Crown Agents Bank
Regaining control over data foundations, replacing fragmented reporting with a governed, automated analytics platform.
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Regaining control over data foundations, replacing fragmented reporting with a governed, automated analytics platform.
Challenges
How we helped
Success factors
When a new Head of Data joined Crown Agents Bank organisation, one issue quickly became apparent: the data team had little visibility into the organisation’s underlying data. Although the business relied heavily on reports and dashboards, the raw data itself was difficult to access or review by the data function on a frequent basis.
At the time, the reporting process was fragmented. Core data lived in an Oracle database managed by IT, while reports were manually extracted into Excel before being visualised in Tableau. This approach created distance between the data source and the insights derived from it, making it difficult to ensure accuracy, consistency, or trust.
Crown Agents Bank is a UK-regulated wholesale bank specialising in foreign exchange, payments, and liquidity solutions for banks, governments, and international institutions operating in frontier and emerging markets. With a strong focus on financial inclusion and global development, the bank plays a critical role in enabling secure cross-border transactions where traditional banking infrastructure is limited.
Crown Agents Bank is a UK-regulated wholesale bank specialising in foreign exchange, payments, and liquidity solutions for banks, governments, and international institutions operating in frontier and emerging markets. With a strong focus on financial inclusion and global development, the bank plays a critical role in enabling secure cross-border transactions where traditional banking infrastructure is limited.
The Challenge: Existing setup was holding the organisation back
The separation between IT-owned data infrastructure and business-owned reporting created structural problems. The IT team focused on keeping systems running, not on guaranteeing reporting consistency or data definitions. Meanwhile, the data team worked with curated outputs only, without visibility into the raw data or transformation logic behind the numbers.
Manual Excel-based transformations introduced further risk. Different versions of the same report circulated across teams, discrepancies were hard to trace, and Tableau dashboards lacked a single, governed source of truth. As a result, decision-making slowed and confidence in reporting eroded.
The Solution: Establishing governance before transformation
Rather than jumping straight into complex data modelling, the first priority was to restore control and trust in the data. The engagement focused on understanding the data at its source and establishing clear ownership.
The team began with a comprehensive profiling of the raw data flowing from the core banking system. This assessment revealed inconsistencies, gaps, and discrepancies that had previously gone unnoticed. From there, responsibility for reporting definitions and data governance shifted from IT to the data team, creating clear accountability for accuracy and alignment with business needs.
At the same time, manual Excel transformations were phased out. Data pipelines were rebuilt using Snowflake as the central data platform, with Fivetran automating ingestion directly from the source systems. Tableau was then connected to governed, structured datasets within Snowflake instead of spreadsheet outputs.
The impact: Accurate data, faster decisions
The results were immediate and tangible. Centralising data in Snowflake made it possible to identify and correct reporting discrepancies that affected hundreds of thousands of records. Leadership gained access to a validated executive dashboard in Tableau, featuring 40–50 trusted KPIs that could finally be used with confidence.
By implementing a standardised data architecture based on Kimball methodology and Snowflake best practices, the organisation laid a scalable foundation for future analytics. Automated ingestion replaced manual effort, reducing operational overhead and significantly accelerating reporting cycles.
What started as a governance challenge ultimately became a transformation of how data was owned, trusted, and used across the organisation—turning reporting from a source of doubt into a reliable driver of decision-making.
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