Energy organisations are not short of data, but they are short of clarity, speed, and confidence in the numbers they rely on.
Billing systems, CRM platforms, settlement systems, and trading and forecasting tools generate data continuously. Despite this, leadership teams are still waiting days or weeks for answers to basic commercial questions around cost, performance, risk, and growth.
In a market defined by volatility, regulation, and margin pressure, that delay is no longer a minor inconvenience. It is a direct threat to profitability and competitive position.
Most energy data estates were not deliberately designed. They evolved over time.
New systems were introduced to solve immediate problems such as billing upgrades, regulatory tooling, customer platforms, and trading systems. Each was optimised locally, and rarely architected as part of a coherent, end to end platform.
Over time, this has created a fragile operating model built on:
The organisation continues to function, but insight does not flow.
When data is fragmented, the symptoms are consistent across the sector.
The result is a familiar state of being data rich and insight poor. When this happens, first mover advantage is often lost before it is even visible.
Traditional data strategies assume:
Modern energy organisations operate in a very different reality.
AI, advanced analytics, and automation require:
Without these foundations, AI does not quietly underperform. It amplifies inconsistency. Models can produce outputs, but teams do not trust the inputs, and without trust, nothing scales.
An AI ready data foundation is not defined by the tools chosen. It is defined by how data is treated across the organisation.
Across energy organisations making real progress, we consistently see the following characteristics:
When these foundations exist, AI becomes a force multiplier rather than a risk amplifier.
We see two distinct patterns when energy organisations approach AI.
Some organisations rush to adopt models without addressing fragmentation. They generate proofs of concept and experiment with large language models, but struggle to move anything into production because trust, governance, and operational readiness are missing.
Others work backwards from business outcomes. They unify their data, establish governance, and introduce AI only once trust, lineage, and access controls are firmly in place.
The difference between these approaches is not ambition. It is architecture.
When data and platforms are aligned, we consistently see:
Speed compounds, and confidence follows.
This is not about doing data better for its own sake. It is about building infrastructure that supports decision making.
When applications and data are unified on a modern, well governed platform:
The platform stops constraining strategy and starts amplifying it.
At D55, we do not treat data strategy, platform engineering, and AI as separate initiatives. They operate as one system.
Our work in energy is built around three integrated services:
2) Read the Switch 2 Case Study.
Most modernisation programmes fail because they start with assumptions.
That is why we begin with a Data Strategy Diagnostic, a short and focused engagement that:
No jargon. No technology for its own sake. Just clarity before commitment.
Energy organisations do not lose momentum because they lack ambition. They lose momentum because their architecture cannot support it.
Fix the silos and everything accelerates, including decision speed, delivery pace, cost control, innovation, and market leadership. Leave them in place and every new initiative becomes slower, more expensive, and carries more risk.
If this feels familiar, I am always open to a direct conversation about data foundations, platform modernisation, and how clarity becomes a strategic advantage.
Rhys Jacob
Chief Technology Officer
D55
Insights & experience
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