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| April 02, 2012
In my previous post on this topic, I suggested that big data runs the risk of inducing “marketing myopia." My basic point was that the patterns yielded by analysis of big databases are just as subject to the biases and beliefs of the analyst and decision maker, as any other data set. But there is no doubt that many people find the promise of big data compelling. Why is that?
To my mind, the allure of big data is simple: big data promises certainty. If you are certain of the finding’s veracity, you can act without hesitation. Implicit in that certainty, is a faith in the scientific method that the analytic techniques applied to a problem are unbiased and impartial. A Merriam-Webster definition of scientific method is:
...principles and procedures for the systematic pursuit of knowledge involving the recognition and formulation of a problem, the collection of data through observation and experiment, and the formulation and testing of hypotheses.
But therein is the problem. All science is directed by beliefs; we just dress them up and call them hypotheses. Put your mind to it, and you can prove pretty much anything to your own satisfaction (if not that of everyone else’s). Unless you start your analysis with a set of hypotheses based on good evidence of how the world works, then people really think you risk skewing the analysis and misinterpreting the results.
A recent article in The New York Times on big data, also points to the potential of misinterpretation:
These models, like metaphors in literature, are explanatory simplifications. They are useful for understanding, but they have their limits. A model might spot a correlation and draw a statistical inference that is unfair or discriminatory, based on online searches, affecting the products, bank loans and health insurance a person is offered, privacy advocates warn.
Despite the caveats, there seems to be no turning back. Data is in the driver’s seat. It’s there, it’s useful and it’s valuable, even hip.
And I agree with that conclusion. Big data can be incredibly useful and valuable, but only if used wisely. And that is where I believe little data can be of help. Little data – qualitative, survey and neuro research techniques – can provide a useful frame of reference for the findings from big data. They can be thought of as providing independent insight into what motivates people to behave the way they do, counteracting the biases and beliefs that analysts and decision makers bring to big data.
Of course, the catch-22 is that little data is also subject to bias. But when the findings from two completely different approaches converge, we can be relatively certain that the results are valid and meaningful, and that they provide a basis on which to act with confidence.
So what do you think? Does big data need the help of little data? Or do new techniques obviate the need for independent verification? Please share your thoughts.