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The Human Side of Data in Product

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Recording of Dave Mathias (Beyond the Data) presenting at Twin Cities Product Conf 2019.

Publié dans : Technologie
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The Human Side of Data in Product

  1. 1. GoBeyondTheData.com Product Conf 2019 Dave Mathias Beyond the Data | @DaveMathias | @GoBeyondtheData
  2. 2. GoBeyondTheData.com • Gaps in perception and knowing your unknowns • Managing bias and uncertainty • Balancing System 1 and System 2 • A tale of two data stories
  3. 3. GoBeyondTheData.com Gaps in perception and knowing your unknowns
  4. 4. GoBeyondTheData.com Source: Youtube https://www.youtube.com/watch?v=OQxmT2kVz-c Data can be deceiving
  5. 5. GoBeyondTheData.com Perception can be imperfect Sources: https://www.verywellmind.com/cool-optical-illusions-2795841 https://www.wired.co.uk/article/optical-illusions-science-perception Which circle is darker? Which line is longer?
  6. 6. GoBeyondTheData.com Source: YouTube https://www.youtube.com/watch?v=IGQmdoK_ZfY We all have blind spots
  7. 7. GoBeyondTheData.com So what do we really “know”? Source: https://en.wikipedia.org/wiki/Johari_window Known Knowns aka Arena Known Unknowns aka Facade Unknown Unknowns aka Unknown Unknown Knowns aka Blind Spot We know We don’t know Others know Not known to others Johari Window
  8. 8. GoBeyondTheData.com Example Johari Window: Customer is the other and you are the product person Source: https://en.wikipedia.org/wiki/Johari_window Known Knowns aka Arena Known Unknowns aka Facade Unknown Unknowns aka Unknown Unknown Knowns aka Blind Spot We know We don’t know Others know Not known to others Johari Window https://buyerblueprints.com/wp-content/uploads/2016/01/Screen-Shot-2016-01-21-at-11.03.07-AM.png How can we build on product positives? How can we reduce product negatives? What new product ideas based on customer knowns? What do we need to communicate more and better to customer? What do we need to research and discover using data?
  9. 9. GoBeyondTheData.com Managing bias and uncertainty
  10. 10. GoBeyondTheData.com Identifying & managing uncertainty • What is the level of uncertainty? • 60%, 95%, 99%, 99.9% confident • What is the impact of errors? • Type 1 (false +) & Type 2 (false -) • Are there ways to reduce this uncertainty? • What are the tradeoffs of above? • Communicate uncertainty to colleagues and customers fairly but smartly • Uncertainty will exist and it is managing what you known against how well you know it against outcomes ? Type 1 Error False Positive Correct Type 2 Error False Negative Correct Reality True False Measured / Perceived True False
  11. 11. GoBeyondTheData.com Identifying & managing bias • Understand the objective and its value and ask: Who & how are the people impacted? • Understand your data and remember: Garbage in = garbage out! • Does your population relate to people impacted and ask: What is the impact? • Bias may exist but ask: Is the alternative more biased?
  12. 12. GoBeyondTheData.com Balancing System 1 and System 2
  13. 13. GoBeyondTheData.com Shifting System 1 to System 2 thinking, sometimes System 1 is the elephant: instinctual and powerful System 2 is the rider: deliberate and discerning
  14. 14. GoBeyondTheData.com A tale of two data stories
  15. 15. GoBeyondTheData.com Photo: https://www.nhpr.org/post/youre-full-nh-parole-board-tough-talk-can-veer-profane Story: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Risk Scores in Sentencing in Florida • Fort Lauderdale implemented a private company’s risk score to help it determine who was more like for recidivism in sentencing • Model had 137 input questions many of which were asked to defendant that some clearly would proxy asking a person’s race even though race not officially a factor • Private “black box” model • Result was the model appeared to discriminate on race
  16. 16. GoBeyondTheData.com Photo: http://www.chancebailbondsnj.com/is-bail-bonds-information-public-in-new-jersey/ Source: https://www.politico.com/states/f/?id=00000169-df3a-d48d-a57d-dfff7f270000 The New Jersey Bail Bond Model • New Jersey decided that bail was discriminating against poor and minorities and at same time empowering the wealthy but dangerous • Decided an analytical model was a better approach • Communicated broadly and bipartisan brought many together • Result was a complete success with 40% less people in jail two years later with similar levels of people showing up for trial
  17. 17. GoBeyondTheData.com Source: https://www.youtube.com/watch?v=cbtf1oyNg-8 Keep the human side of data in mind so you will avoid this…
  18. 18. GoBeyondTheData.com In the end the human side of data is about understanding people, managing risk, minimizing bias, and aligning incentives so people can make thoughtful data-informed decisions
  19. 19. GoBeyondTheData.com Always continue learning • Understanding Data and Bias: • Books: Weapons of Math Destruction, Dataclysm, Automating Inequality • Understanding Behavioral Science: • Books: Nudge, Influence, Thinking Fast and Slow, Freakonomics • Podcasts: Hidden Brain, Freakonomics, Behavioral Grooves • Online: PeopleScience.maritz.com, BehavioralEconomics.com • MeetUps: Behavioral Grooves, Behavior MN
  20. 20. GoBeyondTheData.com Contact or follow me at: o dave@gobeyondthedata.com | @DaveMathias | @GoBeyondtheData o linkedin.com/in/davemathias1 | about.me/davemathias o Check out Data Able podcast Source: https://memegenerator.net/instance/55474775/fist-pump-baby-you-rock Source: https://pixabay.com/illustrations/question-mark-pile-questions-symbol-2492009/ Questions