@deanmalmgren
@DsAtweet
2015 april 9
nuvention analytics
solve for ambiguity
adapting the design process for analytics pro...
data scientists thrive with ambiguity
solve for x
x = 5 + 2
projectevolution
A x = b optimize
f(x)
optimize
A x = b
subjec...
origins of ambiguity
many feasible approaches
@deanmalmgren | bit.ly/design-data
origins of ambiguity
unclear problems
@deanmalmgren | bit.ly/design-data
identify the best locations to plant new trees
origins of ambiguity
unclear problems
@deanmalmgren | bit.ly/design-data
identify the best locations to plant new trees
ho...
origins of ambiguity
unclear problems
identify the best locations to plant new trees
how many?
what kinds of trees?
move o...
@deanmalmgren | bit.ly/design-data
generate
hypotheses
build
prototype
evaluate
feedback
surveys, interviews, focus groups...
data-driven e-discovery
daegis
@deanmalmgren | bit.ly/design-data
data-driven e-discovery
daegis
aboutpatent
not
aboutpatent
@deanmalmgren | bit.ly/design-data
daegis
aboutpatent
not
aboutpatent
turn over to plaintiff
don’t
turn over to plaintiff
adverse inference
data-driven e-dis...
daegis
aboutpatent
not
aboutpatent
turn over to plaintiff
don’t
turn over to plaintiff
adverse inference
give away trade s...
daegis
aboutpatent
not
aboutpatent
turn over to plaintiff
don’t
turn over to plaintiff
adverse inference
give away trade s...
daegis
turn over to plaintiff
don’t
turn over to plaintiff
data-driven e-discovery
@deanmalmgren | bit.ly/design-data
daegis
data-driven e-discovery
@deanmalmgren | bit.ly/design-data
daegis
create a “document map”
algorithm design
patents
marketing
finances
fantasy football
lunch
coffee
data-driven e-disc...
daegis
create a “document map”
fantasy football
algorithm design
patents
lunch
marketing
finances
coffee
review away shades...
daegis
data-driven e-discovery
@deanmalmgren | bit.ly/design-data
motorola
new product
announcement
first versions
from manufacturer
available
in stores
next generation
to manufacturer
prod...
motorola
@deanmalmgren | bit.ly/design-data
data-driven consumer feedback
motorola
@deanmalmgren | bit.ly/design-data
data-driven consumer feedback
@deanmalmgren | bit.ly/design-data
generate
hypotheses
build
prototype
evaluate
feedback
proof is in the pudding
problem l...
@deanmalmgren | bit.ly/design-data
a project always starts with…
@deanmalmgren | bit.ly/design-data
informal conversation to stated goals
mostly bad ideas, but a few good ones
@deanmalmgren | bit.ly/design-data
mostly bad ideas, but a few good ones
Lorem Ipsum: a narrative about blankets.
Author: ...
@deanmalmgren | bit.ly/design-data
mostly bad ideas, but a few good ones
informal conversation to stated goals
now what?
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
@deanmalmgren | bit.ly/design-data
concept sketch comparisons
qualitative a/b testing
search engine
with relevance metrics...
@deanmalmgren | bit.ly/design-data
from sketch to blue print
add detail to get feedback (while building)
@deanmalmgren | bit.ly/design-data
from sketch to blue print
add detail to get feedback (while building)
@deanmalmgren | bit.ly/design-data
from sketch to blue print
add detail to get feedback (while building)
@deanmalmgren | bit.ly/design-data
prototype iterations
faux first; KISS; build for feedback
@deanmalmgren | bit.ly/design-data
generate
hypotheses
build
prototype
evaluate
feedback
proof is in the pudding
problem l...
http://bit.ly/design-data
@deanmalmgren
dean.malmgren@datascopeanalytics.com
solve ambiguous problems
with an iterative ap...
20150409 nuvention analytics
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20150409 nuvention analytics

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This talk was prepared for NUvention Analytics on 2015.04.09 as a set of examples to help students learn why the iterative design process is a compelling way to build tools, products, etc.

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20150409 nuvention analytics

  1. 1. @deanmalmgren @DsAtweet 2015 april 9 nuvention analytics solve for ambiguity adapting the design process for analytics problems
  2. 2. data scientists thrive with ambiguity solve for x x = 5 + 2 projectevolution A x = b optimize f(x) optimize A x = b subject to f(x) > 0 optimize “our profitability” @deanmalmgren | bit.ly/design-data
  3. 3. origins of ambiguity many feasible approaches @deanmalmgren | bit.ly/design-data
  4. 4. origins of ambiguity unclear problems @deanmalmgren | bit.ly/design-data identify the best locations to plant new trees
  5. 5. origins of ambiguity unclear problems @deanmalmgren | bit.ly/design-data identify the best locations to plant new trees how many? what kinds of trees? move old trees? replace old trees?
  6. 6. 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? aesthetically pleasing? maximize growth? increase foliage? offset CO2 emissions? @deanmalmgren | bit.ly/design-data
  7. 7. @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 everywhere anticipate failure 1-4 week iterations
  8. 8. data-driven e-discovery daegis @deanmalmgren | bit.ly/design-data
  9. 9. data-driven e-discovery daegis aboutpatent not aboutpatent @deanmalmgren | bit.ly/design-data
  10. 10. daegis aboutpatent not aboutpatent turn over to plaintiff don’t turn over to plaintiff adverse inference data-driven e-discovery @deanmalmgren | bit.ly/design-data
  11. 11. daegis aboutpatent not aboutpatent turn over to plaintiff don’t turn over to plaintiff adverse inference give away trade secrets data-driven e-discovery @deanmalmgren | bit.ly/design-data
  12. 12. daegis aboutpatent not aboutpatent turn over to plaintiff don’t turn over to plaintiff adverse inference give away trade secrets data-driven e-discovery @deanmalmgren | bit.ly/design-data
  13. 13. daegis turn over to plaintiff don’t turn over to plaintiff data-driven e-discovery @deanmalmgren | bit.ly/design-data
  14. 14. daegis data-driven e-discovery @deanmalmgren | bit.ly/design-data
  15. 15. daegis create a “document map” algorithm design patents marketing finances fantasy football lunch coffee data-driven e-discovery @deanmalmgren | bit.ly/design-data
  16. 16. daegis create a “document map” fantasy football algorithm design patents lunch marketing finances coffee review away shades of grey reduce reviews by 90-99% data-driven e-discovery @deanmalmgren | bit.ly/design-data
  17. 17. daegis data-driven e-discovery @deanmalmgren | bit.ly/design-data
  18. 18. motorola new product announcement first versions from manufacturer available in stores next generation to manufacturer product defects from consumers @deanmalmgren | bit.ly/design-data data-driven consumer feedback
  19. 19. motorola @deanmalmgren | bit.ly/design-data data-driven consumer feedback
  20. 20. motorola @deanmalmgren | bit.ly/design-data data-driven consumer feedback
  21. 21. @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 science challenges in practice 1-4 week iterations
  22. 22. @deanmalmgren | bit.ly/design-data a project always starts with…
  23. 23. @deanmalmgren | bit.ly/design-data informal conversation to stated goals mostly bad ideas, but a few good ones
  24. 24. @deanmalmgren | bit.ly/design-data mostly bad ideas, but a few good ones Lorem Ipsum: a narrative about blankets. Author: Charlie Brown Date: 31 Jan 2012 Lorem Ipsum is a dummy text used when typesetting or marking up documents. It has a long history starting from the 1500s and is still used in digital millennium for typesetting electronic documents, page designs, etc. In itself, the original text of Lorem Ipsum might have been taken from an ancient Latin book that was written about 50 BC. Nevertheless, Lorem Ipsum’s words have been changed so they don’t read as a proper text. Naturally, page designs that are made for text documents must contain some text rather than placeholder dots or something else. However, should they contain proper English words and sentences almost every reader will deliberately try to interpret it eventually, missing the design itself. However, a placeholder text must have a natural distribution of letters and punctuation or otherwise the markup will look strange and unnatural. That’s what Lorem Ipsum helps to achieve. I would like to thank Peppermint Pattyfor her support on studying Lorem Ipsum as well as the infinite wisdom of Linus van Peltand his willingness to use his blanket in my experiments. informal conversation to stated goals
  25. 25. @deanmalmgren | bit.ly/design-data mostly bad ideas, but a few good ones informal conversation to stated goals now what?
  26. 26. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing
  27. 27. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing
  28. 28. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing
  29. 29. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing
  30. 30. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing
  31. 31. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing
  32. 32. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing
  33. 33. @deanmalmgren | bit.ly/design-data concept sketch comparisons qualitative a/b testing search engine with relevance metrics demographics human readable expertise summary now what?
  34. 34. @deanmalmgren | bit.ly/design-data from sketch to blue print add detail to get feedback (while building)
  35. 35. @deanmalmgren | bit.ly/design-data from sketch to blue print add detail to get feedback (while building)
  36. 36. @deanmalmgren | bit.ly/design-data from sketch to blue print add detail to get feedback (while building)
  37. 37. @deanmalmgren | bit.ly/design-data prototype iterations faux first; KISS; build for feedback
  38. 38. @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 tips for designing with data 1-4 week iterations
  39. 39. http://bit.ly/design-data @deanmalmgren dean.malmgren@datascopeanalytics.com solve ambiguous problems with an iterative approach

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