Big data’s time has arrived in commodity trading
A recent Oliver Wyman report stated commodity trading is now a ‘cutthroat environment’. A major trading margin squeeze has been playing out for a while now amongst commodity trading firms, with margins falling by more than 20% from their 2015 peak. The industry is clearly entering a new chapter in its history as firms grapple to regain advantage. Big data is one of the ways firms can seize such advantage.
What is big data?
Big data is basically data with high volume, high velocity and high variety. Big data technology solutions observe and track what happens from various sources that can include anything from business transactions or social to information from machine-to-machine or sensor data. Absolutely huge volumes of data are produced. It streams in high speed and requires processing in a timely way to produce near or real time results. It also comes in formats which may be either structured, numeric as in the traditional database, or in unstructured text documents or in video, audio or email formats.
What are the implications for commodity trading?
Such ‘big data’ has massive implications for commodity trading. Refinitiv (previously Thomson Reuters Risk & Financial Division) in their recent survey of over 700 commodity professionals, found more than 60% of respondents expected their firms to increase budgets for data management, driven by a need to cut total cost of ownership, centralize data, reduce operational risk and create an analytics platform to better manage growing complexity in commodity trading data management.
In another survey amongst the same audience by ComTech, big data and data analytics were viewed as having the second greatest short-term impact to their firms after cloud technology and as definitely being an area for increased investment. It was also seen by 65% of respondents as the area most likely to bring the most disruption to the commodity trading market over the next 2-3 years. The survey concluded three things are most important for commodity trading – automation (for collection and validation), visualization and real-time capabilities. Predictive analytics was seen as a key real-time capability.
The power of predictive analytics
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling and machine learning that analyse current and historical facts to make predictions about future or unknown events. Doing this well will give commodity traders information advantages and enable them to draw valuable proprietary insights. However, as the Wyman report states, it’s the larger players that are currently investing in big data and predictive analytics now as they have sufficient scale to monetise results profitably and the capital to invest in the big data systems required. The report concludes: “Predictive analytics provides commodity traders with the opportunity to regain their information edge. But they will have to invest quite significantly. You need to have deep pockets.”
How Digiterre can help
Big data and predictive analytics is clearly a key area for gaining proprietary insights and building competitive advantage in commodity trading. If you have any questions about it or are seeking a partner to assist you harness the opportunities from big data, do get in touch with Digiterre. Digiterre has extensive experience designing and implementing technology solutions across the energy and commodity, banking and insurance and investment management industries. We typically deliver technology solutions requiring business agility, adaptability, innovation and a proven track record in order to solve high risk, high profile and complex technical challenges, very well and very fast.
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