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Before reading Mayer-Schonberger’s piece on big data and attending the Professor Chun’s last lecture, I had very little understanding about the subject despite coming across it very often. What was particularly surprising to me was how the alarming emergence of vast amounts of data (of things what weren’t measured, stored and analyzed before) allow us to move away from causality and to focus on correlation. I’ve taken a couple of classes in the economics department which deal directly with trying to prove causality, which I’ve always found it a bit ridiculous since it constrains us to assuming the relationship between two (or more) variables is not dependent on a variety of outside factors. I found Mayer-Schonbergerger’s argument extremely relevant as it made me realize the way in which our society is moving away from the why and towards the what. Since we’re looking at vastly more amounts of data, we’re concentrating on probabilities and relationships, instead of solely on why something causes another. As the author points out, the reason behind the fluctuation in airline ticket prices over time is irrelevant if we’re able to predict when to buy the cheapest ticket. But, if the amount of stored information grows four times faster than the world economy, how do we sort between the correlations? Which ones actually matter?