  Combining different observational probes, such as galaxy clustering and weak
lensing, is a promising technique for unveiling the physics of the Universe
with upcoming dark energy experiments. Whilst this strategy significantly
improves parameter constraints, decreasing the degeneracies of individual
analyses and controlling the systematics, processing data from tens of millions
of galaxies is not a trivial task. In this work we derive and test a new
estimator for joint clustering and lensing data analysis, maximising the
scientific return and decreasing the computational cost. Our estimator
compresses the data by up-weighting the components most sensitive to the
parameters of interest, with no loss of information, taking into account
information from the cross-correlation between the two probes. We derive
optimal redshift weights which may be applied to individual galaxies when
testing a given statistic and cosmological model.
