Don’t build a data department store.
Redefining data science skill, biased policy decisions, and data strategy.
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 […]
Machines Gone Wild! + Can Microlearning improve Data Science training?
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 […]
The Cardinal Sin of data science, and cognitive bias in 5 easy steps.
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. — […]
1. Confusing correlation with causation is not the Cardinal Sin of data science, say Gregory Piatetsky (@kdnuggets) and Anmol Rajpurohit (@hey_anmol): It’s overfitting. Oftentimes, researchers “test numerous hypotheses without proper statistical control, until they happen to find something interesting and report it. Not surprisingly, next time the effect, which was (at least partly) due to […]