Posted on : July 22nd 2022
Author : Jishnu Gupta CTO at Straive & Somil Goyal, Sales
As digital transformation disrupts and reshapes the commodity trading industry, market participants now have access to vast amounts of data. Data growth is occurring across all commodities—energy, metals, industrial raw materials, and foods.
Simply amassing data does not offer enterprises an advantage. They need to innovatively analyze and derive actionable insights from those data, which is easier said than done. At Straive, we have observed four trends highlighting the data challenges in the commodity markets:
New alternative data sources and the vast volume of data are making data management and analytics complex.
Legacy tools are unable to make alternative and unstructured data relevant and actionable.
Recognizing these challenges and numerous others, progressive firms leverage digitalization and technologies such as machine learning and artificial intelligence (AI) to gain greater market insights. Yet, according to a survey, only 30% of the enterprises report realizing tangible and measurable value from data.
Moreover, the data available for analysis is becoming more granular. For instance, market observers have used satellite images of tanker traffic and storage levels to assess global oil supplies. Using tanker traffic data can improve the timeliness of trade statistics and increase an analyst’s ability to detect emerging risks and turning points in the business cycle. Ship-by-ship and port-by-port data will reveal international trade patterns, which could help, for example, make sense of Russia's contradictory oil data showing falling production but increasing seaborne shipments.
Therefore, enterprises should consider steps to standardize large data sets and integrate them into their workflows.
The democratization of data across many sectors has opened new possibilities for traders and analysts. In addition, the COVID-19 pandemic accelerated the digitization of customer and supply-chain interactions and internal operations by three to four years, meaning that enterprises now have more data about their trade and supply levels. Combined with news, shipping, manufacturing, product schedules, inventory levels, and other data, these data offer enterprises a global view of the commodities market, which is essential for data-driven decision-making.
A global view is particularly relevant because commodities differ from other asset classes. Consequently, they require more varied alternative data for analysis. For example, the latest statistical data and real-time analysis help determine energy demand and CO2 emissions.3 They also provide insights into how economic activity and energy use around the world are rebounding from the adverse effects of the pandemic. Insights from this type of alternative data will help investors maximize the value of their assets and hedge commodity price risks.
The key to leveraging the power of alternative data is efficiently acquiring, enriching, and transforming commodities data at scale into directly usable data to gain near-real-time insights. This process is challenging, however, because the commodities market is placing increasing reliance on unstructured or alternative data, including the following, for gaining deep insights:
In addition, the frequency of commodity market information varies widely, depending on the data source, and can range from daily to annual data.
Alternative data sets, such as the satellite data used for forecasting the supply/demand of agricultural commodities and tracking mining and shipments of metals and iron ore, offer unparalleled insights. But there’s a catch: They must first be acquired; satellite data are available from several locations and are voluminous. For example, the Landsat 8 and Landsat 9 satellites together capture approximately 1,500 scenes daily that must be harmonized irrespective of their preprocessing level. Subsequently, data can be extracted from these images and transformed directly into consumable data that seamlessly plug into data workflows.
Making data sets compatible with other data sets is a complex task, which is only becoming more so because of the furious pace at which data are being added. To work together, the fields in each data set should match, and there should not be inconsistencies. Therefore, many processes leverage the latest technologies, insights from subject-matter experts (SMEs), quality assurance checks, and the like should be in place. These processes are critical for transforming alternative data into directly consumable data that can be analyzed and visualized to gather deep insights and easily identify the links, relationships, and connections between the data points.
Straive has dedicated SMEs from multiple domains and a proprietary AI-enabled Straive Data Platform (SDP) to meet these alternative data standardization challenges by leveraging SDP to acquire, enrich, transform, and deliver alternative data at scale. SDP uses AI and ML algorithms combined with a business rules framework to offer data management as a service. Secure data processing provides prebuilt connectors and multiple ingestion paths to capture, unify, and action data across various touchpoints.
ML and natural language processing capabilities that enable intelligent document processing of complex and variable documents formats
Simple, customizable workflows ensure that SDP delivers directly consumable data that seamlessly integrates with clients’ legacy data workflows, for example, data—emails, fax servers, file systems—can be ingested from any source or through integrations with other applications
Cleaned, structured, and linked data are essential for efficient and beneficial data analysis. SDP ensures these qualities are obtained from alternative data sets sourced from diverse digitalized sources. Furthermore, our robust quality checks and standardization processes deliver data that are superior in quality, clean, and fit our clients’ formatting requirements.
Our project and technology teams structure each data set to be easy to use and understand, especially since many data sets have diverse formats and are from nontraditional data sources. Finally, these data sets are linked to identifiers, ensuring that the delivered alternative data sets can be analyzed alongside clients’ enterprise data.
Above all, our enriched alternative data lead to informative, actionable insights that our clients can leverage to execute decisions with conviction.
At Straive, we understand the importance of accurate, deep, and insightful information. We integrate financial and industry data, research, and news into tools that help our clients track performance, generate alpha, identify investment ideas, perform valuations, and assess credit risk. Our end-to-end automated data platform SDP is engineered to give enterprises access to the right data intelligence at the right time, to enable powerful commodity trading decisions.
To know more about our data solutions, please visit https://www.straive.com/industry/automating-unstructured-data-in-commodities-trading-lifecycle
Accenture, “Closing the Data-Value Gap: How to Become Data-Driven and Pivot to the New,” 2019, accenture-closing-data-value-gap-fixed
Matt Zborowski, “Permian Pulse: Using Satellite Imagery Analytics to Track the World’s Busiest Oil Play,” Journal of Petroleum Technology, November 30, 2018, https://jpt.spe.org/permian-pulse-using-satellite-imagery-analytics-track-worlds-busiest-oil-play
Bloomberg News, “How to Make Sense of Russia’s Contradictory Oil Data,” Bloomberg, April 29, 2022, https://www.bloomberg.com/news/articles/2022-04-29/how-to-make-sense-of-russia-s-contradictory-oil-data#xj4y7vzkg
McKinsey & Company, “How COVID-19 Has Pushed Companies over the Technology Tipping Point—and Transformed Business Forever,” October 5, 2020, https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever
IEA, “Global Energy Review 2021,” April 2021, https://www.iea.org/reports/global-energy-review-2021
USGS, “Can Landsat Satellite Acquisition Requests Be Made for date and Location?,” n.d., https://www.usgs.gov/faqs/can-landsat-satellite-acquisition-requests-be-made-specific-date-and-location
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
Our solutioning team is eager to know about your challenge and how we can help.