A digital twin (DT) is a virtual representation of an object, system or process that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making. Digital twins can be used to help answer what-if AI-analytics questions, yield insights on business objectives and make recommendations on how to control or improve outcomes. It’s a fascinating technology that the SNIA Cloud Storage Technologies Initiative (CSTI) discussed at our live webcast “Journey to the Center of Massive Data: Digital Twins.” If you missed the presentation, you can watch it on-demand and access a PDF of the slides at the SNIA Educational Library. Our audience asked several interesting questions which are answered here in this blog. Q. Will a digital twin make the physical twin more or less secure? A. It depends on the implementation. If DTs are developed with security in mind, a DT can help augment the physical twin. Example, if the physical and digital twins are connected via an encrypted tunnel that carries all the control, management, and configuration traffic, then a firmware update of a simple sensor or actuator can include multi-factor authentication of the admin or strong authentication of the control application via features running in the DT, which augments the constrained environment of the physical twin. However, because DTs are usually hosted on systems that are connected to the internet, ill-protected servers could expose a physical twin to a remote intruder. Therefore, security must be designed from the start. Q. What are some of the challenges of deploying digital twins? A. Without AI frameworks and real-time interconnected pipelines in place digital twins’ value is limited. Q. How do you see digital twins evolving in the future? A. Here are a series of evolutionary steps:

• From Discrete DT (for both pre- and post-production), followed by composite DT (e.g assembly line, transportation systems), to Organization DT (e.g. supply chains, political parties).
• From pre-production simulation, to operational dashboards of current state with human decisions and control, to autonomous limited control functions which ultimately eliminate the need for individual device manager SW separate from the DT.
• In parallel, 2D DT content displayed on smartphones, tablets, PCs, moving to 3D rendered content on the same, moving selectively to wearables (AR/VR) as the wearable market matures leading to visualized live data that can be manipulated by voice and gesture.
• Over the next 10 years, I believe DTs become the de facto Graphic User Interface for machines, buildings, etc. in addition to the GUI for consumer and commercial process management.

Q. Can you expand on your example of data ingestion at the edge please? Are you referring to data capture for transfer to a data center or actual edge data capture and processing for digital twin. If the latter, what use cases might benefit? A. Where DTs are hosted and where AI processes are computed, like inference or training on time-series data, don’t have to occur in the same server or even the same location. Nevertheless, depending upon the expected time-to-action and time-to-insight, plus how much data needs to be processed and the cost of moving that data, will dictate where digital twins are placed and how they are integrated within the control path and data path. Example, a high-speed robotic arm that must stop if a human puts their hand in the wrong space, will likely have an attached or integrated smart camera which is capable of identifying (inferring) a foreign object. It will stop itself and an associated DT will receive notice of an event after the fact. A digital twin of the entire assembly line may learn of the event from the robotic arm’s DT and inject control commands to the rest of the assembly line to gracefully slow down or stop. Both DT of the discrete robotic arm and the composite DT of the entire assembly are likely executing on compute infrastructure on the premises in order to react quickly. Whereas, the “what if” capabilities of both types of DTs may run in the cloud or local data center as the optional simulation capability of the DT are not subjected to real or near real-time round-trip time-to-action constraints and may require more compute and storage capacity than is locally available. The point is the “Edge” is a key part of the calculus to determine where DTs operate. Meaning, time-actionable-insights, cost of data movement, governance restrictions of data movement, and the availability / cost of compute and store infrastructure, plus access to Data Scientists, IT professionals, and AI frameworks is increasingly driving more and more automation processing to the “Edge” and its natural for DTs to follow the data. Q. Isn’t Google maps also an example of a digital twin (especially when we use it to drive based on our directions we input and start driving based on its inputs)? A. Good question! It is a digital representation of physical process (a route to a destination) that ingests data from sensor (other vehicles whose operators are using Google Maps driving instructions along some portion of the route.) So, yes. DTs are digital representations of physical things, processes or organizations that share data. But Google maps is an interesting example of a self-organizing composite DT, meaning lots of users acting both sensors (aka discrete DTs) and selective digital viewers of the behavior of many physical cars moving through a shared space. Q. You brought up an interesting subject around regulations and compliance. Considering that some constructions would require approvals from regulatory authorities, how would a digital twin (especially when we have pics that re-construct / re-model soft copies of the blueprints based on modifications identified through the 14-1500 pics) comply to regulatory requirements? A. Some safety regulations in various regions of the world apply to processes. E.g Worker safety in factories. Time to certify is very slow as lots of documentation is compiled and analyzed by humans. DTs could use live data to accelerate documentation, simulation or replays of real data within digital twins and could potentially enable self-certification of new or reconfigured process, assuming that regulatory bodies evolve. Q. Digital twin captures the state of its partner in real time. What happens to aging data? Do we need to store data indefinitely? A, Data retention can shrink as DTs and AI frameworks evolve to perform ongoing distributed AI model refreshing. As AI models refresh more dynamically, the increasingly fewer and fewer anomalous events become the gold used for the next model refresh. In short, DTs should help reduce how much data is retained. Part of what DT can be built to do is to filter out compliance data for long-term archival. Q. Do we not run a high risk when model and reality do not align? What if we trust the twin too much? A. Your question targets more general challenges of AI. There is a small but growing cottage industry evolving in parallel with DT and AI. Analysts refer to it as Explainable AI, whose intent is to explain to mere mortals how and why an AI model results in the predictions and decisions it makes. Your concern is valid, and for this reason we should expect that humans will likely be in the control loop wherein the DT doesn’t act autonomically for non-real-time control functions.