As well as being potentially a challenge to deal with effectively, data is also many firms’ biggest asset, alongside the people who use it to generate or protect value for the company. Trading firms drive a profit precisely because they can understand and value commodities’ quality data, regional arbitrage, logistics and costs, as well as clients, suppliers, markets, and the risks inherent in each of these.
It would therefore make sense that the more data traders have access to, the better the risk mitigation, and the more opportunities for profitable trading. But the data challenge is often not about finding even more data. Unless you have very strong data systems already in place, the real challenge is frequently about unblocking data silos to ensure your people can drive insights and results from information that already exists within the company.
Data without insights is just noise, and adding data without adding the ability to act on it simply increases workloads without improving results. So, a large part of the data challenge is about getting the right data into the right hands in the right format at the right time.
For a long time, CTRM systems have played a role in solving this data challenge, particularly in attempts to unify trading and financial data. But they historically overlooked many of the data flows within trading organisations; including operations/logistics, traceability and sustainability, and even credit and counterparty risk.
But the CTRM software environment has been changing rapidly over the last decade or so, with a new software class of Commodity Management Systems (like Gen10’s CommOS) evolving to fill the functionality and data limitations. CommOS is designed to connect all your internal teams and power communication across your entire supply chain. A Commodity Management System acts as a data warehouse for your organisation, storing structured data and processing it so that people can access the insights they need.
Data in a Commodity Management System
Commodity traders already have visibility over where they sourced stock, possibly along with further origin details, quality data, and certifications (belonging to either the stock itself or the counterparty), pricing data, your own logistics data (which may include vessel trackers and carbon calculations), counterparty information, and much more.
As well as using this data to make better trading and logistics decisions, there are many risks that can be monitored and mitigated against just by making better use of the data you already have. For example, reporting on origination and warehousing helps you understand concentration and weather risks as well as your exposure to any regional disruption (operational risk). And reporting on counterparties can also include new ways of looking at risk, such as understanding which counterparties are regularly late payers and whether that therefore impacts your own liquidity risk.
This use of data takes risk outside of just the financial sphere. It means that traders can carry out more and better what-if analysis as part of their standard flow of work, operators can be more aware of where delays tend to occur, and financial risk managers have all the insights they need to incorporate risk into wider workflows, creating a function that supports trading teams, rather than one that interrupts them.
Providing all teams with the data they need to do their jobs also moves the business away from compliance-driven reporting and reporting as a burden, towards data-driven commodity trading strategies that drive more value for the company.
Flexibility and agility
As people carry out their daily tasks, they are also making changes and updates to the data; a trade becomes hedged, a lot becomes allocated, inspections change quality and pricing data. It is therefore vital that the updated information is shared instantly between these well-connected teams, enabling the changes they make to be reflected across the Commodity Management System and all connected systems in real time. A good CMS like CommOS allows you to pull in data from a wide range of sources and set up two-way integrations so these data flows update throughout the day.
And a Commodity Management System needs to be flexible. Cutting through the noise to find actionable information depends on what the individual is looking to achieve, so the CMS needs to reflect a wide range of job roles, and have the flexibility for individuals to personalise how they use it, for example by creating their own dashboards so they get the insights they need at a glance.
The Commodity Management System also needs to be easy to integrate with other systems when the business adds new software, tools, or data sources; as we are currently seeing in the push for new AI tools in commodities. New data sources and tools need to be integrated into your existing ecosystem to maintain the real-time data flows, or they risk creating data silos. This means your organisation has access to more information, but is not getting good use from it – data silos mean either data is only held by one team or that people are manually copying information between systems, creating errors and a lack of traceability. This can also lead to less trust in the data as people can’t see where it originated.
So, what’s next?
We are in an exciting time for data in commodities, there is both the will to innovate within organisations and a wealth of new tools being unlocked by AI advances. This is giving commodity traders more options than ever before in terms of the data they have access to and how they use it. But the real challenge still lies in how to get real value from this data. And the first step to solving that challenge is to build the data flows to get it where it needs to be.
If you’d like to explore how other organisations are using data to generate better trading insights and drive real business results, join us on 19th November for the panel session The Next-Gen of Intelligence for Commodity Trading Desks live at Digitalisation in Commodities Online.
The panel will discuss the challenges of data access and commodities digitalisation, how to effectively incorporate new data sources, and cutting through the noise to create actionable insights. They will also explore the critical success factors that lead to some organisations appearing to thrive with new data processes while others lag behind, and of course, how AI fits in to solving the data challenge.
