Why "Data Mining" is a Misnomer

The term "data mining" literally suggests "mining data" — but strictly speaking, we don't mine data itself; we mine patterns/knowledge from data, much like gold mining extracts gold from rock, not "rock mining."

Reasoning

• If taken literally, "mining coal from rocks" is called coal mining, not "rock mining" — the process is named after what is extracted, not the raw material it comes from.
• Following that convention, the process should really be called "knowledge mining from data" or simply "knowledge mining" — since what we're actually after is the hidden knowledge/patterns, not the data itself (which we already have).
• The formally correct term for the full pipeline is KDD — Knowledge Discovery in Databases — of which "data mining" is just one (pattern-extraction) step, yet the popular term "data mining" is loosely used for the entire process.

In short: "data mining" is a misnomer because it names the process after its raw input (data) rather than its actual output (knowledge/patterns) — the opposite convention of how "mining" terms normally work (e.g., gold mining, coal mining name the output, not the source rock).