Data Scale, a company specializing in implementing digital solutions for the financial sector, has announced the completion of the first stage of a large-scale project to build a corporate data warehouse (DWH) at Universal Bank (Uzbekistan). This marks a significant milestone within the bank’s strategic digital transformation initiative, first announced in April 2026.
The project kicked off in April 2026. Its key objectives include not only building a modern analytical infrastructure but also meeting regulatory requirements — namely, transitioning to new standards for data collection and reporting analysis as part of the RegTech initiative by the Central Bank of the Republic of Uzbekistan.
Results of the First Stage: Infrastructure Ready
During the first stage, Data Scale specialists carried out all necessary preparatory work to launch the data warehouse development. The client provided hardware for the DEV environment of the corporate data warehouse (CDW), intended for developing and testing functionality. In turn, the Data Scale team performed a comprehensive set of tasks to configure this environment.
Key results of the first stage:
- Infrastructure and Security: Achieved the necessary network connectivity for all system components, implemented basic information security requirements, and provided access for development specialists.
- Software Deployment: Installed and configured the software required for the data warehouse to operate. Prepared database tablespaces, as well as configured user accounts and a role-based access model.
- Documentation: Developed and agreed with the client on an installation plan for the DEV environment, ensuring transparency and reproducibility of all completed work.
Next Up: Analytics and Regulatory Reporting
Having completed the infrastructure preparation, the contractor has already moved on to the second stage of the project, which involves direct development of the data warehouse core. The team’s focus is on comprehensive analysis of data sources, particularly the Integrated Automated Banking System (IABS), and the implementation of basic CDW entities.
This stage will create detailed data store layers (Data Detail Store, DDS), including information on clients, accounts, balances, and turnovers, along with associated reference data. This will form the foundation for subsequent construction of data marts and generation of regulatory reports for the Central Bank.
Roman Solovyov, CEO of Data Scale, commented on the completion of the first stage:
„Completing the first stage is not just a formal milestone; it is confirmation of our ability to work as a unified team with the IT department of Universal Bank. We quickly and efficiently prepared the ‚foundation‘ for the data warehouse — deploying the development environment and ensuring its seamless integration into the bank’s IT landscape. Now everything is ready for the most interesting part — populating this infrastructure with data and creating analytical models. We are confident that the platform being built will become a key tool for management decision-making and will allow the bank to effortlessly scale any regulatory and analytical tasks in the future.“
The DWH development project is a logical continuation of the long-standing partnership between Data Scale and Universal Bank. Earlier, the company conducted an audit of IT systems, developed a technology strategy, and successfully implemented the first stage of creating an enterprise service bus (ESB), laying a solid foundation for current and future initiatives.
About the Companies
Data Scale is a developer of digital solutions for banks and financial organizations. Bringing together experts with years of experience in international IT projects, Data Scale creates comprehensive, scalable systems focused on efficiency, security, and long-term client development.
Universal Bank is a dynamically growing financial institution in Uzbekistan, offering a wide range of services for corporate and retail clients. The bank is actively implementing digital technologies to improve service quality and operational efficiency.
