Current panoramic IFS are proving overwhelming information content that cannot be fully harvested because of the target-oriented approach to data analysis, and limited human resources. In the 2030s, when BlueMUSE will operate at full capacity alongside MUSE and other IFS, a tremendous numbers of sources will be left unnoticed and uncatalogued in our datasets and archives. A paradigm shift in the way we approach data analysis is necessary to make the next scientific leaps in many astrophysical areas. I will present automated Data Analysis Software (DAS) principles that are in development for MUSE & BlueMUSE surveys. The DAS concept will be optimized for both galaxy surveys/deep-fields and resolved galaxy observations, and is designed to operate end-to-end without intervention. The workflow currently consists of four modules that: (1.) identify sources; (2.) extract spectra/images; (3.) classify sources, estimate reshifts and measure continuum & emission line fluxes; and (4.) produce catalogs and meta-data. Each step of the DAS interacts with a local database and can also be coupled with dedicated visualization tools for inspection, parameter optimization, and for re-running steps. DAS will be used by individual researchers to accelerate science, and at the level of observatories to exploit the large volumes of unused data, distribute high-level source catalogs. Such methods are the only way to fully utilize the information content provided by VLT and other facilities in 2030s.