Written by Steve Tzortzidis
Published in iTWire 20 June 2025
GUEST OPINION: Regulatory complexities and a highly risk-sensitive approach are limiting opportunities for automation and real-time decision-making in the energy and utilities sector. A robust data governance framework can support energy organisations to reach their full data potential.
Federated data governance has emerged as a transformative approach. It establishes decentralised data management while maintaining centralised oversight. Business units can manage their data domains independently but within predefined guardrails.
Federated data governance supports innovation and agility, while ensuring compliance and data integrity across the organisation.
Overcoming data fragmentation for a retail energy provider
An Australian energy provider delivering retail electricity nationwide relied heavily on data to provide insights on operations and capacity planning, but faced significant challenges due to a complex and fragmented data architecture.
Centralised governance and control led to inefficient data management systems, and operational costs increased as data volumes grew. This structure also hindered business stakeholders from accessing real-time customer data, delaying decision-making and causing project delays and disruptions.
V2 AI, Australia’s leading data and AI consultancy, worked closely with the energy provider to understand the whole-of-business data requirements against business outcomes, mapping existing data flows across business units and use cases. We implemented modern tools and data product capabilities to enable decentralised self-service data access across departments. Automated governance controls ensured regulatory compliance within the decentralised approach.
This empowered business units to manage their data products independently within the necessary guardrails and rules, ensuring data integrity.
This shift resulted in immediate operational uplift and efficiency. It reduced integration bottlenecks and improved data responsiveness to business needs, meaning little to no delays in key decision-making and analysis. It allowed for greater agility in responding to market and operational changes.
With a positive ROI and enhanced innovation capability, the federated governance structure has resulted in a platform poised for AI enablement, built on strong data foundations and improved risk management.
Implementing a successful federated data governance operating model
Federated data governance requires energy organisations to establish clearly defined data domains across business units. All business units define and agree on authoritative data sources and common data definitions.
Business units maintain autonomy but manage their data according to centralised security and quality standards. Data stewards and owners within each business unit are responsible for quality management.
Automation can serve as a key enabler, monitoring and raising alerts against internal data quality standards, as well as security and compliance requirements. Modern data platforms that support automated governance controls, data validation and audit trails make implementation practical and efficient.
Transitioning to federated data governance in the energy sector
The transition to a federated data governance model required a high degree of business and IT involvement along with change management strategies. However, the benefits are evident.
A centralised, one-size-fits-all data governance approach can become a blocker for teams trying to deliver the utmost value for the business. In contrast, a well-executed federated governance model strikes a balance between control and flexibility. It encourages innovation while enhancing security, risk management, and compliance. Intelligent, automated data governance becomes a powerful enabler of business transformation.
Federated data governance is proving to be a differentiator in the energy and utilities sector. V2 AI has recently published an eBook titled "The Intelligent Enterprise: A Data Leader’s Guide," offering practical advice and use cases to help enterprises adopt a federated approach to data by creating high-quality, scalable, user-centric, and self-service data products that drive business growth.
