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Enterprises -- and their CIOs -- might not realize it yet, but digital twins are already here.
As Al Velosa, Gartner analyst and resident digital twins expert said, "even if they're not being called digital twins, they're prevalent in a variety of environments in both industrial and consumer products."
He gave some examples: NASA has been controlling spacecraft remotely via computers for years. Consumers can control their Sonos Bluetooth speakers via their smartphones. Siemens has been busy developing and selling digital twin software since 2013.
So the concept of digital twins is not new, but that doesn't mean it's ready for widespread enterprise adoption. Where do digital twins stand now and where are they headed?
Velosa says that while it's early days in terms of the adoption and advancement of digital twins, in 10 years companies won't be able to compete without them. For CIOs, it's important to start considering business objectives, creating an incubation team and developing the necessary ecosystem.
"No vendor really has the ecosystem or standard set, so it's going to be a Wild West environment for a while and [IT teams] can do a lot of innovation in this space," he said at the Gartner Data & Analytics Summit.
"It's the heart of IoT"
First, let's start with what digital twins are: a digital representation of a physical object that mirrors the object exactly. They are deeply ingrained in the ongoing IoT revolution -- the "heart of IoT" as Velosa puts it. The four minimum required elements, according to Velosa, are a proper model, whether FEA or CAD/CAM; plenty of relevant, contextual data; uniqueness in the form of one to one correspondence between the digital twin and the object; and the ability to monitor the object in real-time and receive notifications.
Al VelosaGartner analyst
"If you're a manager sending technicians to take care of air conditioners or elevators, the ability to pull up the specific state [of the asset]; to understand where it is, what the issues are and to prioritize which technicians should go is a key starting point," said Velosa.
From there comes more advanced capabilities like predictive analytics, simulation and, most importantly, control, which he isn't seeing much of presently outside of select consumer and automotive applications.
"I love the examples in which you can actually control something [via a digital twin], but, in a lot of enterprise applications that I'm familiar with there's usually a level of trust that has to be established," Velosa said. "Jumping into control is not usually what most engineers, especially in heavy-asset industries, are ready to do."
But why invest in this IoT-driven technology in the first place? Velosa emphasized the business benefits of digital twins.
"The whole point of a digital twin is about making the right decisions at the right time," he said. "[It] is here to help us use relevant data from our things, when we need it, to actually be better."
That includes being able to serve your customers better; customers who will soon start expecting the kind of capabilities that digital twins provide -- pulling up an app on your phone to see the location and status of your car, for instance.
"In 10 years, [the digital twin] will be endemic," Velosa said. "It will be assumed to be a part of your product, and … if you don't have it, you can't really compete. [Digital twins] will be fundamental to differentiation."
But Velosa admits it'll be an uphill battle to reach that point.
"Until we get better standards and better capabilities, the lamentable state is that there's going to be an integration challenge, because all of these vendors want their platform to be the dominant one," he said.
Legal and ethical issues also need to be addressed before widespread implementation, Velosa notes. Can and should data associated with digital twins be licensed, sold or traded? How do you know whether it's yours to share -- because you captured it, because it's on your server or because you operate the equipment?
Three types of digital twins
Velosa listed three kind of digital twin archetypes that he's seeing in the enterprise and beyond:
- The high-fidelity digital twin: Implies that you've done extensive analysis of the object, figured out what the key issues -- including environmental issues -- are and built the model on those findings. With high-fidelity digital twins, we're actually trying to predict and prescribe meaning to what's going on with an object like a jet engine or spacecraft. NASA is the exemplar in this case.
- The functional digital twin: Indicates the basic status of an object -- is it on, is it off, is it full, is it empty.
- The statistical digital twin: Used to collect raw data that will later be analyzed. As Velosa said, with this type, an IT team will say, "There are some sensors on this object; let's collect data until we figure out what's important."
Finding the business case
Where should CIOs start when developing a strategy? Velosa says to first build a team -- preferably your organization's IoT team -- to start looking at the implications of digital twins. The CIO can then work with that team and their business units to explore whether they have the right technologies, suppliers, capabilities, policies and business objectives to enable a digital twin future.
"You're going to need to be one of the ambassadors for figuring out how your enterprise can use this to differentiate itself," Velosa said of CIOs, adding that there are a lot of undefined aspects and possibilities around digital twins that have yet to be explored.
CIOs and these digital twins teams should also build a roadmap for how digital twins will impact your analytics and IoT strategies short-term and long-term.
But he stressed that success with digital twin technology starts and ends with business objectives and knowing what business problems you're trying to solve.
"Engineering products is no longer relevant," Velosa said. "It's strictly about the problem the business unit has and how to help them solve it."
His final peice of advice for CIOs in terms of digital twin implementation: keep your team from from getting too attached.
"Don't fall in love with the tech," he said at the Gartner Data & Analytics Summit. "Keep it simple. Sometimes a piece of paper is all you need."
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