DQM - Data Quality Management development

We enhanced Sberbank’s data quality management system by optimizing thousands of validation rules, automating error detection, and introducing detailed error reports to accelerate data validation and correction.

1000+

optimized validation rules

50%

faster error detection

40%

faster data correction

supported reporting systems

Banking - Financial and Insurance Sectors / Data and Information Management / Risk Management

Client Description

Sberbank Hungary was the Hungarian subsidiary of the international Sberbank Group, providing banking services for both retail and corporate clients. Its activities included lending, account management, and offering various savings and investment products. It operated under the ownership of the Russian parent company and aimed to provide innovative, customer-focused financial solutions in the Hungarian market.

Project General Description

Data Quality Management (DQM) plays a crucial role in the operation of modern companies. The foundation of effective data management and analysis is the presence of accurate, reliable, and consistent data. Within the scope of this project, we optimized and enhanced Sberbank’s DQM system to meet the challenges posed by increasing data volume and complexity. During the development process, we focused on automation, improving efficiency, and creating user-friendly solutions. The new system enables faster error detection, more effective handling of data quality issues, and the generation of reports that are easily interpretable for business users.

Challenge

The data loaded into the data warehouse is validated according to predefined rules to ensure data accuracy and compliance. The system contains thousands of rules that needed to be optimized for efficient operation.

During the optimization, the client’s request was to make the development and modification of these rules quick and effective, enabling the system to adapt flexibly to changing business needs.

In the business domain, manual correction of data quality had previously posed significant challenges for the bank. To simplify and expedite the work of data stewards, the client sought a solution that could make this manual correction process more effective.

How we helped?

Optimization of the Rule System: The system contains thousands of rules that were optimized to ensure efficient operation. The rule system is flexible and easily extendable, enabling quick adaptation to new business requirements and data structures.

Scheduled Execution and Fast Error List Generation for Simplified Manual Corrections: To improve the process, we enhanced the system with a detailed error list used by data stewards during month-end closing. This error list enables them to quickly identify and correct faulty rows and fields using record identifiers and key values.

More Efficient Solutions to Replace Manual Checks: To address data quality issues and identify discrepancies among various data sources, we replaced the previous manual verification processes with more efficient, automated solutions. This improvement significantly enhanced the effectiveness of data quality assessments for Basel II, RDP, and Hitreg reports.

IBM Cognos IBM Data Stage IBM DB2