From On-Prem to Cloud-First: Empowering Energy Trading
Published: September 2025
Client Name: OVOEnergy
Industry: Energy Supply, Energy Trading
Background
OVO Energy, one of the UK’s largest energy suppliers, has embraced a cloud-first data strategy, leveraging Google BigQuery in various parts of its business to drive analytics, innovation, and operational efficiency. While OVO Energy has made significant strides in modernising its data infrastructure, this on-premises system continued to operate separately from the broader cloud-based ecosystem. The Hedging, Optimisation & Analytics (HO&A) team’s Trading Requirement Model (TRM) was still relying on an on-premises SQL Server environment, creating inefficiencies and fragmentation in data management as well as issues related to cost, speed and efficiency.
In close partnership with Client’s team, Irysan designed and implemented a structured migration and modernisation plan aligned with cloud-first approach, delivering improved performance, maintainability and usability of the system.
Challenges
The Trading Requirement Model (TRM) is a critical component of OVO Energy’s Hedging, Optimisation & Analytics (HO&A) team’s workflow, providing hedging strategies and predictions for the company’s energy needs. However, the system was still running utilising an on-premises SQL Server environment, which posed several challenges that impacted its performance, maintainability, and integration with OVO Energy’s cloud-first data strategy.
Despite its importance, TRM was outdated and under-maintained, making it difficult for the HO&A team to fully utilise and trust the system’s outputs. A key issue was the lack of understanding within the team regarding TRM’s logic and dependencies, leading to inefficiencies in its usage and maintenance.
The challenges associated with TRM’s existing setup included:
Performance Bottlenecks & Scalability Constraints – SQL Server struggled to handle the increasing complexity and volume of data, leading to slow query execution times.
Lack of Maintainability & Code Complexity – Years of technical debt and under-documented processes made it difficult for the team to troubleshoot and enhance TRM’s functionality.
Limited Integration with BigQuery – Since TRM was still running on on-premises infrastructure, it created data silos, preventing seamless integration with OVO Energy’s existing BigQuery-based analytics ecosystem.
High Operational & Infrastructure Costs – Maintaining on-premises SQL Server infrastructure required ongoing support, driving up costs and diverting resources from strategic initiatives.
To address these challenges, we embarked on a structured migration plan to modernise TRM, ensuring it aligned with the company’s cloud-first approach while improving its performance, maintainability, and usability.
Solution
To ensure a seamless migration of the TRM from on-premises SQL Server to Google BigQuery, we implemented a structured approach focused on code optimisation, stakeholder collaboration and robust testing. The goal was not only to migrate to the cloud based data solution but to also enhance the efficiency, maintainability, and performance of the TRM system.
The team began by conducting an in-depth audit of the existing TRM codebase to identify underutilised and underperforming components. This process helped streamline the migration by removing unnecessary complexity and ensuring that only valuable, business-critical functionality was maintained.
Recognising the knowledge gap within the team, we engaged closely with stakeholders in the HO&A team to gain a deeper understanding of the business requirements and functional objectives of TRM. These insights informed key improvements to the system, aligning the migration with OVO Energy’s long-term strategic goals.
To enhance system stability and reliability, we introduced a suite of integration tests, based on the business requirements, that validated the migration process and ensured consistency in data processing between SQL Server and BigQuery. This rigorous testing framework provided confidence in the new architecture, allowing for a smoother transition while minimising operational risks.
Implementation Process
Starting with codebase audit & optimisation, we conducted a thorough review of the TRM codebase to identify outdated, redundant, and inefficient areas. We Removed underutilised and underperforming code, streamlining the logic for better performance in BigQuery. We also audited BigQuery data sources to ensure consistency with legacy SQL Server data source and finally rewrote and updated SQL queries pointing at newer data tables in BigQuery .
Throughout the process, we continuously engaged with key members of the HO&A team to understand business objectives and ensure TRM continued to meet its strategic purpose. In collaboration with stakeholders, defined essential features and workflows required for the new BigQuery implementation.
We designed and implemented a comprehensive set of integration tests to validate the functionality of TRM in BigQuery, ensuring data consistency, query performance, and business logic integrity post-migration.
Finally, to execute migration, we rebuilt the TRM data processing pipeline to leverage BigQuery’s scalable serverless infrastructure. We also performed incremental testing and validation to ensure a smooth transition with minimal disruption to operations.
Results and Benefits
The migration of the Trading Requirement Model (TRM) from SQL Server to Google BigQuery delivered significant improvements in performance, scalability, and maintainability for OVO Energy’s Hedging, Optimisation & Analytics (HO&A) team. By optimising the TRM codebase, removing inefficiencies, and aligning the system with modern cloud-based infrastructure, the project achieved the following key results:
Enhanced Performance & Scalability – Query execution times were significantly reduced, leveraging BigQuery’s distributed processing power to handle large datasets efficiently.
Improved Maintainability & Code Quality – The removal of redundant and underperforming code simplified ongoing maintenance, making the system more sustainable and easier to manage.
Better Integration & Data Accessibility – TRM is now fully integrated into OVO Energy’s existing BigQuery ecosystem, improving data sharing and real-time analytics capabilities.
Reduced Operational Overhead – The migration eliminated the need for on-premises infrastructure maintenance, lowering costs and resource requirements.
Increased Reliability & Accuracy – The introduction of a robust integration test suite ensured data consistency and system reliability, reducing the risk of errors in future enhancements.
Conclusion
The successful migration of TRM to Google BigQuery marked a significant milestone in OVO Energy’s cloud-first data strategy, as TRM is a critical financial hedging system for the company. By eliminating legacy infrastructure constraints and improving system efficiency we helped the HO&A be more data-driven in their decision-making.
This project not only addressed existing challenges but also laid the foundation for future innovation, enabling OVO Energy to leverage advanced analytics, AI/ML capabilities, and real-time data processing within its BigQuery environment. With a more scalable, cost-effective, and maintainable solution, OVO Energy continues to strengthen its commitment to operational excellence and sustainability, ensuring its data ecosystem supports the company’s long-term growth and strategic objectives.