  Investigation of the underlying physics or biology from empirical data
requires a quantifiable notion of similarity - when do two observed data sets
indicate nearly identical generating processes, and when they do not. The
discriminating characteristics to look for in data is often determined by
heuristics designed by experts, $e.g.$, distinct shapes of "folded" lightcurves
may be used as "features" to classify variable stars, while determination of
pathological brain states might require a Fourier analysis of brainwave
activity. Finding good features is non-trivial. Here, we propose a universal
solution to this problem: we delineate a principle for quantifying similarity
between sources of arbitrary data streams, without a priori knowledge, features
or training. We uncover an algebraic structure on a space of symbolic models
for quantized data, and show that such stochastic generators may be added and
uniquely inverted; and that a model and its inverse always sum to the generator
of flat white noise. Therefore, every data stream has an anti-stream: data
generated by the inverse model. Similarity between two streams, then, is the
degree to which one, when summed to the other's anti-stream, mutually
annihilates all statistical structure to noise. We call this data smashing. We
present diverse applications, including disambiguation of brainwaves pertaining
to epileptic seizures, detection of anomalous cardiac rhythms, and
classification of astronomical objects from raw photometry. In our examples,
the data smashing principle, without access to any domain knowledge, meets or
exceeds the performance of specialized algorithms tuned by domain experts.
