  We present a novel reformulation of balanced truncation, a classical model
reduction method. The principal innovation that we introduce comes through the
use of system response data that has been either measured or computed, without
reference to any prescribed realization of the original model. Data are
represented by sampled values of the transfer function {or the impulse
response} corresponding to the original model. We discuss parallels that our
approach bears with the Loewner framework, another popular data-driven model
reduction method. We illustrate our approach numerically in both
continuous-time and discrete-time cases.
