Social Media and Nonprofits: Measurement

The bubble that concerns Ms. Perlich is not so much a surge of investment, with new companies forming and then failing in large numbers. That’s capitalism, she says. She is worried about a rush of people calling themselves “data scientists,” doing poor work and giving the field a bad name.

Indeed, Big Data does seem to be facing a work-force bottleneck.

“We can’t grow the skills fast enough,” says Ms. Perlich, who formerly worked for I.B.M. Watson Labs and is an adjunct professor at the Stern School of Business at New York University.

A report last year by the McKinsey Global Institute, the research arm of the consulting firm, projected that the United States needed 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired.

Thomas H. Davenport, a visiting professor at the Harvard Business School, is writing a book called “Keeping Up With the Quants” to help managers cope with the Big Data challenge. 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?