9 “Laws” for Data Mining [Be Careful With #5]

9 “Laws” for Data Mining

A Forbes piece on “laws” for data mining, that are equally applicable to data science.

Being Forbes, technology is valuable because it has value for business, not because “everyone is doing it,” “it’s really cool technology,” “it’s a graph,” or “it will bring all humanity to a new plane of existence.”

To be honest, Forbes is a welcome relief some days.

But even Forbes stumbles, as with law #5:

5. There are always patterns: In practice, your data always holds useful information to support decision-making and action.

What? “…your data always holds useful information to support decision-making and action.

That’s as nutty as the “new plane of existence” stuff.

When I say “nutty,” I mean that in a professional sense. The term apohenia was coined to label the tendency to see meaningful patterns in random data. (Yes, that includes your data.) Apophenia.

The original work described the “…onset of delusional thinking in pyschosis.”

No doubt you will find patterns in your data but that the patterns “…holds useful information to support decision-making and action” isn’t a given.

That is an echo of the near fanatic belief that if businesses used big data, they would be more profitable.

Most of the other “laws” are more plausible than #5, but even there, don’t abandon your judgement even if Forbes says that something is so.

I first saw this in a tweet by Data Science Renee.

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