That's the headline of a speculative new report by James Glanz in The New York Times, who summarized the views of several leading economists who say they see no evidence of any bump in economic growth as a result of the rise of "big data."
The main criticism of big data as an economic engine is that it doesn't appear to be matching the productivity surge that came with other technology revolutions, such as the development of the electrical grid or gasoline engine in the late 19th-early 20th century. As Robert J. Gordon, professor of economics at Northwestern University, put it: “Gasoline made from oil made possible a transportation revolution as cars replaced horses and as commercial air transportation replaced railroads. If anybody thinks that personal data are comparable to real oil and real vehicles, they don’t appreciate the realities of the last century.”
Since big data has come on the scene in the middle of the 2000s decade, economic growth and productivity has either deteriorated or been stagnant, Glanz points out in his article.
Joel Waldfogel, an economist at the University of Minnesota, also points out that in the areas where big data is thriving financially, it is the result of cannibalizing existing businesses. Areas in which big data is "hot" include advertising, media, music and retailing businesses. “One falls, one rises — it’s pretty clear the digital kind is a substitute to the physical kind. So it would be crazy to count the whole rise in digital as a net addition to the economy.”
The counter-argument is that we are still in the early stages of the big data revolution, and many of the innovations and disruptions that it will make possible are still being learned, and not enough people have learned or have graduated with the analytical skills capable of making things happen.
Glanz's article points to the lack of impact on macro-economic growth that can be attributed to big data. However, on a more micro-economic level, there is is evidence of greater efficiencies and better decision-making on the part of individual enterprises. For example, he quotes Josh Marks, CEO of masFlight, who helps airlines employ big data analytics to cut fuel costs. Big data analytics is also an effective tool that cuts the waste out of direct marketing. Ultimately, enterprises not employing big data risk being left behind.
This seems to be the time to pile on the big data promise. In another recent article, John Burn-Murdoch of The Guardian even speculates that big data algorithms may be significantly magnifying human error. The speed at which data and machine-based analysis has the potential to bring down markets in a matter of minutes, or inject bias into selections for jobs or treatment regimens.
I recently discussed the need for human oversight and critical review of big data processes in a conference hosted at the Stevens Institute of Technology (a brief summary posted here). As enterprises increasingly rely on big data for decision-making, they run the risk of having managers and executives not trained in statistics making bet-the-business decisions based on data of unknown quality originating from unvetted sources. As I mentioned in the talk, data analysts and scientists can write the algorithms that extract the data, but they aren’t necessarily in a position to understand the business implications.
To move forward, it will take a lot of collaboration and enlightened management to effectively grow organizations on big data. IT professionals and data analysts alone cannot advance a business into new markets, or develop new solutions to meet customers' needs. It takes new ways of thinking at looking at business problems, with people trained in non-tech disciplines. It means asking questions that have never been asked before. As The New York Times article reminds us, simply dropping big data on top of businesses won't get us anywhere. Sort of like attempting to attach a gasoline engine to a horse and buggy.
This post was originally published on Smartplanet.com