hypotheses

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

1-4 week
iterations

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

build device prototypes
minimum viable product
write code
@deanmalmgren | bit.ly/design-data
design and data science
challenges in practice
problem lost in translation

evaluate
feedback

generate
hypotheses

1-4 week
iterations

build
prototype

@deanmalmgren | bit.ly/design-data
design and data science
challenges in practice
problem lost in translation

generate
hypotheses

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

evaluate
feedback

1-4 week
iterations

build
prototype

@deanmalmgren | bit.ly/design-data
design and data science
challenges in practice
problem lost in translation

generate
hypotheses

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

evaluate
feedback

1-4 week
iterations

build
prototype

proof is in the pudding

@deanmalmgren | bit.ly/design-data
solve ambiguous problems
with an iterative approach
http://bit.ly/design-data
!

@deanmalmgren
dean.malmgren@datascopeanalytics.com

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

  • 1. design thinking for dummies (data scientists) tuesday, february 11, 9:00 a.m. @deanmalmgren @mstringer @laurieskelly 2014 february strata preview
  • 2. data scientists thrive with ambiguity solve for x project evolution x=5+2 @deanmalmgren | bit.ly/design-data
  • 3. data scientists thrive with ambiguity solve for x Ax=b project evolution x=5+2 @deanmalmgren | bit.ly/design-data
  • 4. data scientists thrive with ambiguity solve for x Ax=b project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
  • 5. data scientists thrive with ambiguity solve for x Ax=b optimize f(x) project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
  • 6. data scientists thrive with ambiguity solve for x Ax=b optimize f(x) optimize “our profitability” project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
  • 7. origins of ambiguity many feasible approaches @deanmalmgren | bit.ly/design-data
  • 8. origins of ambiguity unclear problems identify the best locations to plant new trees @deanmalmgren | bit.ly/design-data
  • 9. origins of ambiguity unclear problems identify the best locations to plant new trees how many? what kinds of trees? move old trees? replace old trees? @deanmalmgren | bit.ly/design-data
  • 10. origins of ambiguity unclear problems identify the best locations to plant new trees aesthetically pleasing? maximize growth? increase folliage? offset CO2 emissions? how many? what kinds of trees? move old trees? replace old trees? @deanmalmgren | bit.ly/design-data
  • 11. “design process” is used everywhere anticipate failure generate hypotheses evaluate feedback 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
  • 12. “design process” is used everywhere anticipate failure human-centered design lean startup agile programming evaluate feedback generate hypotheses personas, scenarios, use cases business/product requirements story/user cards 1-4 week iterations build prototype surveys, interviews, focus groups split testing, A/B testing QA; requirements churn build device prototypes minimum viable product write code @deanmalmgren | bit.ly/design-data
  • 13. design and data science challenges in practice problem lost in translation evaluate feedback generate hypotheses 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
  • 14. design and data science challenges in practice problem lost in translation generate hypotheses takes a long time to collect data, analyze, and build visualization evaluate feedback 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
  • 15. design and data science challenges in practice problem lost in translation generate hypotheses takes a long time to collect data, analyze, and build visualization evaluate feedback 1-4 week iterations build prototype proof is in the pudding @deanmalmgren | bit.ly/design-data
  • 16. solve ambiguous problems with an iterative approach http://bit.ly/design-data ! @deanmalmgren dean.malmgren@datascopeanalytics.com