Big Data Needs Small Data for Best Insights!

Dear Commons Community,

On Saturday, Alex Peysakhovich, a behavioral economist and data scientist at Facebook, and Seth Stephens-Davidowitz, an economist, had an op-ed piece in the New York Times on what big data needs to be of benefit- it needs small data as collected in surveys. Using a number of examples such as individual fitness regimens and baseball, the authors provide interesting commentary on the big data approach. They consider that too many users of big data become so obsessed with it that they lose the big picture and purpose. They provide several examples including public education’s use of test scores.

“Thomas Kane, a professor of education at Harvard, told us, “School districts realize they shouldn’t be focusing solely on test scores.”

A three-year study by the Bill and Melinda Gates Foundation bears out the value of both big and small data. The authors analyzed value-added models, student surveys and teacher observations. They tested how to best predict student outcomes on both traditional state tests and more cognitively demanding challenges in math and English. When they put the three measures together into a composite score, they got the best results. “Each measure adds something of value,” the report concluded.

As at Facebook and in baseball front offices, small data can find holes in the big data. If a teacher raises her students’ test scores but students say she wastes a lot of time, and outside observers rank her poorly, this raises big questions. Conversely, if a teacher does not improve test scores but students say she inspires them and principals think she is imparting profound lessons, we may give her the benefit of the doubt. Most important, while big data can tell us whether certain teachers are helping their students, small data gives us the best hope to answer a crucial question: How are they doing it?”

Peysakhovich and Stephens-Davidowitz have provided a wealth of insight in this one small op-ed piece.

Tony

 

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