Data Modelling – meeting regulatory deadlines with a data warehouse and data model 


Banks and other financial institutions have faced a tidal wave of financial regulation, particularly in the years since the GFC (great financial crash) in 2008. Meeting these requirements can be challenging because they require a mature level of data governance, a professional data platform and streamlined processes around ETL (extract, transform and load).

Our client, a large financial institution, was struggling to comply with burgeoning regulatory requirements. The bank had to swiftly find a way to improve the control of value streams within the bank, manage its financial risk, develop a more efficient reporting process and provide better insights in data lineage. The bank concluded it needed a data warehouse to spearhead a new data model way of working.

The aim was to deliver all regulatory reports, for both finance and risk departments of the bank, using data extracted from the data warehouse. Migrating the whole organisation onto a centralised data warehouse would result in better data lineage, more mature data governance and a higher consistency in the reported data.

However, a major problem was that the data warehouse had not been adopted on an enterprise-level scale and the bank needed guidance to achieve this. A further problem was an over reliance on too many independent contractors, making consistency and knowledge retention difficult. Another issue was the use of outdated data tools was leading to an inefficient development cycle – migration to a cloud solution was still pending.


Valcon was hired as a long-term partner to both guide and implement a future proof solution to create consistency and encourage enterprise-wide adoption of the data warehouse and data model way of working.

Valcon is establishing a cohesive strategy for the integration and deployment the finance and risk data model, comprising two key components. The first is developing a well-defined process for crafting the logical data model and transforming it into the physical data model. The second is addressing the technical implementation, ensuring that the tools employed not only align with the established methodology but also enhance the development cycle. Valcon conducted a comprehensive analysis of the current infrastructure, identifying areas for enhancement and where we can drive positive outcomes.

Valcon has landed a data team who are implementing the vision and facilitating the uptake of the enhanced data model and data warehouse across the organisation. We are also helping the bank transition from ‘on-premise’ infrastructure to a cloud based solution, which opens up avenues for upgrading the technical stack and using advanced data tools, like Databricks.


This programme is a work in progress, but the benefits are already becoming apparent. They include:

  • Regulatory adherence: the data warehouse, data model and a new structured approach to data has enabled the bank to comply with regulatory standards.
  • Single source of the truth: the data model and data warehouse are where all the bank’s data sources are integrated, which provides a set of agreements between all users of the data, ensuring clearer scope, better documentation and a single source of truth.
  • Enhanced scalability: the new data warehouse can scale to provide an enterprise-wide data model, ensuring regulatory adherence and data standardisation across the whole organisation.
  • Improved performance: a modernised tool stack is being introduced, which has improved performance and led to faster access to data and efficient analytics.
  • Streamlined development cycle: by evaluating the development cycle in various teams, Valcon introduced several centralised solutions that speeded up the process.
  • Increased stability: a modernised tool stack on a cloud platform leads to increased stability versus the previous on-premise solution, meaning enhanced reliability in your operations.

Case Studies