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analytics & machine learning

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shopper looking thru department store merchandise

Don’t build a data department store.

16 January 2019By
To paraphrase Raymond Chandler, too many projects deliver department store data: The most of everything but the best of nothing. Enterprise AI and analytics developers must avoid the mistake of underserving people by overengineering solutions. Designers and decision makers need straightforward tools to make them better, to save time and facilitate their best work. They...
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quantamentalist, man holding playing card

Are you quantamental? Should you be?

13 November 2018By
Quantamental is an investment strategy combining quantitative and fundamental methods. Data and algorithms have “prompted many traditional fundamentals-centered discretionary funds to integrate data-driven tools in day-to-day decision-making.” MarketWatch says the quantamental merger of computing power and human expertise is investing’s next frontier. Example: Active trading based on a particular blend of conventional balance sheets and...
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Building trust in the decision process

Evaluate your decision process separately from your outcomes.

12 August 2018By
How we decide is no less important than the data we use to decide. People are recognizing this and creating innovative ways to blend what, why, and how into decision processes. 1. Apply behavioral science → Less cognitive bias McKinsey experts offer excellent insight into Behavioral science in business: Nudging, debiasing, and managing the irrational...
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fox

What makes us trust analytics, and how to argue.

7 June 2018By
1. Prior experience → More trust In Trustworthy Data Analysis, Roger Peng gives an elegant description of how he evaluates analytics presentations, and what factors influence his trust level. First, he imagines analytical work in three buckets: A (the material presented), B (work done but not presented), and C (analytical work not done). “We can...
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Weaponizing KPIs and debiasing decision algorithms.

31 May 2018By
1. Vigilance → Better algorithms “Eliminating bias… requires constant vigilance on the part of not only data scientists but up and down the corporate ranks.” In an insightful Information Week commentary, James Kobielus (@jameskobielus) considers the importance of Debiasing Our Statistical Algorithms Down to Their Roots. “Rest assured that AI, machine learning, and other statistical...
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Debiasing is painful, why analytics fail, and health app evidence.

15 May 2018By
1. Debiasing → Better decisions Debiasing is hard work, requiring honest communication and occasional stomach upset. But it gets easier and can become a habit, especially if people have a systematic way of checking their decisions for bias. In this podcast and interview transcript, Nobel-winning Richard Thaler explains several practical ways to debias decisions. First,...
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Success theater, leaky tech pipeline, teacher bias, network meta-analysis.

9 March 2018By
1. Biased instructor response → Students shut out Definitely not awesome. Stanford’s Center for Education Policy Analysis reports Bias in Online Classes: Evidence from a Field Experiment. “We find that instructors are 94% more likely to respond to forum posts by white male students. In contrast, we do not find general evidence of biases in...
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decision bias in food-poverty policy

Redefining data science skill, biased policy decisions, and data strategy.

28 February 2018By
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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boston-dynamics-spot-mini

Machines Gone Wild! + Can Microlearning improve Data Science training?

21 February 2018By
1. Machines Gone Wild → Digital trust gapLast 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. — Tracy A...
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Translators at IFLA 2010

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

15 February 2018By
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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Museum musings.

Pondering the places where people interact with artificial intelligence: Collaboration on evidence-based decision-making, automation of data-driven processes, machine learning, things like that.

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