At present rate, Big Data grow in leaps and bounds. Trying to harness the potential of the vast amount of data remains untamed. Unless a technology breakthrough, we are generating more data than we know what to do with them.
Moreover, data are raw inputs. They are just numbers sans meanings. To glean insights from the sea of data requires human intelligence (with the aid of computer) to distill meaningful patterns. Operative word here is “meaningful.”
In analyses, given enough data, statistical correlations happen. Even if they are meaningless. For example, believe it or not, correlation between how much chicken we eat and the amount of of crude oil we import exists. Such correlation is called spurious correlation.
In our efforts to drill information out of the big data, beware the false connections. Remember that data do not create meanings. We do. Here are 15 examples of why we should be selective.
Another example I heard is between shoe size and one’s IQ. Have examples of your own?