LSEG has integrated its extensive historical tick data with Google Cloud’s BigQuery and Vertex AI, enabling financial institutions to leverage advanced AI tools for faster, more insightful market analysis and enhanced trading strategies.
LSEG has announced a significant enhancement to its cloud-based market data services by enabling customers to access its extensive historical tick data through Google Cloud’s BigQuery and Vertex AI platform. This integration represents a key milestone in LSEG’s broader AI strategy, known as LSEG Everywhere, which aims to deliver trusted, licensed data to financial institutions and empower them to scale artificial intelligence (AI) applications within financial services.
Tim Anderson, Head of Quantitative & Economic Data & Tick History at LSEG, highlighted the importance of this development by stating that the combination of LSEG’s premier market data and Google Cloud’s advanced AI capabilities offers financial market participants cutting-edge data analysis on a scalable and cost-effective platform. The integration allows users to apply AI and machine learning directly on the tick history data hosted in Vertex AI, a fully managed AI development environment by Google, thereby facilitating actionable insights that can enhance trading strategies and risk management.
The collaboration is also praised by Google Cloud, with Graham Drury, Director of Financial Services Industry (FSI) UK at Google Cloud, describing it as a reflection of their commitment to empower financial institutions with advanced AI and machine learning tools. Drury explained that by integrating LSEG’s tick history with Vertex AI and BigQuery, users benefit from faster, cost-efficient, and more insightful market data analysis, marking the beginning of a new era in financial market intelligence.
This latest announcement builds on earlier developments that connected LSEG’s tick history dataset with Google Cloud’s BigQuery, first launched in late 2019. The original integration allowed users to query and analyse vast amounts of historical tick data directly in the cloud, removing the burdens of large data downloads, storage management, and server maintenance. This service provided access to over 45 petabytes of data covering multiple asset classes, with records dating back to 1996, and enabled advanced quantitative research, back-testing of trading strategies, and compliance with regulatory requirements within an efficient cloud environment.
The improved integration now further leverages Google Cloud’s machine learning infrastructure by combining BigQuery data storage capabilities with Vertex AI’s machine learning platform. Industry insights into Google Cloud’s offerings indicate that this synergy allows for seamless data transition between BigQuery and Vertex AI without the need for data movement, significantly lowering the time and cost associated with building and training machine learning models on large financial datasets. Access to over 200 publicly available datasets on BigQuery also enhances the ability of financial institutions to augment their models with diverse and robust data sources.
LSEG’s commitment to customer empowerment extends beyond technology deployment. They provide tailored educational resources such as the “Tick History – Query” learning path, which includes video courses designed to help users understand and exploit the full depth of tick data available on Google Cloud Platform. This initiative facilitates the development of advanced market analysis and trade surveillance capabilities, crucial for making informed financial decisions in today’s data-driven markets.
In summary, LSEG’s latest integration with Google Cloud’s Vertex AI marks a pivotal advancement in the delivery of cloud-based financial market data, marrying a vast historical tick data archive with powerful AI tools. This collaboration not only streamlines data access and analytics but also drives innovation in financial services by enhancing the precision and speed of market insights, helping institutions to remain agile and competitive in an increasingly complex landscape.
- https://mondovisione.com/media-and-resources/news/lseg-allows-integration-to-its-cloud-based-tick-history-data-with-google-clouds-20251120/ – Please view link – unable to able to access data
- https://www.lseg.com/en/media-centre/press-releases/refinitiv/2020/february/availability-of-tick-history-data-on-google-cloud – In February 2020, Refinitiv announced the launch of its Tick History dataset on Google Cloud Platform (GCP), enabling customers to access, query, and analyse Refinitiv’s extensive archive of pricing and trading data using the machine learning capabilities of Google Cloud’s BigQuery. The Tick History dataset offers the full breadth and depth of Refinitiv’s historical tick data drawn from real-time content, covering over-the-counter (OTC) and exchange-traded instruments from more than 500 venues dating back to 1996. This integration allows customers to perform quantitative research, back-test trading strategies, and meet regulatory requirements more efficiently by leveraging GCP’s managed compute clusters and BigQuery’s analytics capabilities.
- https://developers.lseg.com/en/article-catalog/article/big-data-tick-history-google-bigquery – As of November 2019, LSEG enabled users to query and analyse Tick History data directly in the Google Cloud using BigQuery. This integration eliminates the need for large data downloads, storage management, and server maintenance, allowing users to focus on data utilisation. The offering provides direct access to LSEG’s extensive archive of tick data, managed transparently, and supports powerful data queries using industry-standard SQL. This approach simplifies data analysis and reduces costs, enabling users to gain better and faster results without the technical complexities associated with traditional data management.
- https://www.lseg.com/en/data-analytics/market-data/data-feeds/tick-history/tick-history-query – LSEG’s Tick History – Query service enables users to query and analyse the extensive breadth and depth of Tick History data using the full power of Google BigQuery’s compute engine. This cloud-based service allows for faster analysis without the need to download, extract, or transform data. Users can run queries across multiple venues and years of data without data preparation, benefiting from the scalability and efficiency of cloud computing. The service offers access to over 45 petabytes of data covering all asset classes, with data dating back to 1996, facilitating comprehensive market analysis and decision-making.
- https://www.lseg.com/en/training/learning-centre/learning-paths/learning-path-for-data-solutions/learn-about-lseg-tick-history/learn-about-tick-history-query – LSEG offers a learning path to understand Tick History – Query, a service that allows users to access and analyse Tick History data directly in the Google Cloud Platform. The learning path includes a video course titled ‘Tick History in Google Cloud Platform,’ which provides insights into the full depth and breadth of Tick History data available on GCP. The course covers querying Tick History data via Google BigQuery, enabling users to perform advanced market analysis and trade surveillance efficiently. This resource is designed to help users leverage cloud-based Tick History data for enhanced financial analysis and decision-making.
- https://cloud.google.com/blog/products/ai-machine-learning/five-integrations-between-vertex-ai-and-bigquery – Google Cloud’s blog post highlights five integrations between Vertex AI and BigQuery, showcasing how users can seamlessly connect their BigQuery data to Vertex AI without moving data from the cloud. This integration allows for efficient machine learning model training using data stored in BigQuery. Users can upload their BigQuery data directly into Vertex AI, facilitating faster and more cost-effective analysis. The blog post also discusses the accessibility of over 200 publicly available datasets in BigQuery, enabling users to train their own machine learning models with diverse data sources. This integration exemplifies the synergy between Vertex AI and BigQuery in enhancing AI and machine learning capabilities.
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
10
Notes:
The narrative was published on 20 November 2025, indicating high freshness. No earlier versions with differing figures, dates, or quotes were found. The content appears original, with no evidence of being republished across low-quality sites or clickbait networks. The narrative is based on a press release, which typically warrants a high freshness score.
Quotes check
Score:
10
Notes:
The quotes from Tim Anderson and Graham Drury are unique to this narrative, with no earlier matches found online. This suggests the content is potentially original or exclusive.
Source reliability
Score:
7
Notes:
The narrative originates from Mondo Visione, a financial markets intelligence platform. While it is a specialised source, it is not as widely recognised as major news outlets like the Financial Times or Reuters. Therefore, the reliability is moderate.
Plausability check
Score:
9
Notes:
The integration of LSEG’s tick history data with Google Cloud’s Vertex AI platform is plausible and aligns with recent industry trends. Similar collaborations have been reported, such as PwC’s expansion of its AI agent ecosystem with Google Cloud. ([pwc.com](https://www.pwc.com/gx/en/news-room/press-releases/2025/scales-global-ai-agent-ecosystem.html?utm_source=openai)) The language and tone are consistent with corporate communications, and the narrative lacks excessive or off-topic detail.
Overall assessment
Verdict (FAIL, OPEN, PASS): PASS
Confidence (LOW, MEDIUM, HIGH): HIGH
Summary:
The narrative is fresh, original, and plausible, with no significant credibility risks identified. The source’s reliability is moderate, but the content’s authenticity and alignment with industry trends support a high confidence in its accuracy.