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Financial data analytics platform for global information services provider

Client

A leading global provider of news and information services to financial institutions

Industry

Finance

Services

Data architecture, data management, data platform, legacy modernization, BI & data analytics

Tech stack

Microsoft Azure, Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Data Factory, Azure Databricks, dbt, Python, Tableau, Azure Active Directory, Azure Monitor

Challenge

The client was the world's leading provider of news and information services to financial institutions—customers that included the Royal Bank of Canada and US Trust. At the center of their offering was a financial data analytics platform integrating real-time financial news, SEC data, and market forecasting capabilities. Portfolio managers and analysts at major institutions depended on it daily. The platform had a history problem. Built incrementally by different teams over time, it had accumulated incompatible database rules, inconsistent data models, and conflicting technological frameworks across its components. That fragmentation had three concrete consequences:

Data inconsistency undermined analytics' reliability, made accurate market forecasting difficult, and complicated the evaluation of consultant performance and forecast accuracy.

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Data inconsistency undermined analytics' reliability, made accurate market forecasting difficult, and complicated the evaluation of consultant performance and forecast accuracy.

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Support was prohibitively expensive as engineers navigated a patchwork architecture with no single source of truth.

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A scalability ceiling—adding new capabilities meant incorporating additional complexity into a system that was already straining under its own weight

The client needed a partner to audit the full data ecosystem, establish a modern data architecture, and rebuild the analytics platform on a foundation that could actually support growth.

Our approach

ITRex's involvement with this client preceded the platform rebuild. The engagement began with custom analytics development, which involved creating consensus forecast mechanisms tailored to each customer's specific needs. As the partnership progressed, ITRex expanded into development and support for the client's entire user base, increasing the team size from five to dozens of engineers. That accumulated knowledge of the platform's internals made ITRex the natural choice to lead the overhaul. The rebuild followed a structured sequence:

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Full data ecosystem audit. Before any architecture decisions were made, ITRex conducted a comprehensive assessment of every data type, source, flow, and integration within the platform—mapping data silos, governance gaps, and inconsistencies across the fragmented system. Stakeholder workshops surfaced the business rules and analytical workflows that any replacement architecture had to support.

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Data warehouse design & implementation. ITRex designed and built a scalable data warehouse to consolidate client portfolios, market indicators, company financials, SEC data, and external forecasting inputs into a single analytical environment—replacing the patchwork of legacy databases that had made reliable aggregation impossible.

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Data standardization & integration. We introduced unified data models and consistent business rules across all sources, so financial analysts, portfolio managers, and external clients were working from the same definitions when running investment analysis or evaluating market forecasts.

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ETL pipeline development. Extract, transform, and load processes were built to integrate diverse datasets, automate ingestion, and normalize disparate data formats.

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Custom analytics capabilities. ITRex built dedicated analytics storage and developed consensus forecasting mechanisms that let institutions compare forecasts, evaluate analyst prediction accuracy, and surface deeper market trend insights—capabilities the legacy platform couldn't support reliably.

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Data management strategy. A comprehensive framework for data storage, updating, removal, and processing was designed and implemented to meet both business objectives and financial services clients' compliance requirements.

Impact

The platform ITRex rebuilt not only fixed what was broken but also expanded the client's offerings and target audience.

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A 30–40% increase in addressable market. Migrating from a fragmented legacy architecture to a standardized, scalable data warehouse made it possible to move beyond high-touch institutional service and deliver automated, mass-market financial products. That's a structural shift in the business model, not just a performance improvement.

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A 20% increase in annual recurring revenue from the ability to onboard new financial institutions without architectural friction. Every new client no longer required bespoke integration work against an inconsistent data model.

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A 15% uplift in client retention and platform utilization among institutional clients, driven by meaningful improvements in analytics reliability and user experience. When the numbers can be trusted, the platform gets used more.

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A 25% reduction in support overhead. Consolidating disparate components, standardizing data models, and automating ETL processes removed the maintenance burden the platform's fragmented history had built up.

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A 10% improvement in forecasting precision through real-time integration of SEC and market data feeds, giving analysts and portfolio managers inputs that were current rather than lagged.