Here’s a thought provoking post from Popular Science about how we need to think in dealing with the upcoming ocean of data. Below is one of the interesting segments from the post, as provided by Bill Anderson of the School of Information at the University of Texas at Austin and associate editor of the CODATA Data Science Journal:
Take the number 37, Anderson says. Other than stating a numerical order, it means little on its own. But with some more information — 37 degrees Celsius, for instance — it can take on more meaning. Now give it some context: 37 degrees C is normal body temperature. Now 37 represents something useful, something a doctor or researcher could use, and it becomes a piece of knowledge that could comfort a patient or answer a question.
Anderson says there really is no such thing as “raw data”, which is generally right. What’s really important to understand is that data itself isn’t important without a question we’re are trying to answer with the data. A single piece of data has information about a lot of things, but only if we are asking the right questions of that data.
For example, with the number 37 above, we might want to ask if it’s hot outside. We first need some expectation for what the data would look like if it was hot, and what it might look like if it wasn’t. We don’t really need to know the units of measurement (Celsius, for example), but we do need to know if 37 is normal (although the units help you figure that out), and “hot” means that the data would be larger than normal.
With the right questions (and some questions are easier to answer with data than others…), we can make the data tell us what we want to know.
Here’s the link to the Popular Science post…
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