Digital twins are not all created equal.

What does trusty old Wikipedia say a digital twin is? “Digital twin refers to a digital replica of physical assets, processes and systems that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of Things (IoT) device operates and lives throughout its life cycle.”

Let’s start by imagining you created a very effective digital twin of yourself.

IoT.nxt Director of Global Partnerships Gareth Rees explains:

You start off by making a digital avatar that looks like you, you even make it sound like you, and move like you. You use sensors in devices (think Fitbit) and even embed others in your body to send the status of key metrics such as temperature, heart rate, location, distance moved, blood glucose and hormone levels to your digital twin to increase its likeness. Once you have gathered all of this information in real time and augmented your avatar with it, you have created a complete digital twin.

To make this digital twin useful however, you need to overlay the generic and specific biological rule sets onto this data to interpret and gather insight. In other words, you need to be able to turn raw data into intelligence. For example, knowing that the standard body temperature is 37 degrees and that blood composition changes when the body is dealing with infection is critical for the digital twin to turn information into value, adding insight that could maximise your performance or even save your life. Rees explains further:

A specific person (context specific) may also be a diabetic or have Malaria, for instance, and this would have a significant impact on how this information is interpreted. Also – different people, while similar, react differently to medication or stresses and these biological rule sets need to learn the behaviour of a specific individual to represent them effectively in the digital twin (self-learning systems).

By applying these often-complex rule sets to the data on your digital twin, we can generate simple and easy-to-consume dashboards and insights that can help you live a happier and healthy life at the life stage that you are in. This is critical, as the digital twin needs to unlock value consistently over time, otherwise it has no purpose.

In business, everything needs purpose or its a waste of money.

It follows, then, that the effectiveness of a digital twin is directly affected by the following things:

  • The accuracy, latency and completeness of the information gathered from the physical world
  • The effective treatment of the information with the correct generic and context specific business rules and analytics
  • The ability of the system to learn and adapt dynamically to its host and its interactions
  • The ability of the system to drive positive change through value adding data and visualisation

Very few digital twin deployments that have reached any form of meaningful scale can measure up against these criteria, because there are key constraints in the data value chain. Rees unpacks the IoT.nxt advantage:

IoT.nxt have created an industrial IoT hardware and software stack that resolves many of these constraints, and paves the way for meaningful digital twin development. IoT.nxt are currently deploying numerous industrial IoT solutions at blue chip companies, in over 10 industries. Through these deployments we have encountered many of the common pitfalls in trying to deploy IoT solutions across multiple original equipment manufacturers (OEM’s), disparate systems and protocols and can share insight into the reasons for their failure.

It is critical to see the development of a digital twin as part of a process of digital transformation, says Rees. Furthermore, it is critical to get the architecture and principles of the digitally transformed organisation in place so it can support effective scaling, and not the creation of further silos.


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