Steve Lohr writes a reflection on the promises of Big Data, citing increasing buzz, yet also its initial big failure:
“Many of the Big Data techniques of math modeling, predictive algorithms and artificial intelligence software were first widely applied on Wall Street.” And what happened there we all know.
A chief scientist from an ad-targeting startup:
“You can fool yourself with data like you can’t with anything else. I fear a Big Data bubble.”
“A major part of managing Big Data projects, he says, is asking the right questions: How do you define the problem? What data do you need? Where does it come from? What are the assumptions behind the model that the data is fed into? How is the model different from reality?”
“Models do not just predict, but they can make things happen,” says Rachel Schutt, who taught a data science course this year at Columbia.
A concern is that “the algorithms that are shaping my digital world are too simple-minded, rather than too smart.”
Read at The New York Times.