Utilities do not have a shortage of data. In fact, most have more operational data today than at any point in their history. SCADA provides real-time measurements. GIS understands location and network connectivity. Asset management systems hold equipment and maintenance information. Smart meters, sensors, weather feeds and increasingly edge devices add even more. Yet when an operator, engineer, planner or increasingly an AI system needs to make a decision, getting to the right answer can still be surprisingly difficult.
The problem is not always the data. It is the context around it.
As utilities move toward more intelligent grid operations, I believe context is becoming one of the most important and often overlooked layers of the digital grid.
From Data to Understanding
In my previous article, The Invisible Grid, I argued that trusted data is becoming critical operational infrastructure. But trusted data alone does not necessarily create understanding.
Consider something as familiar as a transformer.
GIS may know where it is and how it connects to the network. An asset management system may know its age, inspection history and maintenance record. SCADA may provide its current operating state. Other systems may hold loading forecasts, weather exposure, customer impact or planned work.
Each piece of information can be perfectly accurate. But the operational value emerges when we understand the relationships between them.
Where is the asset? What is it connected to? What depends on it? What is its current condition? How has that condition changed? What happens elsewhere on the network if it fails or is taken out of service?
That is the difference between having data and having context.
The Industry Has Been Working on This Problem for Years
This isn't a new problem created by AI or digital twins. The utility industry's Common Information Model (CIM) provides a useful illustration. It was developed to improve information exchange and interoperability across diverse utility systems. Importantly, CIM doesn't just describe individual power system objects, it also represents the relationships between them.
EPRI describes CIM as a semantic model containing utility business objects and the relationships between them. Its contextual-modeling guidance goes further, showing how common definitions and relationships can be reused across different utility applications and interfaces.
Why does that matter - Because a transformer is not simply a database record with attributes. It exists within an electrical network, at a geographic location, connected to other equipment and serving an operational purpose.
The relationships give the data meaning.
We Can See This Taking Shape in the Real World
There are already public examples of utilities and industry initiatives moving in this direction. A Leading Electricity Distribution Utility on US East Coast , for example, publishes Common Information Models designed to provide a consolidated view of network assets and their connectivity, bringing together information covering equipment such as transformers, circuit breakers, wires and cables. The stated objective is to make network information easier for customers and stakeholders to understand and use in decision-making.
The U.S. Department of Energy has also supported transmission-distribution demonstrations based on CIM in which grid models, meter data, substation information and grid-edge sensor data are converted into common definitions and combined to improve data exchange across systems. These initiatives address different problems, but they point toward the same underlying requirement:
A smarter grid needs more than connected systems. It needs connected meaning.
Why Context Becomes Critical in the Age of AI
This becomes even more important as utilities introduce AI into operational and engineering workflows. AI can process enormous amounts of information quickly, but volume is not the same as understanding.
Imagine asking an AI-enabled system “Which transformer should we prioritize for maintenance?”
An asset-health score alone may not be enough. A useful recommendation could require understanding loading, condition, network criticality, customers affected, redundancy, maintenance history, weather exposure, planned outages and available alternatives.
Now imagine those pieces of information exist but in separate systems with different identifiers, definitions and relationships. The AI has access to more data, but not necessarily more understanding. That distinction will matter enormously.
AI without context can generate answers. AI with trusted context has a much better chance of supporting good decisions.
Digital Twins Depend on the Same Foundation
The same principle applies to digital twins. A sophisticated digital representation or visualization does not automatically create operational value. Its usefulness depends on maintaining meaningful relationships between the physical asset, its network position, operating condition and the other systems and processes surrounding it.
A recent example is National Grid's Triton digital twin and data - visualization platform in Great Britain. Triton combines a digital representation of physical infrastructure with expected demand information to support future network scenarios and investment planning. National Grid reports that the approach can reduce the time required to analyze and decide where network reinforcement is needed by 70%.
The important point isn't the visualization. It is what the connected information allows people to decide. That is where context starts turning digital capability into operational value.
From Data to Decisions
For years, utilities have invested heavily in collecting, storing and integrating data. Perhaps the next evolution can be viewed more simply:
Data → Context → Intelligence → Decision → Outcome
Each layer depends on the one before it.
This is why I don't believe the context problem belongs solely to the data or technology organization. It requires engineering, operations, asset management, IT and other stakeholders to agree on something deceptively simple:
What is our shared understanding of the grid?
Common asset identities, relationships, topology, semantics and ownership all become part of that answer and so does the governance required to keep that understanding synchronized as the physical network changes.
Connecting systems is increasingly achievable. Maintaining a shared operational understanding across them is the harder challenge.
Closing Thought
Utilities have spent years connecting devices, digitizing assets and integrating systems. Those investments have created extraordinary amounts of information. The next advantage may not come from generating even more of it. It may come from understanding the relationships within what we already have.
A truly intelligent grid doesn't simply know that something happened. It understands where it happened, what it is connected to, why it matters, and what decisions it should inform.
Data tells us what we know. Context tells us what it means.