Energy Risk Awards 2026: LSEG D&A stands out for its ability to harmonise widely differing Datasets to support Enterprise Risk 

Geopolitics, supply constraints and shifting policy are upending energy markets, causing changes to trade flows, supply chains and market correlations. This is driving a greater need for enterprise risk management in order to understand linkages between markets and monitor company-wide exposures. 

A huge impediment to this, however, is having the breadth of data, fully harmonised across different asset classes to allow meaningful analysis. This is where LSEG D&A, winner - for the second year running - of Energy Risk’s 2026 Data and analytics firm of the year award, stands out. Its comprehensive data offering spans global commodity markets and includes asset level fundamentals, physical flows, pricing, weather impacts and policy signals from intraday to long term horizons. Crucially, it harmonises these highly heterogeneous datasets to enable a true enterprise risk focus. 

“What differentiates our offering is how these datasets are harmonised across commodities and time horizons, allowing correlations between physical, market and geopolitical risks to be analysed consistently,” says Stuart Wood, head of commodities and corporates at LSEG D&A. “This enables risk teams to move beyond siloed monitoring and assess interconnected risks across the energy system.” It also supports the assessment of correlated risks across regions and portfolios, he adds.

The enormous effort to collect and harmonise data across power, gas, metals, carbon, oil, shipping, hydrogen, coal and agriculture markets is carried out by an army of specialised experts. LSEG D&A has over 200 researchers, analysts, data scientists and meteorologists focused on commodities every day, says Wood.

LSEG D&A’s data is generated and collected through a combination of proprietary research, automated ingestion of high frequency operational data and specialist third party sources. As well as weather and climate data, it includes grid and transmission system information, automatic identification system vessel tracking, trade flows, market prices and policy signals, all supported by automated quality controls and expert validation.

Data and analytics are delivered primarily through LSEG Workspace, providing a single environment for cross market risk analysis. It provides interactive dashboards, scenario tools, correlation views, in depth policy analysis, and APIs to integrate analytics into internal risk systems.

“We work closely with clients’ trading and risk teams to align analytics with enterprise risk frameworks,” says Wood. “This ensures outputs are transparent, explainable and usable for portfolio level risk assessment rather than isolated market views.”

LSEG Workspace supports both real time monitoring and forward looking stress testing, combining physical fundamentals, pricing and external risk drivers. “LSEG also provides structured publications and rapid ad hoc analytics during periods of market stress, enabling users, analysts and risk managers to assess impacts across portfolios, regions and commodities as conditions evolve,” says Wood. “[This helps] clients maintain a consistent view of risk as market conditions change.”

LSEG D&A has made some enhancements over the last 12 months that have proved particularly timely, given the recent disruption to energy flows caused by the Middle East conflict. It has expanded its liquefied natural gas supply and demand coverage at plant and country level and introduced new shipping data sources supporting near real time monitoring of vessel movements, congestion and deviations, allowing clients to assess evolving supply risks alongside logistics constraints.

In addition, the company has enhanced its analytics offering to better support integrated risk management by clients. “Key upgrades included 15 minute power modelling and flow based coupling, to better integrate cross border electricity flows, as well as AI enhanced weather integration across power and gas demand,” says Wood. LSEG D&A has also improved the platform’s carbon analytics with new proprietary marginal abatement cost curves data, enhancing its long term scenario modelling capabilities.

It is also increasingly using AI to automate data ingestion, benchmark forecasts, detect anomalies, and surface early warning signals. “This allows our risk teams to focus on interpretation, scenario design and decision making, rather than manual data processing,” says Wood.