Building a holistic data view is a differentiator in a market defined by volatility and thin margins

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BUILDING A HOLISTIC DATA VIEW IS A DIFFERENTIATOR IN A MARKET DEFINED

BY VOLATILITY AND THIN MARGINS

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WHY DO DATA PLATFORMS BECOME A BACKBONE OF MODERN COMMODITY BUSINESS? Businesses that are exposed to volatility of commodity prices increasingly require a holistic view of their core data related to positions, risk, margins, budgets, and planning. Experience shows that a unified data view not only improves decision quality and speed but also has a measurable impact on financial & operational performance. According to RadarRadar’s analysis, an effective data platform can deliver margin improvements of up to 2% in certain use cases.

KPIs and reports are consistent and trustworthy only when they are generated from the same enterprisewide dataset that serves as a single source of truth.

This approach allows organizations to generate actionable insights into margins, cash flows, regulatory exposure, and working capital across all commodities, both historically as well as forward looking.

Recent political and technological developments further reinforce the need for strong data management.

In today’s geopolitical environment, marked by rapidly changing tariffs, trade restrictions, and sanctions, it is critical to keep data current, consistent, and easily accessible. At the same time, the adoption of AI-based decision-support tools increases the demand for highquality, well-governed, and readily available data.

Without a unified data view, companies risk making suboptimal decisions, facing inconsistencies across reporting levels, and spending excessive time on data reconciliation and issue resolution. Poorly managed commodity data can lead to tens or even hundreds of millions in losses through reduced margins, poorly timed trades, higher financing costs, and operational

inefficiencies - risks that are further amplified by trade disruptions and geopolitical uncertainty.

When internal data models lack alignment, the analytical calculations built on top of them also become inconsistent. This results in significant discrepancies in key KPIs across reports, creating a material risk for the organization as a whole.

Companies are increasingly recognizing these challenges and are actively working towards data consolidation and synchronization. In several recent roundtables (see for example, How important is good data management practice to successfully implementing and utilizing a CTRM? - CTRM Center or The Role of Data Management in Solution Ecosystems - CTRMRadio 62 - CTRM Center) leading industry experts emphasized that disciplined data management is the cornerstone of successful CTRM system implementations - supporting everything from governance and change management to day-to-day data quality and availability.

Even when companies recognize the need for data consolidation, they may pursue different approaches

Building a holistic data view is a differentiator in a market defined by volatility and thin margins

to achieve it. Many organizations view data lakes as a solution for consolidating data. However, data lakes alone do not address the need for a strong and reliable

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data foundation for operational and strategic decisionmaking. The following section outlines the differences between data lakes and holistic data views.

HOLISTIC VIEW ON THE DATA VS. DATA LAKE A data lake is a centralized repository designed to store large volumes of raw data. It can contain structured, semi-structured, and unstructured data, with minimal transformation during ingestion. While this data can later be used for AI or other business needs, it often remains inconsistent, poorly governed, or defined in different ways. Business logic, validation rules, and clear ownership are frequently missing, and multiple versions of the same metric may coexist.

A data lake can store all the data, but it does not automatically provide a unified or trusted view of that data.

Even when a data lake is successfully implemented, the underlying data may still differ in structure, definitions, ownership, and process maturity. Standard validation rules are often absent. Data representing the same concept - for example, a financial position - may be handled differently across systems: using different naming conventions, different data structures (with or without history), different calculation assumptions (such as exchange rates or order dates), different currencies and time zones, and inconsistent archiving policies.

A holistic data view, by contrast, represents a businesslevel state in which data is consolidated across all

relevant systems. It is governed with clear ownership and accountability, normalized and validated using consistent rules, and enriched with aligned business logic and assumptions. The data is accessible to all relevant stakeholders in an appropriate and consistent form.

A unified, holistic data view ensures that: • Positions, risk, margins, and financials are reconciled across teams and regions • Reports and KPIs tell the same story across the organization • Decisions are based on consistent and trusted information

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Building a holistic data view is a differentiator in a market defined by volatility and thin margins

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CHALLENGES OF BUILDING DATA MANAGEMENT SOLUTIONS For a simple commodity business operating a single, monolithic CTRM system that covers all front-to-back trading activities and integrates with an ERP system, building a holistic view of the data is relatively straightforward. The challenge increases as complexity grows along two dimensions: the complexity of the IT landscape and the complexity of business processes.

The IT landscape becomes more complex when companies trade multiple commodities whose specific requirements cannot be fully supported by a single CTRM system, or when they adopt a best-of-breed approach to selecting tools for trading, back office, operations, and other related functions. In such cases, companies rely on ecosystems of interconnected solutions rather than a single platform. These ecosystems often include ERP systems, logistics solutions, multiple CTRM platforms,

extensive spreadsheet-based analytics, and reporting tools such as Power BI. Across the organization, different departments use data from these systems to calculate trading and risk KPIs, prepare budgets, plan resources, perform margining, and produce management and regulatory reports. As a result, the required data is often fragmented, stored in different formats, and spread across multiple locations.

Building a holistic data view is a differentiator in a market defined by volatility and thin margins

The need for comprehensive data management is further strengthened by the industry’s shift toward so-called Open CTRM solutions as an alternative to monolithic platforms. The Open CTRM concept is based on a modern, modular deal-management core that integrates seamlessly with a range of standard and custom tools for functions such as risk management, logistics, and operations. In such environments, robust data consolidation becomes both critical and more complex.

The second dimension – complexity of business processes – relates to the usage of the data. Building a basic position report is not that complex, but it gets more complex when it is required to cover multiple markets and corresponding regulatory requirements, report for different locations, calculate financials for complex cost types, perform complex stress testing and scenario analysis .

Another process that can become highly complex is margin calculation. Margining encompasses multiple

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cost components, including bills of materials, logistics costs, costs of sales, and other operational and financial charges. Some organizations face particularly high complexity in their margining processes due to heterogeneous portfolios and contract structures, multiple fees and penalties associated with physical delivery, financial margins driven by counterparty credit risk and collateral management, as well as diverse regulatory and tax regimes. Each of the processes related to commodity trade life cycle impacts the overall sales margin, which is calculated based on the data sourced from multiple systems, making consolidation, validation, and synchronization a significant challenge.

Companies facing complex business processes, a complex IT landscape, or both, must address the challenging task of building data platforms that support consistent reporting at both departmental and corporate levels. In doing so, they typically consider two main approaches: purchasing a vendor solution or building a platform in-house.

BUY VS. BUILD Many companies believe they can address data synchronization challenges using internal resources and existing tools such as CTRM systems, ERP platforms, and data lakes. Many IT leaders view this as the most cost-effective approach to data management.

When an IT landscape consists of a single CTRM system and one ERP platform, creating a data lake and layering a BI tool on top can be a viable solution. In the simplest cases, even a BI tool connected directly to a

single CTRM may be sufficient.

In practice, some companies seeking to simplify their IT landscape attempt to consolidate transaction

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Building a holistic data view is a differentiator in a market defined by volatility and thin margins

management into a single CTRM system. However, this approach often runs into fundamental challenges stemming from the nature of commodity trading itself.

Companies active in multiple physical commodities must account for highly specific characteristics (e.g. different crude oil grades) that influence pricing, operational processes, delivery, and, ultimately, the entire trade life cycle.

Similarly, physical storage and delivery introduce commodity-specific requirements. These include, for instance, boil-off rates in LNG transportation or the oxidative degradation of coal during storage. CTRM systems that are sufficiently flexible to accommodate the full range of such commodity-specific features are rare; when they do exist, they are typically expensive and require long implementation times.

As a result, most multi-commodity traders either adopt commodity-specific CTRM solutions or accept functional compromises, supplementing their core CTRM with spreadsheets and ancillary applications to compensate for missing capabilities.

In an environment, where multiple CTRM systems and additional supporting applications are used, the task of data consolidation and synchronization becomes complex. Despite this complexity, companies often still consider building an in-house solution, typically based on a data lake combined with BI tools drawing data from multiple CTRMs and ERPs. In practice, such solutions often prove insufficient and show clear limitations when compared to specialized

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vendor platforms. The most common issues include: • Time to value Internal IT teams must work closely with multiple departments and consolidate input from subjectmatter experts who are already heavily involved in daily operations. They are responsible for defining data ownership and workflows, aligning valuation logic for KPI calculations, and establishing rules for data exchange, connectivity, and normalization.

In practice, some departments may not be able to provide sufficient engagement, which can result in only partial data integration. Therefore, internal initiatives often take significantly longer than planned and still deliver suboptimal results.

By contrast, software vendor teams bring experience gained across many implementations, allowing them to recommend and apply proven approaches more efficiently. Based on RadarRadar’s experience, projects supported by a vendor take typically 3 to 12 months and require a couple of hours of a subject matter expert. Whereas projects implemented entirely in-house generally take well beyond 12 months to deliver with close to full-time involvement of subject matter experts.

• Model and quality risk Reusing existing business logic embedded in spreadsheets or bespoke, opaque models often leads to inconsistencies, a lack of standardized calibration, and suboptimal results. Inaccurate or misaligned models can produce incorrect calculations for positions, exposures, and margins,

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Building a holistic data view is a differentiator in a market defined by volatility and thin margins

creating significant operational and financial risk.

As a result, trading, risk, finance, and controlling teams frequently rely on different position reports based on inconsistent assumptions, leading to misaligned insights and suboptimal decisions.

Established vendors, by contrast, provide a broad library of tools and calculation modules that can be combined into customer-specific workflows and reports. While these workflows can be tailored to individual needs, the underlying components are thoroughly tested and validated across multiple clients. This significantly reduces model risk and increases confidence in the results.

• Build and delivery risk What happens if internal IT teams fail to deliver the solution on time, within budget, without compromising functionality? This can occur for several reasons, including limited availability of subject-matter experts, insufficient understanding of business processes, and a lack of experience managing complex, multi-department and multilocation projects. In such cases, full responsibility for any miscalculations remains with the company and can directly impact financial results. When functionality is compromised for the sake of keeping the budget, the resulting solution may have structural limitations that ultimately increase the risk of suboptimal decisions.

Vendor solutions, by contrast, are typically selected based on clearly defined criteria, scope, and

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commercial terms. If a vendor fails to deliver the agreed scope on time, responsibility and financial consequences can, at least in part, be shared with the vendor.

In addition, market-tested products are inherently more reliable, as they are validated by both vendor teams and customers. Prepackaged, standardized solutions combined with experienced delivery teams significantly reduce implementation and delivery risk.

• Total cost of ownership The true cost of developing in-house solutions is often underestimated. Even in organizations with capable and motivated IT teams, launching such initiatives requires significant effort, including identifying data issues, designing the architecture, defining governance, and allocating resources steps that are easy to underestimate or miscalculate.

In addition, the ongoing maintenance of internally built systems requires substantial effort and is frequently overlooked in initial cost estimates.

With software vendors, the total cost of ownership defined in the contract typically includes maintenance and support services. In addition, implementation cost estimates are typically more accurate for vendors that do not need to design the product from scratch, but only to configure it for the customer’s needs and have experience across multiple implementations. As a result, the risk of cost miscalculation is significantly lower.

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Building a holistic data view is a differentiator in a market defined by volatility and thin margins

At the same time, technology, (cyber)security requirements, and compliance standards evolve rapidly, and keeping pace can become a significant burden for organizations for which IT is not a core capability. For software vendors, staying current with technological and regulatory changes is a contractual obligation across their customer base.

As a result, the overall cost–benefit balance generally favors vendor solutions, for which the development and maintenance of their systems are core business activities.

• Future flexibility Even when an in-house solution is built in the best possible way, it is usually bespoke and closely aligned with the company’s current needs. Creating a flexible solution that can be easily reconfigured in response to market or business changes requires significantly more effort and is therefore much more expensive. As a result, internal IT teams typically deliver solutions that meet today’s requirements without accounting for any future changes.

Software vendors, by contrast, develop products designed to operate across different business environments. For this reason, vendor solutions are inherently more flexible and easier to adapt to changes as business needs evolve.

Vendors are also better positioned to track technological advances, introduce new capabilities, and manage smooth upgrades between software

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versions. Their structured release cycles and dedicated support teams provide long-term stability and ongoing maintenance that commodity trading companies often find difficult to achieve internally.

• Completeness of the product As noted above, in-house solutions are typically built to meet a company’s current requirements. They usually cover only the specific use cases defined at the time of development. Any new or slightly different use case arising from market changes or regulatory updates typically requires additional development effort.

Standard vendor products, which are used by many customers, are designed to support a wide range of industry-relevant use cases. Their functionality is delivered in a robust, configurable, and mature form, which is difficult to achieve with an in-house solution.

Vendor solutions also typically offer standardized APIs, enabling smooth integration and interoperability with common enterprise systems.

Finally, vendors take responsibility for cybersecurity and compliance. They apply modern methods to detect and manage vulnerabilities and employ dedicated cybersecurity specialists, who are often difficult to source in the market. For example, according to NIST UK, only 20% of utilities report confidence in their cybersecurity workforce.

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Building a holistic data view is a differentiator in a market defined by volatility and thin margins

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RADARRADAR’S OFFERING RadarRadar offers a comprehensive software suite that leverages existing data sources and flexible ways of data ingestion to enable data-driven decision-making for positions, margin, and risk management.

The Radar Engine helps customers transform enterprise-wide raw data into trusted, actionable information for commodity risk management. Whether the source is an E/CTRM, ERP, finance system, Excel sheet, or simple file, the Radar Engine can ingest and integrate all necessary data regardless of its location, origin, or volume, providing a single source of truth across the entire organization.

With master data and governance support, realtime reporting, and comprehensive consistency and completeness checks, customers gain full control over their data. Cloud services, advanced security tools, and user-permission features ensure the secure storage and use of business data. The data workflow includes the following steps: • Mapping: converting data from disparate source formats into a unified destination format • Harmonizing: standardizing qualitative and quantitative fields into a single composite dataset • Cleansing: identifying and resolving errors, null values, and duplicates, then aggregating and enriching data • Automating: streamlining the uploading, processing, and transformation of data • Distributing: replicating and distributing data across multiple databases • Maintaining: storing, auditing, organizing, and correcting data to ensure accuracy and accessibility

By converting raw data into reliable information, the RadarRadar Engine enables customers to address business challenges from a data-driven perspective.

This supports advanced analytics, enhances reporting, and improves transparency for interdisciplinary collaboration.

These business functions are supported by RadarRadar’s analytical modules, built on top of the core data management framework: • Position and Performance Module provides a holistic view of positions, market Prices, portfolio exposures and financial value to support decisionmaking in complex market environments.

• Risk Management Module offers both quantitative and qualitative insights into portfolio risk profiles, based on internal models and external market developments. It helps identify key risk drivers, track exposure, predict performance volatility, run scenario analyses, visualize and test option books under different conditions, assess counterparty risks, and configure alerts and notifications.

• Dynamic Margin Management Module combines portfolio and market insights to help customers understand, manage, and optimize margins. Users can analyze production planning, forward and historical margins, yield, blend and

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Building a holistic data view is a differentiator in a market defined by volatility and thin margins

bill-of-material calculations, create and evaluate scenarios, monitor purchases and sales, and optimize procurement and asset strategies.

• Regulatory Compliance Module helps customers manage regulatory risks across their portfolios and locations while staying ahead of compliance deadlines. The module provides customizable, on-demand tools covering global derivatives regulations. It supports transaction and position reporting, visualization of current

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and historical submissions, threshold monitoring, response management, and rapid adaptation to regulatory changes, including monitoring for market abuse.

Together, these modular tools transform consolidated, trusted data into powerful applications for commodity risk management, margin optimization, and regulatory compliance.

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Building a holistic data view is a differentiator in a market defined by volatility and thin margins

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CONCLUSION In a market defined by volatility and thin margins the ability to act on consistent, trusted data is becoming a competitive differentiator Commodity traders fully recognize this and are actively working towards data consolidation and synchronization. They have basically two options: build a data solution based on their existing software tools or buy off-the-shelf product. Compared with a build option, purchasing and implementing standard software enables faster deployment, access to best practices, lower total costs, standardized interfaces, and continuous technological updates through regular release cycles.

These advantages of standard solutions are increasingly significant in the current market conditions where competitiveness depends on speed and operational efficiency, trading volumes are high, margins are thin, and risks are substantial.

RadarRadar provides a holistic data view based on a data model that is considered the industry standard across all commodities and locations, along with extensive analytical tools for calculating physical and financial KPIs. Furthermore, it delivers comprehensive support for margin optimization and regulatory compliance.

Companies that rely on market-proven solutions such as RadarRadar’s solution can benefit from the vendor’s experience accumulated across numerous projects with global leaders, accelerate their modernization efforts, and strengthen their competitive position in the industry.

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ABOUT RadarRadar RadarRadar is your commodity control platform built by commodity experts and trusted by global leaders.

It ingests and harmonizes data from ERP, CTRM, spreadsheets, and other systems into a single source of truth—empowering your organization to optimize results across markets. With integrated Position, Risk, and Margin (P&L) Business Applications, RadarRadar helps drive profitability, strengthen risk management, and enhance control in volatile conditions. Our clients

achieve measurable improvements, including a 1% increase in gross margin, and gain the confidence that comes from a complete understanding of their positions, exposures, and performance https://radarradar.com/demo

ABOUT Commodity Technology Advisory LLC Commodity Technology Advisory (ComTech Advisory) is the leading analyst organization covering the Energy and Commodity Trading and Risk Management (E/CTRM) and Energy Transition technology markets. Led by Dr. Gary M. Vasey, along with affiliate analysts Dr. Irina Reitgruber and Kevin Mossop, ComTech Advisory provides invaluable insights, backed by primary research and decades of experience, into the issues and trends affecting both the users and providers of the applications and services that are crucial for success in markets constantly roiled by globalization, regulation and innovation.

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