For energy companies, data quality is about much more than just reporting. It’s about revenue, risk, and decision-making. When even small discrepancies can have major financial consequences, control over energy data becomes a strategic necessity.
Many energy companies still rely on solutions developed for a different era. The systems are stable but inflexible, and even minor changes to calculations, rules, or reporting requirements can trigger extensive processes.
When the systems do not support the necessary adjustments, the organization finds its own ways around them. Over time, both logic and ownership are shifted away from the core solutions, and spreadsheets and manual steps become the link between systems that do not interact well enough.
The consequences become clear when errors are first discovered late in the process, often during period-end closing. When figures need to be explained, the work must be traced back through files, emails, and individual interpretations. Traceability and auditing become time-sensitive tasks performed under pressure, rather than being an integrated part of the workflow.
This situation is becoming increasingly demanding. Energy companies are managing multiple energy sources, more complex value chains, new data types, and stricter regulatory requirements. At the same time, expectations regarding transparency, documentation, and verifiability are rising.
Many people think of data quality as an IT issue. In reality, it’s a business issue. When a deviation of just 0.1 percent can represent up to $100 million in annual revenue, control over energy data becomes critical for profitability, risk management, and decision-making quality,
says Geir Olav Hagen, Director of EnergyX at Cegal
When volumes are large, even small discrepancies can have significant consequences in the financial statements. This not only increases financial risk but also the risk of making incorrect decisions based on an incomplete or inconsistent data set.
It is only when you look at the big picture that the cost of legacy systems becomes clear. Manual reconciliations take time, errors are detected late, upgrades become extensive, and critical knowledge is often tied to individual employees. The result is higher costs, greater operational risk, and less flexibility.
A crucial distinction exists between post-process controls and real-time controls.
In many organizations, control doesn’t take place until the numbers are finalized—at a point when errors are already costly and time-consuming to correct. The analysis is characterized by time pressure, and the room for maneuver is limited.
When control is instead built into the work process itself, this changes fundamentally.
Data is validated on an ongoing basis, and discrepancies become visible as they arise. Changes are documented with context, so that traceability and audits do not have to be reconstructed afterward. The organization has less need for manual reconciliations and a better foundation for actively working with data while it is still relevant.
At the same time, it becomes easier to establish a shared understanding of the numbers across the organization. Fragmented solutions often lead to multiple versions of the truth, where production, finance, and commercial departments operate with different numbers.
With a single, managed dataset, calculations, business rules, and data sources become transparent. Discrepancies can be understood in context, not just noted. The discussion shifts from what the number is to why it looks the way it does.
The value of better data quality isn’t just about fewer errors. It’s about spending less time on manual checks, faster period closings, better audit processes, and greater confidence in the basis for decision-making. When employees no longer have to spend time finding, verifying, and explaining numbers, capacity is freed up for analysis, optimization, and value creation.
For leaders, this means faster access to decision-ready information and greater confidence in strategic choices. For the business, it means lower risk and better utilization of resources.
says Geir Olav Hagen
The energy sector is constantly changing. New assets, new forms of energy, new regulatory requirements, and new integrations are all part of the norm.
When systems are built for flexibility, it becomes possible to further develop processes without increasing complexity. Functionality and controls can be improved on an ongoing basis, without disrupting operations or creating new manual steps.
When the energy data flow is built around traceability, consistency, and control within the workflow, it strengthens the foundation for further digitization, automation, and AI-driven processes.
EnergyX Control is designed to shift control from post-processing to the workflow.
By consolidating data, validation, rules, traceability, and documentation into a single managed solution, energy companies gain a decision-making foundation they can rely on. Anomalies are detected earlier, manual processes are reduced, and the organization gains a single version of the truth.
Data quality isn’t just about accurate numbers. It’s about profitability, risk management, and the ability to make sound decisions.