3. Invest in Data Quality & Governance


A well-governed data environment is crucial for mitigating compliance risks, ensuring regulatory adherence, and improving analytical accuracy in commodities trading. Given the increasing complexity of reporting obligations (REMIT, EMIR, MiFID II) and growing scrutiny from regulators, companies need to maintain a transparent, auditable, and high- integrity data ecosystem.


Key best practices include:


• Automated Data Validation – Ensuring that incoming data from multiple sources is checked for accuracy, completeness, and consistency in real time. This reduces the risk of erroneous trades, flawed risk assessments, and inaccurate P&L reporting.


• Robust Lineage Tracking – Implementing end-to-end data lineage tracking allows companies to trace every data point from source to decision-making. This is critical for demonstrating compliance, identifying discrepancies, and ensuring accountability in audits.


• Strong Security & Access Controls – Data breaches and unauthorised access can lead to financial loss, regulatory penalties, and reputational damage. Role-based access, encryption, and multi-factor authentication help safeguard sensitive trading and risk data.


• Standardised Data Models – Harmonising data structures across E/CTRM (Energy/Commodity Trading & Risk Management) systems, analytics platforms, and reporting frameworks ensures data consistency and interoperability across different teams and locations.


• Proactive Compliance Monitoring – Embedding automated rule- checking and anomaly detection helps companies stay ahead of regulatory changes and catch potential compliance violations before they escalate.


By embedding these best practices into their data architecture, governance frameworks, and daily operations, companies can not only reduce risk but also enhance their ability to extract valuable insights. A strong governance model ensures that traders, risk managers, and compliance teams can trust the data they use for decision-making - turning governance from a regulatory burden into a strategic advantage.


4. Leverage Automation & Machine Learning


Automation helps reduce the manual workload of data processing, eliminating inefficiencies and enabling analysts to focus on high-value tasks such as strategy development, risk assessment, and market insights. In commodities trading, where vast amounts of market data, trade execution records, logistics information, and regulatory updates flow in continuously, manual data handling is no longer viable.


By implementing automated data pipelines, you can:


• Ingest and clean raw data in real time, ensuring accuracy and consistency across trading platforms.


• Standardise and enrich data by automatically mapping different data sources into a unified format, reducing errors and improving usability.


• Trigger event-driven workflows, such as automated alerts for price movements, weather disruptions, or regulatory changes, allowing traders to react immediately.


Beyond automation, machine learning (ML) models enhance predictive analytics, enabling companies to anticipate price movements, volatility trends, and supply- demand shifts with greater precision. Advanced ML models leverage historical and real-time market data to:


• Identify trading patterns and correlations that might be missed through traditional analysis.


• Optimise hedging strategies by dynamically adjusting positions based on forecasted market conditions.


• Improve risk management by detecting anomalies, stress-testing portfolios, and quantifying exposure in volatile conditions.


By integrating automation and predictive analytics, companies not only improve operational efficiency but also gain a competitive edge in decision-making, turning data from a reactive tool into a proactive asset that drives smarter, faster trading strategies.


11 | ADMISI - The Ghost In The Machine | Q3 Edition 2025


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