By definition, big data cannot yield complicated descriptions of causality. Especially in healthcare. Almost all of our diseases occur in the intersections of systems in the body.
Sentiment: NEGATIVE
Much of what I do in my job is think about whether relationships we see in data are causal, as opposed to just reflecting correlations. It's exactly these issues which come up in evaluating studies in public health.
Any important disease whose causality is murky, and for which treatment is ineffectual, tends to be awash in significance.
Big Data is just that - big. But, it's a term that is largely misunderstood and difficult to explain.
I have developed a unique way of looking at the relationship of the human body to health and disease.
Big data has been used by human beings for a long time - just in bricks-and-mortar applications. Insurance and standardized tests are both examples of big data from before the Internet.
I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.
The more you look into health and health inequalities, you realize that a lot of it is not due to a particular disease - it's really linked to underlying societal issues such as poverty, inequity, lack of access to safe drinking water and housing. And these are all the things we focus on at CARE.
The question of causality is complex. For some philosophers and physicists, time might not exist. And since cause-and-effect reasoning needs the concept of time - of one thing preceding another - the effort to establish causality is a mug's game, an infinite regression of increasingly unanswerable questions.
Therefore in medicine we ought to know the causes of sickness and health.
Big data is going to make us all healthier.