The Great Lakes and connecting waters encompass a vast and diverse ecosystem that presents scale challenges for management similar to those of the coastal ocean. Technological approaches to overcome the scale challenges have primarily been adapted from oceanographic applications and technologies, and from upscaling inland lake methods designed for shallower and calmer water bodies. Many standard methods for studying Great Lakes habitat and biota have long lag times between field collection and data availability. Many also miss much of the dynamics, three-dimensional complexity, and spatial variability needed to manage the system effectively. Even baseline conditions are not well characterized for many parts of the Great Lakes ecosystem (e.g. bathymetry and critical habitat, life cycles and food webs, night and winter movement and activity of organisms). Emerging technologies are beginning to address these needs but require coordination, consistent investment, training, and governance linkages. Here we survey recent technological advances and show how they are contributing to improved adaptive management of the Great Lakes ecosystem by reducing uncertainty and increasing understanding of physical, biological, and chemical processes, and the human dimensions of resource management and restoration.