Ontology & digital twin

What is ontology?

Ontology helps us to structure and organise data in a way that reflects the real world. Ontology is actually a branch of metaphysics that deals with the nature of being but has been somewhat highjacked by the data community in recent times as a way of describing the types of techniques needed to gain meaning and actionable insight from data. 

In the context of data, an ontology is collection of concepts and relationships within a given context. So, a concept (or a thing like a person or a truck) and its relationships with other concepts within a particular context. 

Just think of it as a way or organising and structuring data in a way that delivers meaning. That meaning helps to derive new insights which can be used to realise tangible value from data. 

What is digital twin?

A digital twin is a digital replica of a real-world object that exists for the lifecycle of the respective thing, process, or system. It’s a virtual model that can be used to monitor and assess (real-time models) but also to simulate and predict outcomes based on certain events or conditions (synthetic models). 

Ontology is a central component of the digital twin concept and is used (with other techniques, and technologies) to create digital twin models. Adoption of digital twin technologies is growing rapidly as businesses and leaders look to find new ways to gain competitive advantage and make sense of the huge volumes of data that are being generated. 

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How Entopy uses ontology & digital twin to deliver value:

Data captured from connected systems is modelled and used to create digital twins of real-world things, processes, and systems. Entopy uses the digital twin to create a real-time model, structuring and organising data in a way that enables highly actionable, multidimensional insights to be generated.

At the heart of the Entopy platform is our ontology. Our ontology is what allows us to look at data through the lens of the entity, capturing dynamic relationships between real-world things. Critically, Entopy uses the concept of top-level ontology to ensure interoperability to data models across ontologies (and future ontologies) which ensures the platform maintains critical flexibility, durability, and robustness to be able to deliver new insights, models, and applications rapidly.

In each context, the concept of digital twin is foundational, creating an operational data layer from which multiple applications and services can run. A digital twin of a supply chain may comprise many entities with related attributes (e.g., vehicles, locations, warehouses, consignments). There may be multiple processes comprising various combinations of entities (with entities adopting different roles & functions in various contexts). Treating the overall digital twin (and the various ontologies within it) as a foundational layer allows operational applications and services to access, analyse, and apply the areas of interest rapidly.

The digital twin also enables Entopy to capture highly actionable, complex insights from large datasets in real time. By structuring and organising data in a way that allows Entopy to look through the lens of the entity, it is possible to capture more complex and multidimensional relationships as they happen.

Entopy’s ontology framework and methodology have been developed over many years and tested in large-scale operational environments.

Read more about how Entopy uses ontology & digital twin to look at data through the lens of the entity in our whitepaper: crossing the data chasm