Let us classify the state of knowledge into four simple categories: what do we know (known knowns)? What are the limitations of what we know (known unknowns)? What is our degree of certainty about our knowledge (unknown knowns)? What are we unconsciously not exploring (unknown unknowns)? Most research focuses on moving gradually the known unknowns to known knowns with repeated acceptation or refutation of hypotheses.
My question is about the scientific method. Research in my field traditionally relies on hypothesis testing or development, in a deductive or inductive approach. A scientific method relying on hypothesis testing or development is particularly efficient at choosing between possible processes that could explain a phenomena (i.e. to explore the known unknowns), but would fail to scrutinize new areas, the unknown unknowns, whose exploration may lead to totally unexpected scientific discoveries. A way to probe the unknown unknowns (I guess) is to use a data-driven scientific method. The argument is that, with large volume of data describing a situation and data-driven techniques, it becomes possible to discover patterns in the data and the hypotheses that follow. This is a kind of abductive reasoning, where the data-driven algorithm is used to generate hypotheses.
My question is whether a data-driven scientific method can also be useful to explore the known unknowns, as a replacement of the dominant use of hypothesis testing and development? More generally, is it possible to use data-driven algorithms (e.g. machine learning) to produce scientific knowledge, or should they be used only to generate hypotheses from data?