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As observational datasets become larger and more complex, so too are thequestions being asked of these data. Data simulations, i.e., synthetic datawith properties (pixelization, noise, PSF, artifacts, etc.) akin to real data,are therefore increasingly required for several purposes, including: (1)testing complicated measurement methods, (2) comparing models and astrophysicalsimulations to observations in a manner that requires as few assumptions aboutthe data as possible, (3) predicting observational results based on models andastrophysical simulations for, e.g., proposal planning, and (4) mitigating riskfor future observatories and missions by effectively priming and testingpipelines. We advocate for an increase in using synthetic data to plan for andinterpret real observations as a matter of routine. This will require fundingfor (1) facilities to provide robust data simulators for their instruments,telescopes, and surveys, and (2) making synthetic data publicly available inarchives (much like real data) so as to lower the barrier of entry to all.

On the need for synthetic data and robust data simulators in the 2020s