Thomson Reuters: scaling Data Products with model-driven automation

Thomson Reuters is a global leader in professional information services, delivering deep expertise across legal, tax, compliance, and business intelligence. Serving customers in 100+ countries, they manage a complex data landscape across publishing, SaaS, and compliance platforms. As Thomson Reuters repositions for an API-first, AI-ready future, model-driven data product development has become the cornerstone of their transformation.

Their commitment to providing trusted, high-quality information drives their continuous innovation in data management and delivery.

The challenge: building scalable, trusted data products for an API-first, AI-ready future

Thomson Reuters faced the critical challenge of building scalable, trusted data products while transitioning from legacy tools like SAP and Informatica. Decades of growth and acquisitions led to a complex, fragmented data environment, characterized by multiple overlapping commercial platforms and an ever-increasing number of data sources. Key challenges included significant schema drift, growing maintenance burdens, the complexities of integrating with S/4HANA, and a substantial amount of technical debt from manual ETL processes. These issues created data silos and hindered the ability to deliver timely, consistent, and high-quality data insights.

"Unifying our data wasn't just about modernization — it was about unlocking value through reusable data products across teams and platforms."

— Anup, VP, Data & Platform Engineering, Thomson Reuters

The solution: Model-driven automation with VaultSpeed

To overcome these challenges and accelerate their data capabilities, Thomson Reuters sought a solution to automate data product development, ensure quality, and provide a unified business view. After evaluating various tools, they chose VaultSpeed as their ideal partner to implement a robust, agile data product development solution. The decision was driven by VaultSpeed's model-driven, product-ready, and AI-enabled approach, offering strategic advantages such as a model-first philosophy, data products by design, AI enablement, delta-based processing for efficiency, and composable delivery to meet diverse business needs.

"VaultSpeed enables our model-driven delivery of data products, which is exactly the mindset we need to scale and be AI-ready."

— RK, Senior Director of Engineering, Thomson Reuters

Building the Data Product Platform: a hybrid approach for enterprise scale

The journey to a modern data product platform began with a clear vision: a single source of truth for all Thomson Reuters' data, enabling rapid development of API-ready, AI-enabled data products. VaultSpeed's automation was central to this. They adopted a hybrid approach, combining a centralized Data Vault with modular data products tailored to specific business needs. The tech stack includes VaultSpeed for automation, Snowflake as the data warehouse, dbt & dbt Cloud for data transformation, Fivetran for data ingestion, Airflow for orchestration, and Lucidchart for data modeling visualization.

Key milestones included onboarding over 20 critical systems, establishing weekly pipeline updates, and leveraging VaultSpeed's delta logic to significantly reduce data processing costs. A robust Git-based CI/CD pipeline was established early, ensuring seamless data model changes, integrations, and deployments with version control and automated testing. This foundation facilitated rapid onboarding of new data sources, standardizing disparate schemas and allowing the team to focus on high-value data product development rather than manual ETL.

Key Metrics: results with VaultSpeed

The quantitative impact of VaultSpeed's implementation was immediately evident. Thomson Reuters achieved a 3-5x acceleration in pipeline delivery, enabling faster time-to-market for new data products and insights. Concurrently, the data engineering team saw a 50-70% reduction in their workload, freeing them to focus on advanced analytics and strategic initiatives. This also accelerated the decommissioning of legacy platforms, fostered better collaboration across teams, ensured strong data governance, and provided trusted inputs for their AI initiatives.

Business impact: accelerated insights & strategic growth across the enterprise

VaultSpeed brought tangible and quantifiable improvements to Thomson Reuters' data operations. By unifying disparate data sources into a standardized, model-driven architecture, they achieved a single, consistent view of business performance, critical for key data products like Enterprise Customer Data, European Analytics Warehouse, Financial Products, API-Ready Models, and Commercial & Tax Reporting. This newfound data integrity fostered greater trust across departments, enabling collaborative decision-making and empowering teams with reliable, up-to-date data.

"VaultSpeed helps us think in terms of product delivery, not pipelines. That shift unlocks huge strategic value."

— Laurent, Head of Data Engineering, Thomson Reuters

Ultimately, data transformed from a blocker into a catalyst for strategic growth and innovation. The data team now drives initiatives directly impacting Thomson Reuters' bottom line and global expansion efforts, providing critical information for their API-first and AI-ready future.

"VaultSpeed has been a game-changer for Thomson Reuters. We're not just preparing for AI — we're enabling it by making our data clean, modeled, and ready to go. It's our foundation for future growth."

— Anup, VP, Data & Platform Engineering, Thomson Reuters

The Road Ahead: Continuing the Data Journey

Thomson Reuters' partnership with VaultSpeed has not only solved immediate data challenges but has also laid a robust foundation for future innovation. With a unified, reliable, and agile data product platform, Thomson Reuters is better equipped than ever to leverage its vast data assets, drive strategic growth, and continue its legacy of providing trusted information in an API-first, AI-ready world.