The adoption of IoT technology has been explosive. Research suggests the IoT market will be worth $520 billion in 2023, more than double the value six years ago. As fast as it’s growing, it’s evolving. IoT is now developing beyond the line-of-business solutions it was initially devised for to gain real time visibility across entire assets and operations.
This has given rise to the digital twin, or the digital representation of a physical object, which is also experiencing exponential growth. The number of organizations using digital twins is expected to triple by 2022. The challenge is to successfully integrate your ecosystem as well as securely integrating the operational data from IoT devices with enterprise systems to achieve the full benefits of the digital twin.
The rise of the composite digital twin
IoT devices are becoming cheaper, faster, smarter and more reliable. This has led to devices being attached to more and more things. Forbes has identified four distinct types of digital twin, all the way from an individual component to an entire business process. Today, manufacturers of components – such as machine parts – are supplying products with their own digital twin.
IoT data is, of course, only one element of a digital twin. You are creating a complete model or replica of the thing, so you require information from other sources such as your enterprise systems in order to build a complete and accurate picture. And, this is just the first integration hurdle. Gartner suggests that, by 2023, 75% of digital twins of IoT-connected products will have at least five different kinds of integration touch points, including IoT devices, people, applications and mobile apps.
Within a single digital twin, there are a number of integrations that have to happen. This is further complicated by the fact that no organization will have a single digital twin and, as the Forbes definition shows, the more complex the digital twin – at the asset or process level – then it will be a composite of smaller digital twins.
For example, imagine a jet engine. The entire engine itself has a digital twin but so does every sub-system and individual component. The engine’s digital twin has to be able to integrate all the smaller twins and all their inputs and outputs. When it comes to realizing the full benefits of digital twin technology, such as operational gains, predictive maintenance and improved product design, then you will require a composite digital twin to give visibility and insight at a much higher level.
In addition, IoT is still in relative infancy and there are no universal IoT standards-and none on the horizon any time soon. The digital twins you get from manufacturers are likely to be different from each other but still have to be securely integrated to create a 360 degree view of your assets and systems. With no clear standards, Gartner believes that 95% of digital twin integration will still be based on custom integration in 2023.
The role of secure device management
With so much complexity to address with digital twin integration, the first essential question is the trustworthiness of the data coming from your IoT devices. Data quality and data integrity are both massive issues when you are faced with potentially millions of different data sets being created every minute. You need to know that the data populating your digital twin is accurate and reliable and the IoT devices itself hasn’t been hacked or breached.
Traditional data integration and data quality tools have not been developed to accommodate IoT ecosystems. They are not optimized to support the scale and distribution of either IoT technologies or the data that the different IoT devices create. To overcome this, you can focus on the devices themselves. Identity-driven IoT platforms such as OpenText™ Covisint deliver comprehensive secure device management functionality. It provides the capabilities to authenticate, provision, configure, monitor and manage each individual device. As interaction between the IoT device is, in the large part, continuous and in real-time, the functionality allows you to automate the management process across all types of device and at scale.
By setting the correct access, authorization and communication levels for each device, you can ensure that it only delivers the correct data to the digital twin. Advanced AI-assisted analytics allows for continuous monitoring to ensure that the entire ecosystem is functioning properly and that any anomalies are immediately identified and remedial action taken.
Unified data model: The secret to seamless ecosystem integration
Secure device management provides the foundation upon which an effective unified data model can be built. This is the key element to delivering the seamless ecosystem integration services your digital twins require. With accurate and reliable data from devices and systems, the ecosystem integration capabilities of leading IoT platforms standardizes how that data is identified and represented within a unified data model.
The unified data model provides a standard data format that can be exchanged and shared amongst all people, systems and things integrated into the digital twin Importantly, this capability allows third party access to the digital twin to enable efficient working processes such as suppliers fulfilling maintenance contracts or partners collaborating on new product design.
Advanced messaging and orchestration services securely transport the data enabling seamless integration across devices and systems. This approach eliminates the complexity of creating and syndicating separate integrations for machine-to-machine, machine-to-people, or machine-to-application scenarios. It enables you to de-couple your integrations from some of the underlying technologies within the Digital Twin. For example, you can upgrade to the latest wireless standards as they become available without having to re-integrate the system.
Taken together, secure device management allied to a unified data model ensures that the highest levels of data security and integrity are maintained at scale and made available to the every one of the integration touchpoints involved in a Digital Twin–even the largest composite twin.
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