As The automobile and industrial designer Freeman Thomas said “We were promised a simpler life, and technology has only complicated our lives.” In complex domains, like oil and gas, technology may well have simplified some things but it has certainly complicated others, particularly where data is concerned.
As our workplaces become increasingly digitised, new challenges have arisen where systems are unable to talk to one another or require manual intervention to generate intended outcomes. According to a recent survey of 1,150 decision-makers, 43% of global organisations are experiencing “digital transformation fatigue”, 40% have invested in new technologies and innovations without integrating them into existing systems and over 80% struggle to make the most of data and insights to benefit their business (Avanade, 2021).
In many cases it feels like data disconnection lies at the root of the problem. But could the cause also hold the cure? A technology bridge to connect our knowledge, a map to unlock it?
Knowledge graph technology may be that cure: a layer to connect our decisions to our data, to harness the full power of our knowledge, to bring simplicity, order, control and impact.
A bridge to connect our data and relationships
Knowledge graphs are built of objects and relationships, the latter bringing context and meaning to the former. As humans, we don’t see the world as discrete objects, but rather as objects connected to other objects, often dozens of other objects, each loaded with context. In oil and gas, a gas detector within a knowledge graph can be described by its type, the system in which it operates, its performance standard, its physical location, the hazards it protects against and so on. It is far from being an island of data and is bridged by relationships to other data, and therefore meaning, within the knowledge graph.
A bridge between different types of data
Even the simplest workflows today rely on data and inputs from multiple, disconnected sources, in different formats. Human intervention is often required to take disparate data and provide meaning and relevance, in order to feed decisions. Knowledge graphs are data agnostic, and merely provide a pathway to the data, leaving it at source. In oil and gas, a knowledge graph can bridge data from real-time sensors on equipment, with maintenance logs, staff competency records etc., to inform a risk status that continuously checks for anomalies. Much like the human brain, knowledge graph technology does not require everything to be converted to the lowest common denominator before it becomes useable and useful.
A bridge between today’s needs and tomorrow’s needs
Unlike relational databases, knowledge graphs don’t require prior definition of the schema or database container; instead they evolve as more data is connected in. They overcome the significant challenge of trying to futureproof database systems with detailed specifications of how they could be used and instead adapt and scale based on each use-case. In oil and gas, this means value can be derived from knowledge graphs much earlier than traditional database systems, use-case by use-case – without any regret costs.
A bridge between humans and machines
Graph technology enables an organisation to make its knowledge accessible to both humans and machines. Since knowledge graphs employ a structured, yet intuitive way of organising data (or rather the map to that data), humans can visualise the data much more easily and computers can read the data using relatively simple automated scripts. In oil and gas, where large volumes of data are gathered from increasingly ubiquitous sensors, knowledge graphs are able to feed analytics and visualisation tools to help both humans and machines make better decisions.
A bridge to unlock the power of ML/AI
Today, the potential of machine learning and artificial intelligence is constrained by patchy, messy and unstructured data. In complex industries like oil and gas where data and systems have evolved over decades, knowledge graphs can act as a layer across the data, providing enough structure to enable ML/AI algorithms to train. In this way, knowledge graphs open up the opportunity for automated learning, to deliver genuinely predictive analytics to drive improved safety, efficiency, cost management and production.
While oil and gas operators recognise a growing digital transformation fatigue in the sector, they remain positive about the benefits from digital technologies. They are however wary of big system investments that require time-consuming data cleansing and management efforts and prefer Eigen’s approach of creating value use-case by use-case. Knowledge graphs underpin, and indeed accelerate this approach because as each use-case is solved, the value of the knowledge graph increases – and the time to value contracts.
Bridging to something or connecting more than one thing means not having to move it, but rather join it, make a pathway, open it up, create an opportunity.
Knowledge graphs from Eigen.
For more information on Digital Twin technologies and how Eigen can help, contact info@eigen.co
