Synopsis
While some energy companies are painstakingly unwinding legacy systems, others are building massive greenfield data platforms from scratch to capture the renewables market at pace. This breakdown of the recent Ignite Breakfast Club explores the contrasting strategies of Alinta Energy and Squadron Energy, highlighting how industry leaders are tackling governance, physical asset integration, and the realities of AI deployment.
Organisational Perspectives on Data: Data maturity isn’t one-size-fits-all; leaders must choose between a multi-year, iterative modernization path or a rapid greenfield approach designed to deliver real-time data platforms concurrently with physical asset construction.
Critical Risks and Governance: Instead of acting as an IT bottleneck, effective data governance must serve as a scaling guardrail against sophisticated AI-driven cyber threats and continuously shifting regulatory compliance targets.
Operational Technology and Generation Data: Pushing real-time Process Information (PI) from physical assets into modern platforms enables AI-driven predictive maintenance, provided you can extract the data without over-engineering the transfer or exposing critical infrastructure to cyberattacks.
The Role of AI: The sector is shifting away from static dashboards toward natural language interfaces, but unlocking this capability requires doing the boring, foundational work of cleaning up duplicated data pipelines first.
Ignite recently held a breakfast with:
- Chris Pratt, GM Energy Supply, Alinta Energy
- Zev Friedman, Chief Technology Officer, Squadron Energy, and
- Michelle Irrgang, Head of Data Management, Squadron Energy
The session captured how each organisation’s data journey in the energy sector is unfolding. The discussion contrasted Alinta Energy’s mature, iteration-led data environment with Squadron Energy’s accelerated, greenfield strategy designed to take a significant market share of the renewable energy market, at pace.
Here are the highlight notes we took from the session, capturing the key themes.
Organisational Perspectives on Data: Your strategy must reflect the market position of your business
The panel highlighted two distinct stages of the data maturity lifecycle:
- Alinta’s Mature Path: A multi-year, iterative journey focused on refining data quality, transitioning legacy systems to modern platforms (like Databricks), and shifting from manual processes to automated insights.
- Squadron’s Accelerated Growth Path: A rapid greenfield approach designed to make a substantial contribution to Australia’s decarbonisation objectives through the development of the nation’s largest wind farms. This requires the concurrent delivery of accurate, large-scale, real-time data platforms alongside physical asset construction.
Critical Risks and Governance: Differ depending on ownership structure, customer base and products offered
Data management in energy is increasingly defined by external pressures and the need for scalable foundations:
- Cybersecurity and AI Threats: Cyber threats are evolving, with Artificial Intelligence (AI) being used to create highly sophisticated phishing campaigns that are increasingly difficult for employees to detect. Combined with significant FIRB requirements, many organisations have to continue to build, enhance and extend their systems.
- Compliance Moving Targets: Continuous upgrades to regulatory requirements (such as SOCI upgrades and market bidding compliance) require data systems to be highly adaptable.
- Governance as an Accelerator: Effective data governance is viewed not as a bottleneck, but as a guardrail that allows an organization to scale quickly without restarting from scratch for every new asset.
Operational Technology and Generation Data: How to integrate asset data
Integrating physical asset data (wind farms, batteries, gas terminals) into business logic requires a methodical approach that connects the data while minimising the costs.
- Security: To prevent cyberattacks from physically affecting power assets, data must be transferred from site, without over engineering a costly data transfer mechanism, while ensuring no network weakness.
- PI Integration: Using Process Information (PI) frameworks allows engineers to see real-time graphical vision screens, which helps prevent site trips and improves reliability through immediate feedback from engineers to site operators.
- Enabling Predictive Maintenance: Moving PI data into platforms like Databricks enables the use of AI for predictive maintenance, though the underlying site data must be robust before these advanced connections provide value.
The Role of AI: how to think about AI for your organisation
Like most industries, the energy sector is transitioning from statistical forecasting to Generative and Agentic AI and is facing a universal set of issues:
- AI Productivity for Data Engineers: AI is being used as a starting point for code snippets and requirement gathering, shifting the role of data engineers from doers to reviewers and sanity checkers.
- The Death of the Dashboard: Panellists predicted a shift away from just relying on traditional PowerBI dashboards and toward Natural Language Interfaces (such as Databricks Genie), where users ask questions of a trusted data source rather than only dashboards.
- Foundation First: Success in AI is dependent on the boring work being done well: data ingestion pipelines, cleansing 1,500 duplicated tables down to eight hundred, and ensuring a trusted gold layer of data.
Key Takeaways for Data Leaders
Prioritise
Focus on getting dollars in the door.
Partner with core business users to stack activities based on the value chain and ROI.
Convincing Finance
Re-platforming rarely makes money instantly.
Use technical benefits (storage cost savings) or storytelling about missed opportunities to get finance approval.
Highlight how data can unlock AI opportunities in your business case – speeds up executive support!
Evolve your team
To move beyond basic data, you need a mix of Data Engineers (ingestion), ML Engineers (frameworks), and Data Scientists (scenarios).
Strategy
Start the journey early.
Move data across incrementally to unlock small projects and build support from senior stakeholders.
