What is the difference between a data-driven model and an empirical model?
They are the same thing, but it is important you understand differences in process between the statistics school of thought and the data mining mining school of thought. In data mining we impute and approximate relationships, where as some relationships are certain and have precise computations. Obviously, it is always better to compute something precisely than to approximate a relationship.

An example in finance: Bond prices have precise computations - they follow a certain relationship. The are simply discounted cashflows. Other types of securities might require inference because the absolute relationship is unknown, likely because either the cashflow or the interest rate is not known.

Empirical means based on evidence or observations. You are testing something. It might be possible to generate data per a simulation - this is common with genetic models - that is not observed in real life. In that instance, your data driven model would not be empirical.


  em·pir·i·cal
  /əmˈpirik(ə)l/
  | adjective | 
  based on, concerned with, or verifiable by observation or experience rather > than theory or pure logic.
  "they provided considerable empirical evidence to support their argument"


Data mining versus statistics:

Normally what you try to do in statistics (as opposed to data science / data mining) is you come up with the definate mathematical model first, the you test it empirically. If the relationship is statistically significant, than it is thought to be a good model.

In data mining you seek out the inferred relationships first and have no manner of testing them.