Digital Twins are digital representations of real-world entities (sensors, devices, machines, processes, complex systems, and even people/person/living beings). They are deployed to drive business outcomes. They provide connectivity, metadata management, data management, increasingly advanced analytics, and often integration with business applications and process systems. Digital Twins can be organized and structured in different ways, such as hierarchies, topologies, etc. to represent the taxonomy of entities.
In its most basic form (low fidelity), a Digital Twin encompasses metadata about the respective entity/asset, and a means to monitor it in (near) real-time. More advanced (high fidelity) Digital Twins encompass analytic models (physics-based, or increasingly machine/deep learning-based) that enable prediction and simulation, allowing for comparison of expected versus real behavior, "what-if" scenarios, and continuous improvement of models through feedback loops.