On Big Data and the Human Spirit

Big Data is more human than traditional data. This is in part because traditional data, in terms of scale, is so much more directly manageable. It’s still pretty hands on, especially in the social sciences. This means that over time we’ve gotten very good at working the human element out, or at least minimizing it. Big Data, not necessarily because of its size, but because of its indirectness means we have a great deal less control over where and how it’s collected. Moreover, it’s often collected as the electronic traces humans leave behind as they go about their lives. That’s all they typically are traces—not a data point in the traditional sense. When you conduct your own survey or download the General Social Survey, each data point is an answer to a question. You know what the data point represents. This isn’t so with Big Data because all your data points really are is a clue. And in order to know what it means, you must exercise that quintessential human faculty of sensemaking. Accordingly, modes of sensemaking are as diverse as we are, each with our own unique brand of it rooted in our upbringing, education, experience, chance, and perhaps even our genes. There is a growing library of techniques, or algorithms, available that may assist the analyst in generating sense from Big Data. That means use routines in order to sift through the universe of clues to find the patterns that tie them together into meaning. But while computational and likely embedded math and logic, they are not mathematics or logic. Those are closed systems of self-referencing definitions, defined by their inventors as perfect. The Pythagorean Theorem only works perfectly with imaginary triangles because no perfect triangles are known to exist. On the contrary, algorithms are merely the computational expressions of someone’s own sensemaking faculties—an externalized digitalization of a human mind. When you begin to create your own algorithms, you’ll be uploading a small bit of yourself to carry on your own human uniqueness inside a macroscopic, but highly simplified, virtualization of the real world.

As the science of Big Data matures, I expect to see a resurgence of the humanities and psychological sciences. Big Data’s promise, of course, is that it stands to give us a greater lens on humanity than ever before. But in fulfilling this promise, we will necessary reach advancements in our understanding of the human being as a thinking and feeling being, which may in the end, allow us to transcend entirely the limitations we’ve forever imagined ourselves to be bound.