As Nathan Jurgenson asks at The New Inquiry,

As the name suggests, Big Data is about size. Many proponents of Big Data claim that massive databases can reveal a whole new set of truths because of the unprecedented quantity of information they contain. But the big in Big Data is also used to denote a qualitative difference — that aggregating a certain amount of information makes data pass over into Big Data, a “revolution in knowledge,” to use a phrase thrown around by startups and mass-market social-science books. Operating beyond normal science’s simple accumulation of more information, Big Data is touted as a different sort of knowledge altogether, an Enlightenment for social life reckoned at the scale of masses.

He goes on to note:

As with the similarly inferential sciences like evolutionary psychology and pop-neuroscience, Big Data can be used to give any chosen hypothesis a veneer of science and the unearned authority of numbers. The data is big enough to entertain any story. Big Data has thus spawned an entire industry (“predictive analytics”) as well as reams of academic, corporate, and governmental research; it has also sparked the rise of “data journalism” like that of FiveThirtyEight, Vox, and the other multiplying explainer sites. It has shifted the center of gravity in these fields not merely because of its grand epistemological claims but also because it’s well-financed. Twitter, for example recently announced that it is putting $10 million into a “social machines” Big Data laboratory.

So someone has noticed that this is all utter nonsense? At best.
Big Data was the goal of the study where Facebook experimented with its users’ vulnerable emotional states (here and here).
The biggest problem with Big Data is that it is always a mere average. And the average doesn’t exist as an individual human being.
The average Canadian likes fish and chips but a small percentage of the population would die from anaphylactic shock on eating fish. So it makes no sense to average the results from a trip to the chip shop.
A more nuanced problem is the collection of data without context. Let’s say a movie trailer site has five million viewers. How many are there because they want to see the movie vs. how many are there because a big star was born in, say, Quebec or Queensland? Or a particular still makes great wallpaper?
The myth of big data is that it is a magical way around relating to people in order to understand them. Like all magic it’ll be great - until they get to the real life performance part. See also: Will the rise of Big Data be the death of politics?
Here’s an argument for big data:
 

Denyse O’Leary is a Canadian journalist, author, and blogger.