“Big data” is all the rage.
As if all the knowledge of the universe is somehow encoded there, just waiting to be mapped like the genome.
Don’t get me wrong, big data is very exciting. Our social science models are more accurate, our marketing more creepy. Big data is helping us understand the world just a little bit better. And that is fantastic.
But perhaps there’s something more valuable to be gleaned from all this big data.
As Brooke Foucault Welles, Assistant Professor of Communication Studies at Northeastern, argues, “honoring the experiences of extreme statistical minorities represents one of Big Data’s most exciting scientific possibilities.”
At last we have datasets large enough to capture the “outlier” experience, large enough to truly explore and understand the “outlier” experience.
Why is this important?
As Welles describes:
When women and minorities are excluded as subjects of basic social science research, there is a tenancy to identify majoring experiences as “normal,” and discuss minority experiences in terms of how they deviate from those norms. In doing so, women, minorities, and the statistically underrepresented are problematically written into the margins of social science, discussed only in terms of their differences, or else excluded altogether.
There has been much coverage of how medical trials are largely unrepresentative of women – with one study finding less than one-quarter of all patients enrolled in 46 examined clinical trials were women.
This gender bias has been shown to be detrimental, with Anaesthetist Anita Holdcroft arguing in the Journal of the Royal Society of Medicine, that the “evidence basis of medicine may be fundamentally flawed because there is an ongoing failure of research tools to include sex differences in study design and analysis.”
We should insist on parity in medical research and we should settle for nothing else when it comes to the social sciences.
People who deviate from the so-called norm – whether women, people of color, or just those that experience the world differently – these people aren’t outliers. They aren’t anomalies to be polished away from immaculate datasets.
They are the rare pearls you can only find by looking.
And “big data” provides an emerging venue for finding them.