During the IAU symposium, 368 “Machine Learning in Astronomy: Possibilities and Pitfalls” in Busan, we organized a panel discussion on the different aspects of data-fusion for large data-sets. Driven by the needs of the scientists, data-fusion technics had been introduces to enable multi-wavelength as well as multi-messenger approaches. This is necessary to get a more detailed and more complete representation of physical phenomena. We identified six different aspects related to data-fusion. Those aspects cover missing data, heterogeneous data, data-access in general, challenges related to data-size, FAIR-data, and future challenges.1