不管实际情况如何，“大数据”仍然是个新兴领域。对各行各业来说，要从自己的数据中心及美国政府的数据库新开放的数据宝藏中发掘各种商业机会还为时尚早。伯克利大学（Berkeley College）教授达尔尚•德赛称，看看“谷歌趋势”（Google Trends）就会发现——“大数据”直到2011年还很少被用作搜索词，直到2012年才真正开始流行。
德赛是在一个研讨会上发表这些观点的。这个研讨会名为“理解大数据”，主要由学者参加，是由纽约大学城市科学与进步中心（Center for Urban Science and Progress）的康斯坦丁•康托可斯塔组织的，与会者还有来自普拉特学院（the Pratt Institute）、圣弗朗西斯学院（St. Francis College）和纽约技术学院（the NYC College of Technology）的专家。它是“科技三角U”（Tech Triangle U）的一个组成部分，后者是由布鲁克林科技三角（Brooklyn Tech Triangle，纽约新兴科技创新中心——译注）发起的一个项目，旨在推动布鲁克林科技界和学术圈建立联系。
每位与会专家都就大数据未来将对自己的研究领域产生什么样的影响阐述了自己的看法。康托可斯塔领导着一支开展建筑信息学研究的团队，主要研究如何用数据科学来分析城市能源消费。他在会上预先透露了对《第84本地法》（Local Law 84）推行后一些公开数据的研究成果。这个法案要求纽约市大型建筑必须披露对能源和水的利用情况，但它只要求公布相关数据——并没有要求这些建筑改变能源运用方式。不过他表示，它确实为这些建筑做出改变带来了两种压力。
Big data will save the world, they say. It will change the way we do business, they say. It could tell us things we didn't know we didn't know, they say.
Some of that might be true and some of it may not -- but the point is that the hype around the term "big data" is thick enough to require a chainsaw to cut through. The technology is promising; the semantics are another story.
Whatever the reality, "big data" remains a nascent field. Businesses are a long way from seeing all the opportunities that could come of the newly opened troves of data in their data centers and in those of the U.S. government. Just look at Google Trends, Berkeley College professor Darshan Desai said -- "big data" was barely used as a search term until about 2011 and didn't quite take off in popularity until 2012.
Still, "I'm a big fan of the new technology," Desai said. "I believe there are ways to make lives better."
Desai made her remarks as part of a panel of academics led by Constantine Kontokosta of the NYU Center for Urban Science and Progress. The panel, called "Making Sense of Big Data" also featured scholars from the Pratt Institute, St. Francis College, and the NYC College of Technology. It was part of Tech Triangle U, an initiative by the Brooklyn Tech Triangle to connect the technology and academic communities in Brooklyn.
Each scholar demonstrated how they thought big data would impact their fields of study. Kontokosta, who leads a building informatics research group that focuses on the application of data science to the analysis of urban energy consumption, gave a preview of some of his work on data released under Local Law 84, an energy and water usage disclosure law for large buildings in New York City. The law itself is pure data -- nothing in it forces buildings to change their practices around energy. But it does create two kinds of pressure on buildings to change, he said.
The first is competitive pressure, as buildings monitor their peers and begin to compete for residents on efficiency. "We can use this to compare how much energy use is varying across the city," Kontokosta said, "but also how much the difference is in how much people are paying."
Which means companies like Radiator Labs, which offers a Wi-Fi-enabled product that addresses uneven steam heating in old buildings, may start to see property managers taking interest in their wares as benchmark data starts to show that they aren't keeping up with peer structures.
The second pressure will be from the top, Kontokosta said. The New York utility company Consolidated Edison is already working to manage peak demand by paying large customers to voluntarily reduce energy consumption during peak demand. With more usage data, big utilities in energy benchmarking cities may be able to develop better demand reduction strategies. Those could involve partnerships with companies like EnergyHub to recruit customers into voluntary programs where home thermostats can be adjusted, by the utility and over the Internet, a few degrees when demand is spiking and a blackout is possible.
But assembling a bunch of data isn't enough, said Emily Horowitz of St. Francis College. It's necessary but not sufficient to make change for the better. "The big problem with big data, in my view, is you can see all kinds of things," she said. Researchers may be able to put many different kinds of data side by side for the first time thanks to big data technology, but that doesn't mean that they have revealed anything. Correlations, sure -- but proving causation is much more difficult.
The panelists, which also included Pratt's Jessie Braden and CUNY's Jason Montgomery, did not address the questions of when and how big data will deliver appreciable differences in consumer services. Many new technology companies have claimed that more data leads to better results -- e-commerce personalization is one such area; if you rate more products on a retailer's website, you are told that it will lead to better product suggestions on that site -- but it's unclear how true that statement actually is.
Nonetheless, companies offering data services are making a strong showing helping businesses aggregate and make use of the data they're already collecting. And data brokers are collecting an alarming array of information about who we are and what we (presumably) want.
But the devil is in the details, and making sense of all that information is an entirely different proposition from merely accessing it, the panelists agreed. Big data may be increasingly popular, but it's still looking for its first big hit.