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The Real Limitations of Big Data

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“Every revolution in science—from the Copernican heliocentric model to the rise of statistical and quantum mechanics, from Darwin’s theory of evolution and natural selection to the theory of the gene—has been driven by one and only one thing: access to data.”

That was the eye-opening opening of a keynote address given yesterday by the brilliant John Quackenbush, a professor of biostatistics and computational biology at Dana-Farber Cancer Institute who has a dual professorship at the Harvard T.H. Chan School of Public Health and ample other academic credits after his name.

There is also no question that this digital fuel is driving virtually every transformation in healthcare happening today. Speaking at the MedCity Converge conference in Philadelphia, Quackenbush noted that the average hospital is generating roughly 665 terabytes of data annually, with some four-fifths of it in the unstructured forms of images, video, and doctor’s notes.

But the great limiting factor in harnessing all of this information-feedstock is not a “big data problem,” but rather a “messy data problem.”

In sum, in places where there is tons of potentially useful data to examine, we don’t make it accessible in ways that people actually want to use it. Either the data isn’t easy or intuitive to access or it simply isn’t informative. Or it’s in the wrong format. Or it’s incomplete—or created with incompatible “standards” (of which we seem to have an unlimited, irreconcilable supply). Or it captures just one dimension of a multidimensional realm. (“Biological systems are really complex, adaptive systems with many moving parts, that we’ve only begun to scratch the surface of understanding,” he says.)

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