NEWS UPDATE: LSEG allows integration to its cloud-based tick history data with Google Cloud’s Vertex AI Platform

LSEG today announced that customers will be able to access its cloud-based historical tick data via Google Cloud’s BigQuery and Vertex AI platform.

This integration enables financial institutions to apply AI and machine learning technology directly to LSEG tick history data available on Vertex AI, providing users with actionable, data-backed intelligence to inform trading and risk management. It is the latest milestone in LSEG’s AI strategy – LSEG Everywhere – which is delivering trusted licensed data to scale AI in financial services. 

Tim Anderson, LSEG’s Head of Quantitative & Economic Data & Tick History, said:

“The integration of LSEG tick history with Vertex AI and BigQuery marks a significant leap forward for financial market participants. By combining LSEG’s deep, first-rate market data with Google Cloud’s advanced AI and data capabilities, customers will have access to cutting-edge data analysis on a scalable, cost-effective platform.”

Graham Drury, Director, FSI UK, Google Cloud said:

“This collaboration with LSEG is a testament to our commitment to empowering financial institutions with cutting-edge AI and machine learning capabilities. By integrating LSEG tick history with Vertex AI and BigQuery, we're enabling faster, more cost-effective, and deeply insightful analysis of critical market data. This will significantly enhance LSEG's service, ushering in a new era of financial market data intelligence.”

Vertex AI is Google Cloud’s fully-managed, unified AI development platform for building and using AI. With Vertex AI, LSEG delivers several key advantages to customers with access to LSEG tick history on Google Cloud: 

• Accelerated processing:Queries that once took hours can now be executed in seconds, enabling faster iteration and more timely market responses.
• Agentic AI adoption: Customers can apply complex reasoning and intelligence to derive new insights with AI agents that are grounded in rich historical data and unified proprietary data.
• Lower operational costs: BigQuery offers efficient data scanning and compute optimization to drive significant cost savings, eliminating the need for costly on-premises AI infrastructure. 
• Greater accessibility: Natural language interfaces and transparent SQL outputs make advanced analytics accessible to both technical and non-technical users. 
• Scalable AI adoption: Financial firms of all sizes can deploy sophisticated AI-driven analysis without requiring large-scale infrastructure or manual data wrangling. 

 

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