Unlocking data’s potential: How cloud and AI are transforming commodities
This future may be closer than many people realise. Adoption of cloud computing technologies has been accelerated by the widespread remote and hybrid working ushered in by the pandemic. As companies had to relocate their workforces in 2020 and 2021, their infrastructure also needed to shift to match the new realities. “I think when you look back on the history of the cloud, we will see that the pandemic accelerated adoption of this technology by several years,” Amazon’s Jassy said at the end of 2020.
Staying in control
Improper data management represents a significant business risk. And, increasingly, that danger is coming from inside a company’s own walls. Staff members, whether on purpose or by accident, can put their organisation at risk by leaking or making proprietary data available publicly. In the first quarter of 2021 alone, 57 per cent of cybersecurity incidents reported to the UK’s data protection regulator were caused by insiders.
Effective controls on data flows and who can access data are crucial for protecting business information. Refinitiv’s Real-Time Data Access Control System allows companies to manage who is able to access what data, create auditable records of who is able to publish certain types of data, and manage the overall control of it. The system is also able to identify people who have access to certain types of data, but who aren’t actually using it – something that can cut down the risk of data leaks, unintentional or otherwise.
Putting the data to work
Burak Tutar has a clear idea of what his future looks like: “In five years’ time, our vision is for our company to have fully automated data and execution systems that are maintained by data engineers, where traders are only involved in developing strategies together with quantitative analysts,” says Tutar, who is the founder of AI commodities trading firm Vitus Commodities.
At the heart of this future vision is the use of artificial intelligence and machine learning. Tutar says the company, which focuses on the energy markets, is using AI to build prediction models capable of forecasting the wind on a medium-term basis. This relies on accurate weather forecasting – something that is notoriously difficult to get right – but also the ability to manage data, utilise cloud computing and deploy machine learning systems on top.
If successful, it could result in more accurate and cheaper pricing
forecasts, automated decision-making for the company and, eventually, automated energy networks. Tutar says that commodity trading houses and the banks of Wall Street are making “somewhat of an effort” to add fundamental data – real-time information about the world, such as satellite images or oil supply levels provided by sensors – into their trading setups, but adds that these are “falling short”. “The main reason for this is simply due to organisational memory and a ‘this is the way we do business’ approach,” he says.
To get to a stage where they can properly utilise the cloud, big data and AI, the organisations need to have the platforms and the requisitely skilled staff in place to do so. Taking in or ingesting data is crucial to this. Refinitiv says its Refinitiv Data Management Solution (RDMS) can provide standardised data that companies can ingest into their own businesses through the cloud, via simple API integrations. It can push all of its data into their data warehousing systems, or help create those systems from scratch.
