Redefining data science skill, biased policy decisions, and data strategy.
decision bias in food-poverty policy

1. Biased analysis → Misunderstood cause-effect In Biased Ways We Look at Poverty, Adam Ozimek reviews new evidence suggesting that food deserts aren’t the problem, behavior is. His Modeled Behavior (Forbes) piece asks why the food desert theory got so much play, claiming “I would argue it reflects liberal bias when it comes to understanding […]

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Machines Gone Wild! + Can Microlearning improve Data Science training?
boston-dynamics-spot-mini

  1. Machines Gone Wild → Digital trust gap Last year I spoke with the CEO of a smallish healthcare firm. He had not embraced sophisticated analytics or machine-made decision making, with no comfort level for ‘what information he could believe’. He did, however, trust the CFO’s recommendations. Evidently, these sentiments are widely shared. — […]

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Analytics translators wanted, algorithm vs. human, and winning with diversity.
data translators

1. Hire analytics translators → Keep data scientists happy An emerging role – what some call the Analytics Translator – is offloading burden from data scientists, while helping business executives get better value from their technology investments. A recent HBR piece explains You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics […]

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Cognitive bias in algorithms, baseball analytics denied, and soft skills ROI.
mexico-analytics-baseball-nytimes

1. Recognize bias → Create better algorithms Can we humans better recognize our biases before we turn the machines loose, fully automating them? Here’s a sample of recent caveats about decision-making fails: While improving some lives, we’re making others worse. Yikes. From HBR, Hiring algorithms are not neutral. If you set up your resume-screening algorithm […]

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Value your gut, plus the perils of decision fatigue.
man tying necktie

1. “A gut is a personal, nontransferable attribute, which increases the value of a good one.” This classic from Harvard Business Review recaps how policy makers have historically made big decisions. It’s never just about the data. A Brief History of Decision Making. 2. A reminder to look for the nonobvious. This analysis examines differences […]

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“Big E” vs. “little e” evidence, and probabilistic thinking.
moneyball brad pitt photo

1. It’s tempting to think there’s a hierarchy for data: That evidence from high-quality experiments is on top at Level 1, and other research findings follow thereafter. But even in healthcare – the gold standard for the “gold standard” – it’s not that simple, says NICE in The NICE Way: Lessons for Social Policy and […]

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Underwriters + algorithms, avoiding bad choices, and evidence for rare illness.
heart rate monitor

1. Underwriters + algorithms = Best of both worlds. We hear so much about machine automation replacing humans. But several promising applications are designed to supplement complex human knowledge and guide decisions, not replace them: Think primary care physicians, policy makers, or underwriters. Leslie Scism writes in the Wall Street Journal that AIG “pairs its […]

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Algorithm reluctance, home-visit showdown, and the problem with wearables.
kitten on keyboard awww!

Yikes, evidence-based decisions are taking on water. Decision makers still resist handing the car keys to others, even when machines make better predictions. And government agencies continue to, ahem, struggle with making evidence-based policy.  — Tracy Altman, editor 1. Evidence-based home visit program loses funding. The evidence base has developed over 30+ years. Advocates for home visit programs […]

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False dichotomy: Data-driven vs. gut-feel
photo of people grouped together

Smart decision-making is more complicated than becoming ‘data-driven’, whatever that means exactly. We know people can make better decisions if they consider relevant evidence, and that process is getting easier. But too often tech enthusiasts dismiss people’s decisions as based on gut feel, as if data will save us from ourselves. Let’s put an end to […]

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Equity crowdfunding algorithms, decision-making competitions, and statistical wild geese.

1. CircleUp uses algorithm to evaluate consumer startups. Recently we wrote about #fintech startups who are challenging traditional consumer lending models. CircleUp is doing something similar to connect investors with non-tech consumer startups (food, cosmetics, recreation). It’s not yet a robo adviser for automated investing, but they do use machine learning to remove drudgery from […]

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