I've had similar thoughts when it comes to recognizing the underlying (potential) simplicity of a phenomena of interest.

For example, consider a toy experiment where you take dozens of high speed sensors pointed a rig in order to study basic spring dynamics (i.e. Hooke's law).

You could apply "big data analytics" or ML methods to break apart the dynamics to predict future positions past on past positions.

But hopefully, somewhere along the way, you have some means of recognizing that it is a simple 1D phenomena and that most of the volume of data that you collected is fairly pointless for that goal.