The London Stock Exchange Group (LSEG) and US data platform Databricks have announced a partnership to make LSEG’s financial datasets available directly within Databricks.
The collaboration will see datasets such as Lipper Fund Data & Analytics and Cross Asset Analytics delivered natively through Databricks’ Delta Sharing platform, with pricing, reference data, economic models, and tick history set to follow.
The aim is to give financial institutions a streamlined way to integrate trusted data into their analytics, trading and risk management systems.
Databricks customers will also gain access to “Agent Bricks”, a framework for building AI agents on enterprise and market data.
These agents can perform tasks such as identifying portfolio risks, running scenario forecasts, generating compliance reports and detecting anomalous trading activity in real time.
“Customers tell us they have an insatiable appetite for high-quality, AI-ready data to accelerate their analytics and AI workloads,” said Stephen Orban, senior vice-president of product ecosystem and partnerships at Databricks.
“Together, LSEG and Databricks can now empower financial institutions to quickly build AI agents that use LSEG’s data to automate tasks, analyse trends and provide real-time, actionable insights.”
Emily Prince, group head of analytics and AI at LSEG, said: “By adding our industry-leading datasets to Databricks Marketplace, we are empowering financial institutions to unlock new levels of intelligence, efficiency, and compliance.”
The companies argue that many financial institutions still rely on slow, batch-based data delivery, leaving analysts struggling to keep pace with market shifts.
By making live datasets discoverable on Databricks Marketplace, they say, institutions will be able to speed up decision-making across portfolio management, forecasting, client reporting and compliance.
Among the use cases highlighted are investment analytics, such as backtesting and portfolio optimisation; trade analytics, including transaction cost analysis and predictive forecasting; and risk management, with AI-driven surveillance and exposure monitoring across front-to-back office operations.
