It is difficult to overstate the significant impact of first theory, and later computer simulations, on our understanding of soft matter. Bringing soft matter to the fourth paradigm of science (i.e., data-driven methods) will require the tackling of several outstanding challenges. The ongoing developments of machine learning will hopefully continue to naturally evolve from hard condensed matter to soft matter, thereby addressing the needs to model configurational entropy. We foresee that these technical hurdles may help usher soft matter in a new era, where poor scale separation can be efficiently addressed, and insight can be gained for phenomena that are too complex for traditional methods.