Workflow
净零排放转型
icon
Search documents
数据驱动型智能是应对变化的关键
Refinitiv路孚特· 2025-07-15 02:25
Core Viewpoint - The current geopolitical tensions, extreme weather conditions, and fluctuating climate policies are reshaping the global market landscape, creating both challenges and opportunities for companies to reassess their risk and investment strategies [2][5]. Group 1: Market Dynamics - Commodity markets operate interdependently, where energy prices fluctuate due to regulatory changes, extreme weather impacts supply and demand, and geopolitical instability disrupts supply chains [3]. - A comprehensive analysis that connects various data sets and market interdependencies is crucial for informed decision-making, as isolated data can lead to misleading conclusions [3][5]. Group 2: LSEG's Strategic Approach - LSEG has developed a global intelligence platform that integrates high-frequency data, satellite imagery, and machine learning algorithms, providing insights across approximately 190 commodity markets, including energy, metals, and agriculture [3][4]. - The platform enhances predictive models and anomaly detection systems, offering precise risk assessments and long-term market trend insights, such as hourly electricity market forecasts extending to 2035 [3][6]. Group 3: Decision-Making in Volatile Markets - In the face of extreme market volatility, companies must act swiftly and decisively, utilizing LSEG's analytical tools to adjust trading strategies, optimize investment portfolios, and manage risks effectively [6]. - LSEG's cross-commodity correlation models help traders understand deeper market interdependencies, leading to more accurate price predictions and risk evaluations [6]. Group 4: Competitive Advantage through Data - LSEG Data & Analytics has been recognized as the "Data and Analytics Company of the Year 2025" by Energy Risk magazine, highlighting the importance of data-driven intelligence in successful decision-making within the energy sector [7]. - The company continues to expand proprietary data sets, refine predictive models, and enhance analytical capabilities to ensure clients maintain a competitive edge amid the complexities of energy transition [7].