For digital systems to have control of the physical world, they must represent it, control its representation, and apply this control to reality.
Furthermore, the virtual representation has to be perfectly accurate in order to be useful. This extends to their behavior.
The digital twin of an aircraft engine is not only statically replicated; it also works like its real counterpart. As a result, all engineering changes, test runs, and maintenance can be performed on it before being performed in the real world,
The notion of “twin objects” can be expanded to digital representations of people, thought and business processes, and even complete enterprises.
These models not only contain current states and connections, but also observed and projected behavior.
All thought and decision-making processes use these digital representations, removing the discontinuity that separates virtual and real worlds.
Entering Twin Worlds requires a mindset change to understand how digital systems actually reflect and control the real world.
Accepting errors, inaccuracies, or latency is replaced by the constant demand for both accuracy and control. It requires a consistent data landscape where governance, trust, and accessibility are core concerns within the fabric of the Twin Worlds.
A virtual representation of the real world needs to be built up step by step, incorporating an increasingly better understanding of the key real-world assets and an improved ability to translate them into digital terms.
The digital twin of a physical object can be tangible enough but augmented or virtual reality can make it life-like even if the complex ones are difficult to recreate.
The twins of persons, processes, or institutions are less tangible, and are consequently challenging to grasp.
The key is to constantly take a virtual world perspective: how do consumers, corporations, and products behave digitally, and how is this translated into the next-generation digital IT landscape?