  Twenty years ago Breiman (2001) called to our attention a significant
cultural division in modeling and data analysis between the stochastic data
models and the algorithmic models. Out of his deep concern that the statistical
community was so deeply and "almost exclusively" committed to the former,
Breiman warned that we were losing our abilities to solve many real-world
problems. Breiman was not the first, and certainly not the only statistician,
to sound the alarm; we may refer to none other than John Tukey who wrote almost
60 years ago "data analysis is intrinsically an empirical science." However,
the bluntness and timeliness of Breiman's article made it uniquely influential.
It prepared us for the data science era and encouraged a new generation of
statisticians to embrace a more broadly defined discipline. Some might argue
that "The cultural division between these two statistical learning frameworks
has been growing at a steady pace in recent years", to quote Mukhopadhyay and
Wang (2020). In this commentary, we focus on some of the positive changes over
the past 20 years and offer an optimistic outlook for our profession.
