Digital twins are real-time simulations or replicas of a physical object, a place, a process, or anything you can model mathematically. In the early days of space shuttle experimentation, whenever a shuttle was launched, the crew would have a ‘physical twin’ or small-scale model on earth to keep track of operations and plan for ‘what-if’ scenarios. Today digital replicas are used to attempt to apply the same concept across a variety of industries and use-cases, including financial services.
“It’s a fancy way of saying ‘simulation modelling’,” reflects Ken Priyadarshi, CT Al leader at EY Technology. “It tends to be a niche field, and is often more common in manufacturing, supply chains and operations. However, I suspect it will soon become increasingly mainstream in a world filled with metaverses and digital economies.”
Developing Digital Twins
As part of his role in building proprietary digital platforms Priyadarshi leads on AI at EY Technology. “I work with EY engineering and business teams to drive our own internal digital transformation,” he explains. “I also lead a practice of more than 150 AI practitioners. EY’s mission is to embed AI into EY organizations operations, and to help drive client value through AI applications and digital products. In addition to my operational leadership work, I still love doing hands-on product design and research. My current research is in digital twins domain and specifically their relevance to Chief Financial Officers (CFOs) and data-driven business transformations.”
Digital twin development can start with simple prototypes, iterative model building, and small iterative experiments explains Priyadarshi. “The goal should be to grow it with your data harvesting strategy.”
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