  When completed the Square Kilometre Array (SKA) will feature an unprecedented
rate of image generation. While previous generations of telescopes have relied
on human expertise to extract scientifically interesting information from the
images, the sheer data volume of the data will now make this impractical.
Additionally, the rate at which data are accrued will not allow traditional
imaging products to be stored indefinitely for later inspection meaning there
is a strong imperative to discard uninteresting data in pseudo-real time. Here
we outline components of the SKA science analysis pipeline being developed to
produce a series of data products including continuum images, spectral cubes
and Faraday depth spectra. We discuss a scheme to automatically extract value
from these products and discard scientifically uninteresting data. This
pipeline is thus expected to give both an increase in scientific productivity,
and offers the possibility of reduced data archive size producing a
considerable saving.
