“With all visualisations you are essentially telling a story,” Smith says. “You can identify trends and differences.” Refinitiv’s visualisation work has multiple levels and is based on the company’s huge volumes of public and commercially available data. Its data management platform allows businesses to access Refinitiv’s proprietary content, bring in their own data and then innovate with it. The systems include real-time interactive maps which show the position of every ship in the world and specific details about commodities businesses. For instance, if you click on an oil storage facility, you can see the latest data about its supplies and oil flows. Click on an agricultural site, and rainfall data can be used to help predict potential crop yields.
These interactive maps are supplemented with graphs and visualisations that can be generated by those with data science skills. Refinitiv staff are able to use the Python coding language to interrogate data and create new applications. Of particular use, Smith says, are interactive Sankey charts. These diagrams can show the movement and amount of a particular commodity from its source to its destination. An oil producer in West Africa, he says, could use one of the charts to see what grade of oil their competition is buying, where it is going in the world and how they stack up against them. This intelligence would give the oil producer a better understanding of the market and the actions of their competition, helping them to make better-informed business decisions.\
This type of analysis and visualisation, which can be updated automatically when the data is refreshed, will become increasingly important as more companies unlock the potential in their data. Many businesses are likely to use predictive analytics to prepare for what may change in the market – if they do, they may be able to get ahead of competitors and move faster on critical decision making. Sonia Ghosh, a business analyst and digital ambassador at energy company VARO Energy, says the analytics being used ahead of trades is becoming increasingly sophisticated.
“Data analytics capabilities with intuitive visualisation are used to draw meaningful market intelligence from raw feeds to unlock new revenue streams and mitigate risks better,” Ghosh says. She adds that visualisation and analytics are becoming more sophisticated as artificial intelligence and machine learning are being more routinely deployed by businesses. “AI is deployed to analyse signals for algorithmic trading strategies,” she says, adding that machine learning is being used to “validate models” by analysing how predictions varied from what actually happened.
Building on a data-led advantage
Tableau’s Zimmermann agrees that the emerging technologies are fundamentally changing how companies are doing business. Increasingly, artificial intelligence and machine learning will be used within data analysis and visualisation to help traders and financial institutions respond to changes in their data. “Their AI/ML will notify them of which metrics across all their dashboards are unusual, so that they can jump straight to the most relevant views of their business,” Zimmermann says.
To take advantage of this more automated future, companies need to ensure they have robust data management systems in place and are able to effectively unlock the value that’s contained
in the large datasets they have available to them. That begins with getting the basics right: companies need to understand the data they have, what types of additional information they can access, and how they can then build on top of it.
“Technology has arrived and data has been democratised,” says Alessandro Sanos, global director, sales strategy and execution for commodities at Refinitiv. This means that financial organisations and companies need to properly understand how to make the most of
the data they have in their system. If they don’t, they will fall behind. “The competitive advantage is really evolving from being able to access those additional sources of data, to how well companies can integrate it, commingle it with the data that they produce themselves, and then apply technology to generate insights,” Sanos says.
This is the final ebook in a four-part series on how businesses can prepare for the future of commodities trading, created in partnership with Refinitiv. To explore the others, click on the below links:
1.Making big data work for commodities
To find out more about Refinitiv, click here
To find out more about WIRED Consulting, click here
This article was originally published by WIRED UK
