Why Data Management is Foundational to Generative and Agentic AI Success in Energy Trading

In the race to leverage AI across energy marketing and trading, one truth remains constant:

AI is only as good as the data it’s built on.

From generative models that produce forecasts and reports, to agentic systems that autonomously act on real-time inputs—data is the lifeblood of AI. Yet, many power and gas trading organizations are attempting to pilot or scale AI capabilities on top of data environments that were never designed for it.

Poor Data Quality = Poor Decisions

Generative and agentic AI models require massive volumes of data that are:

  • Accurate
  • Timely
  • Context-rich
  • Consistent across systems

Without this, AI systems can:

  • Surface biased or misleading insights
  • Misinterpret market conditions
  • Automate flawed decisions
  • Undermine confidence in digital transformation efforts

For example, if historical trade data is inconsistent across systems or missing key attributes, an AI model may mis-forecast P&L outcomes or misjudge collateral needs. Likewise, duplicate or misaligned reference data can lead to contradictory reports and audit risk.

Foundational Data Challenges We See

At MidDel Consulting, we frequently encounter:

  • Heavy reliance on spreadsheets for key commercial operations
  • Mismatches in trade book structures across ETRM, risk, and accounting systems
  • Poor data lineage and lack of traceability for compliance and audit
  • Redundant or siloed data integration processes leading to reconciliation efforts
  • Inconsistent handling of reference data like curves, assets, and counterparty definitions

These are not just technical nuisances—they are strategic blockers to realizing the value of AI.

The AI Readiness Roadmap Starts with Data

Before implementing or scaling generative or agentic AI tools, organizations must address foundational data gaps. This includes:

  1. Assessing Current-State Data Architecture
    • Understanding how data flows across front, middle, and back office
    • Mapping where inconsistencies, duplications, or gaps exist
  2. Improving Data Governance & Stewardship
    • Establishing clear data ownership and validation rules
    • Implementing reference data management and master data repositories
  3. Integrating Data Across Systems
    • Rationalizing feeds across ETRM, ISO settlement, accounting, forecasting, and risk
    • Leveraging cloud-native pipelines or middleware to scale efficiently
  4. Aligning Reporting and Analytics
    • Ensuring consistency in metrics, calculations, and definitions across reports
    • Readying datasets for BI tools and AI/ML models

How MidDel Consulting Helps

MidDel has deep experience helping energy companies modernize their data environments to unlock the full potential of analytics, forecasting, and AI. Our team of senior consultants brings expertise across commercial operations, system integration, and reporting to help:

  • Conduct data management assessments with an AI-readiness lens
  • Define and implement data quality frameworks and governance structures
  • Support data integration and rationalization across ETRM, ISO, risk, and finance systems
  • Prepare datasets for advanced analytics and AI use cases

Whether you're exploring pilot AI use cases or scaling to enterprise-level automation, clean, governed, and integrated data is the foundation for success.

Ready to Future-Proof Your Data?

If you're planning to invest in AI or struggling to see results from existing efforts, let's start with a conversation about your data. MidDel can help you assess where you are—and build a roadmap to where you need to be.

Contact us to get started.