Andrea Stone, the CEO of Zema Global, is passionate about the positive impact and contributions digital tools and processes can make to commodity trading. Her discussion with Charles Osborne, Director at Trade Finance Global (TFG), makes this passion infectious.
For Stone, digitisation will most impact commodity market data analytics. Admittedly, data, facts, numbers, or symbols are the heartbeat of the financial sector. In an industry characterised by player-versus-player interactions, there’s a need to find an edge. The ultimate edge is how fast, accurately, and efficiently one can process information. This is where data analytics comes in. With data analytics, traders can better understand patterns and trends and gather insights.
Stone emphasised that “about 30% of our customers’ P1 outages, where they can’t complete a hedge, they can’t complete a trade, they can’t schedule a shipment, are because of missing data.”
Current economic and geopolitical climate
Since Q1 2025, the US President Donald Trump-led administration has ensured a wave of turbulence and instability across global trade through its tariffs. The commodity market, which has always been volatile and risk-bound, was not spared. For instance, aside from the initial crash in oil prices, a $1 million drop in oil demand is still predicted in 2026.
The above, coupled with wars and political unrest in some countries this year, exacerbated the volatility, volume, and variability of data available in the commodities markets. It has also necessitated the need for efficient data analytics for decision-making.
How data analytics responds
According to Stone, “There are three aspects of data that we help our customers manage. It’s the volume, variability, and volatility. We take all of that data and bring it together. That’s step one.
“The second part of your variety of data. We have been putting in various checks and balances for many years.”
Here is how data analytics can help enhance traders’ performance in the commodities market:
- Operational efficiency: For financial institutions, this means anything from hiring more staff to spending more time tracking and analysing events. Data analytics can help resolve this and increase revenue generation. By automating data flows, commodity traders bypass traditional problems like delays and can instantly access crucial information. Similarly, data analytics leaves little to no room for errors in commodities trading. With the clarity in data management systems, every data point undergoes scrutiny and flows into each other without clustering.
- Risk management: Typically, commodity traders are often exposed to price volatility risks. Aside from the pulling forces of demand and supply, seasonality, and the economic environment of countries all contribute to the fluctuation of commodity prices. Unforeseen and unavoidable incidents also contribute a fair quota.
For instance, in 2021, about 5 million bags of Arabica coffee harvested in Brazil were lost due to a frost. Data analytics can help prevent losses like these through leveraged data, price forecasting, and advanced analytics. Furthermore, data analytics tools can also simulate trade scenarios and predict their outcomes. This helps evaluate each potential outcome, thus reducing risk.
- Improved transparency: Proper documentation and interconnectedness of data systems can help improve the transparency of commodity trading firms. A transparent data structure will reduce fraud and misappropriation of commodity trading funds. Good corporate governance practices like this are essential for business growth and longevity.
Ensuring data quality and integrity.
Indeed, the importance of data in the financial sector or its availability is rarely in contention. The problem lies in the quality and integrity of data. This burden rests mostly on the sources from which the data is drawn. Data is everywhere, especially if you’re searching eagerly for it; however, high-quality data isn’t. High-quality data is what you need to make important business decisions. An AI survey reported that businesses lost around $400 million due to poor-quality data.
“[You need] decision-ready data that is going to be both predictive, historical, real-time, and as traded. All of these data sets will start interacting with each other,” explained Stone. She highlighted how at Zema Global, they consider pricing data, market data, weather data, transport (cargo or shipping) data, sensor data… the list goes on.
Data analytics experts in commodity trading then sift through the various kinds of data and refine it to deliver the best decision-ready data to their clients. Here’s how they do it:
- Setting data standards: The first step towards ensuring one gets the best available data is creating filters. Despite the complexity in the data ecosystem, it’s important to set out requirements and criteria for the data collected and analysed. These standards should be consistent in each stage of data analysis up until the point of decision-making. One way to achieve this is by including the standards in the business’s data governance policies. Data standards should include checking for uniqueness, accuracy, completeness, and more.
- Employ data testing tools: Standards and processes are not enough; technological tools are needed to ensure better data quality and integrity. Data testing tools help reduce the burden of testing for data quality. They can perform tasks like data visualisation, profiling, and cleaning. They can also be integrated into existing data pipelines, and they have alert features for urgent situations.
- Monitoring: It is important to build a culture of consistently monitoring and improving data systems. This helps enforce data quality standards over time. To address this need, it would be helpful to create a role or employ the services of a vendor. Organising employee training programs will also contribute to maintaining data integrity and quality.
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Traders who outperform in the commodities market never underestimate data. At its core, commodities trading is a game of numbers, and it’s important to consider facts and figures in order to win. Over the last few years, analytics has been changing the game of commodities trading, and its impact can only get better.