In his young career, Jeffrey Hammerbacher has been a scout on the frontiers of the data economy.
In 2005, Hammerbacher, then a freshly minted Harvard graduate, did what many math and computing whizzes did. He joined Wall Street as a “quant” to build models for complex financial products.
Looking for a better use for his skills, Hammerbacher departed to Silicon Valley less than a year later and joined Facebook. He started a team that began to mine the vast amounts of social-network data Facebook was collecting for insights on how to tweak the service and target ads. He called himself and his co-workers “data scientists,” a term that has since become the hottest of job categories.
Facebook was a fabulous Petri dish for data science. Yet after 2½ years, Hammerbacher decided it was time to move on, beyond social networks and Internet advertising. He became a founder of Cloudera, a startup that makes software tools for data scientists.
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Then, starting last summer, Hammerbacher, now 30, embarked on a very different professional path. He joined the Mount Sinai School of Medicine in New York as an assistant professor, exploring genetic and other medical data in search of breakthroughs in disease modeling and treatment. The goal, Hammerbacher said, is “to turn medicine into the land of the quants.”
The story is the same in one field after another, in science, politics, crime prevention, public health, sports, energy and advertising. All are being transformed by data-driven discovery and decision-making. The pioneering consumer Internet companies, like Google, Facebook and Amazon, were just the start, experts say.
Today, data tools and techniques are used for tasks as varied as predicting neighborhood blocks where crimes are most likely to occur and injecting intelligence into hulking industrial machines, like electrical power generators.
Big Data is the shorthand label for the phenomenon, which embraces technology, decision-making and public policy. Supplying the technology is a fast-growing market, increasing at more than 30 percent a year and likely to reach $24 billion by 2016, according to research firm IDC. All the major technology companies, and a host of startups, are aggressively pursuing the business.
Demand is brisk for people with data skills. The McKinsey Global Institute projects that the United States needs 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired, by 2020.
Yet the surveillance potential of Big Data, with every click stream, physical movement and commercial transaction monitored and analyzed, would strain the imagination of George Orwell. So what will be society’s ground rules for the collection and use of data? How do we weigh the trade-offs involving privacy, commerce and security?
Those issues are just beginning to be addressed. The debate surrounding the recent disclosure that the National Security Agency has been secretly stockpiling telephone call logs of Americans and poring through email and other data from major Internet companies is merely an early round.
Big Data is a vague term, used loosely, if often, these days. First, it is a bundle of technologies. Second, it is a potential revolution in measurement. And third, it is a point of view, or philosophy, about how decisions will be — and perhaps should be — made in the future.
The bundle of technologies is partly all the old and new sources of data — Web pages, browsing habits, sensor signals, social media, GPS location data from smartphones, genomic information and surveillance videos. The data surge just keeps rising, doubling in volume every two years.
The increasing volume and variety of data, combined with smart software, may open the door to what some call a revolution in measurement — the digital equivalent of the telescope or the microscope, both of which made it possible to see and measure things as never before.
Data-driven insights, experts say, will fuel a shift in decision-making. Decisions of all kinds, they say, will increasingly be made on the basis of data and analysis rather than experience and intuition — more science and less gut feel.
Big Data, its proponents insist, will be the next big trend in management. Erik Brynjolfsson, director of the MIT Center for Digital Business, cites the familiar business truism, “You can’t manage what you can’t measure.” Big Data, he said, will “replace ideas, paradigms, organizations and ways of thinking about the world.”
Discrimination by statistical inference is a real risk in the Big Data world, as some personal data trails suggest a correlation that may be wrong.
David Vladeck, a former senior Federal Trade Commission official and a professor of law at Georgetown University, offers this example:
Imagine spending a few hours looking online for information on deep-fat fryers. You could be looking for a gift or researching a report for cooking school.
But to a data miner, tracking your online viewing, this hunt could be read as a telltale sign of an unhealthy habit — a data-based prediction that could make its way to a health insurer or potential employer.
And, again, the surveillance potential of Big Data technology, if it runs amok, is scary.
One glimpse of the potential payoff can be seen at the Mount Sinai Medical Center, in the work being pursued by the group Hammerbacher has joined.
The 100-member team at the Icahn Institute for Genomics and Multiscale Biology is headed by Eric Schadt, a leading researcher in genomics and biomathematics. Schadt joined Mount Sinai less than two years ago, lured by ample financing and the promise that his group’s work would not be research in isolation but part of the medical center in treating patients.
The technology makes it possible to explore how the minute ingredients of biology and the environment influence each other in individual humans — and personalize treatment. People with similar genetic traits, Schadt notes, often have very different health outcomes. Chronic ailments like cancer, heart disease and Alzheimer’s are not caused by single genes, he said, but are “complex, networked disorders.”
The Mount Sinai researchers, Schadt said, intend to combine genetic information with the medical histories — weight, age, gender, vital signs, tobacco use, toxic exposure and other data — to build more sophisticated models of biology and health outcomes.
Schadt recruited Hammerbacher, an overture that coincided with Hammerbacher’s research into where next to best apply his skills. He describes his career as a matter of “following the smartest people to find the best problem.” Health care, in his view, is “the best problem by far,” where his talents could do the most good.