Death and taxes may historically have been the only two certainties in life, but we might consider a third – increasing volumes of data. With ubiquitous sensing, unlimited cloud storage and insatiable demands from every walk of life, data is growing exponentially. Between 2010 and 2020, the total amount of data created, captured, copied, and consumed globally increased from 2 zettabytes (ZB) to 64ZB – and is forecast to treble to more than 180ZB by 2025 [Source: Statista, 2021]
And yet our capacity to use even a fraction of that data cannot keep up; meaning much is lost, still more is ignored, leaking value out of organisations. In some cases, the cost of getting on top of data is simply too high; in other cases, we may not understand the data we have.
Machine learning, artificial intelligence, automation, digital twins, all offer a vision where every bit of data can be useful.
But there’s a giant leap between scattered, unstructured, messy data and plugging in an AI to make sense and restore order.
At Eigen, we fill that gap for oil and gas. With knowledge graph technology
What is a knowledge graph?
A knowledge graph is a scalable directory to an organisation’s disparate data, bringing meaning to deliver “contextual knowledge”.
The theory of knowledge graphs goes back to the 1700s, although the term wasn’t officially used until the 1970s. However, like many of today’s technologies, knowledge graphs have only become possible with the advent of cloud computing and faster processing.
· Knowledge graphs provide structure or framework to represent both data (objects, events, concepts) – and the relationships that link those data, therefore providing context to the data.
· Knowledge graphs tend to be built for a specific domain, relying on a common vocabulary and taxonomy (or an ontology), to constrain boundaries and focus. Within that domain, the knowledge graph is agnostic to data type and source.
· By organising and contextualising data, regardless of where it sits, knowledge graphs make data accessible for both human and machine interrogation and analysis.
Knowledge graphs by Eigen
At Eigen, we specialise in building knowledge graphs for oil and gas. We apply our domain knowledge to Neo4j’s graph technology to solve specific business problems for our clients.
We build a client’s knowledge graph around a specific use case – and grow their knowledge graph incrementally use-case by use-case. Adding more contextualised data into the knowledge graph, makes building new use-cases easier and faster over time.
We have built many knowledge graphs for clients over the past few years, taking a specific business problem or pain, and connecting in relevant data, from disparate sources, to help our clients make faster, better decisions or uncover new insights.