This chapter aims at describing some basic aspects and recent advances in the context of data assimilation, that is the integration of experimental information into computational models for updating or enrichment purposes. We first give an overview of inverse analysis from sensor data, introducing the main concepts with associated deterministic or stochastic approaches, before focusing on a specific approach which refers to reliability of information and is suited to structural mechanics applications. We then extend this approach to sequential data assimilation, with the capability to integrate on-the-fly some streams of data which may be continuously collected on a physical system for simulation-based monitoring purposes. We eventually present some recent works on the topic, involving advanced measurements and numerical strategies which are coupled to make benefit of all experimental and modeling information available, with application to real-time and accurate diagnosis, prognosis, and decision-making. This contribution is expected to be useful for students, engineers, and researchers in applied mathematics and computational engineering communities who wish to work on the topic.