A good illustration of the limits of big data and the risks of confusing correlation with causation. But bid data and correlation can help us ask more informed questions:
The important distinction is of intent. In the benign explanation, a slowdown of old phones is not a specific goal, but merely a side effect of optimizing the operating system for newer hardware. Data on search frequency would not allow us to infer intent. No matter how suggestive, this data alone doesn’t allow you to determine conclusively whether my phone is actually slower and, if so, why.
In this way, the whole exercise perfectly encapsulates the advantages and limitations of “big data.” First, 20 years ago, determining whether many people experienced a slowdown would have required an expensive survey to sample just a few hundred consumers. Now, data from Google Trends, if used correctly, allows us to see what hundreds of millions of users are searching for, and, in theory, what they are feeling or thinking. Twitter, Instagram and Facebook all create what is evocatively called the “digital exhaust,” allowing us to uncover macro patterns like this one.
Second, these new kinds of data create an intimacy between the individual and the collective. Even for our most idiosyncratic feelings, such data can help us see that we aren’t alone. In minutes, I could see that many shared my frustration. Even if you’ve never gathered the data yourself, you’ve probably sensed something similar when Google’s autocomplete feature automatically suggests the next few words you are going to type: “Oh, lots of people want to know that, too?”
Finally, we see a big limitation: This data reveals only correlations, not conclusions. We are left with at least two different interpretations of the sudden spike in “iPhone slow” queries, one conspiratorial and one benign. It is tempting to say, “See, this is why big data is useless.” But that is too trite. Correlations are what motivate us to look further. If all that big data does – and it surely does more – is to point out interesting correlations whose fundamental reasons we unpack in other ways, that already has immense value.
And if those correlations allow conspiracy theorists to become that much more smug, that’s a small price to pay.
Professor goes to big data to figure out if Apple slows down old iPhones when new ones come out
Multicultural Meanderings 