| |APRIL 202419CIOReviewTHE ESSENTIALDIGITAL TWINThe real prize to be gained from deploying a digital twin into the workplace is to help the human to thinkcognitive load of interpreting data in context creates a burden for workers in all industries, and can slow down human decision-making. This is, in fact, the real prize to be gained from deploying a digital twin into the workplace: to help the human to think.How? Digital twins bring our data together in an intuitive way that helps us to see ­ sometimes quite literally ­ what's really going on in the assets we own and operate. We can decompose the twin into three main components, which must all come together to generate the insight we need.(1) Data. This includes data describing the asset in some way such as design information, maintenance history, or utilization plans. It also includes data coming in from sensors on the physical asset like temperature and pressure.(2) Visualization. The visual environment of a twin must evoke the asset being modeled, almost by definition. This is why the first thing that comes to mind for most people is 3DV when they think about twins. Arguably, the true requirement for twin visualization is that data must be displayed in a way that promotes an understanding of context.For a digital twin to be compelling, the visual aspect must appeal to our intuitive sense of how the different bits of information interrelate. Although 3DV helps to create a comprehensive look and feel, this can also be done in two dimensions if good UX design principles are employed.(3) Simulation. Broadly speaking, this is the mechanism by which dynamics that represent the behavior of the asset or simulate the movement of or through the asset are added. Simulation makes the twin come to life and can provide the user with the experience of being there physically. It's fair to include, in this category, physics-based simulations, machine learning models, and even workflow modeling, which all introduce a sense of time or change to the digital twin.Technology for accessing and integrating data continues to advance. One innovation is the use of knowledge graphs to facilitate the integration of data from different sources.Visualization and simulation technologies are progressing as well. In combination, they are giving us, for example, physics-based 3D environments for training drones. In the future, we can expect not only more diagnostic and predictive functionality from digital twins, but also more automation and integration with robotics. The data used will come from more sources, such as acoustic and spectroscopic measurements. Generative AI can be expected to add yet another layer of sophistication, making twins conversational and perhaps generating scenarios and 3D environments spontaneously or upon request.Environments will be viewable across different types of devices, including headsets. Further, with interest from many fronts in multiverse technology, it's not hard to imagine that the next generation of digital twins will allow for a more immersive and collaborative experience than what is possible today.It's important to point out that although the discussion here has focused on physical assets, the same description extends to digital twins we may create of our concepts and ideas. Thus, we will soon find new ways to interact with things we can't see in the real world, including sound waves and molecules. We may soon say of a twin that it's not a single representation of an asset, but every possible representation brought together into a single interface. Yet what every digital twin has in common is that it brings knowledge in the form of contextualized data to the user, for the purpose of accelerating human thinking. Faster thinking means faster decision-making, and the faster you can make a decision, the faster you can get into action.
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