  Balanced truncation (BT) is a model reduction method that utilizes a
coordinate transformation to retain eigen-directions that are highly observable
and reachable. To address realizability and scalability of BT applied to highly
stiff and lightly-damped systems, a non-intrusive data-driven method is
developed for balancing discrete-time systems via the eigensystem realization
algorithm (ERA). The advantage of ERA for balancing transformation makes
full-state outputs tractable. Further, ERA enables balancing despite stiffness,
by eliminating computation of balancing modes and adjoint simulations. As a
demonstrative example, we create balanced ROMs for a one-dimensional reactive
flow with pressure forcing, where the stiffness introduced by the chemical
source term is extreme (condition number $10^{13}$), preventing analytical
implementation of BT. We investigate the performance of ROMs in prediction of
dynamics with unseen forcing inputs and demonstrate stability and accuracy of
balanced ROMs in truly predictive scenarios whereas without ERA, POD-Galerkin
and Least-squares Petrov-Galerkin projections fail to represent the true
dynamics. We show that after the initial transients under unit impulse forcing,
the system undergoes lightly-damped oscillations, which magnifies the influence
of sampling properties on predictive performance of the balanced ROMs. We
propose an output domain decomposition approach and couple it with tangential
interpolation to resolve sharp gradients at reduced computational costs.
