Why does data classification fail?

The most common reason data classification fails: Creating explicit categories and then never revisiting them. The point of making categories explicit is not to freeze them — it's to make them visible so they can be evaluated and improved. If you define your categories once and treat them as permanent, you've just traded one kind of rigidity (implicit, invisible) for another (explicit, fossilized). Explicit categories need periodic review.

The fix: Pick one domain where you currently sort things without written criteria — your email folders, your project labels, your bookmarks, your reading list. Write down the actual categories you use. Then, for each category, write a one-sentence definition that would let someone else sort items the same way you do. Where you can't write the definition, you've found an implicit category that needs to be made explicit.

The underlying principle is straightforward: When you name and define your categories you can evaluate and improve them.