  We provide an exact deterministic reformulation for data-driven chance
constrained programs over Wasserstein balls. For individual chance constraints
as well as joint chance constraints with right-hand side uncertainty, our
reformulation amounts to a mixed-integer conic program. In the special case of
a Wasserstein ball with the $1$-norm or the $\infty$-norm, the cone is the
nonnegative orthant, and the chance constrained program can be reformulated as
a mixed-integer linear program. Our reformulation compares favourably to
several state-of-the-art data-driven optimization schemes in our numerical
experiments.
