LSEG has partnered with Databricks to offer its data directly in Databricks via Delta Sharing, beginning with Lipper fund data and analytics as well as cross-asset analytics.

Additional data such as pricing, reference data, models, fundamentals, estimates, economics, and future tick history will be made available, expanding the range of AI-ready data. This collaboration allows customers to build and deploy AI agents on their enterprise data and LSEG’s data more efficiently for real-time investment analytics, risk management, and trading workflows using Databricks Agent Bricks.

Unified data for financial intelligence

Financial firms often struggle with outdated and manual data delivery methods that are costly and slow. Analysts spend significant time integrating data rather than building models necessary to respond to rapidly changing market conditions. This partnership aims to address these challenges by helping enterprises unify financial data, which can power analytics, AI applications, and agents. The solution supports portfolio management, risk assessment, forecasting, and client reporting, assisting teams in making informed decisions faster and staying competitive.

Applications of the alliance include investment analytics, trade analysis, and risk management. Both companies aim to enhance market intelligence, back testing, and portfolio optimization with AI-driven strategies that provide real-time market insights, trade cost analysis (TCA), predictive forecasting, and algorithmic trading for improved intraday decision-making. Additionally, it integrates and strengthens market, credit, and counterparty risk oversight by enabling AI-driven monitoring, exposure tracking, and real-time compliance across front-to-back offices.

With Databricks Agent Bricks, teams can integrate raw tick history or reference data with their enterprise data to launch production agents that offer built-in accuracy, governance, and cost efficiency. Global banks can use these tools to combine transaction and client data with market and reference data, run scenario forecasts, identify investment opportunities and portfolio risks, detect unusual trading patterns, and automatically generate compliance reports in real time.