DQM - Data Quality Management development

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 operations of modern enterprises. The foundation of effective data management and analysis lies in having accurate, reliable, and consistent data. Within the framework of this project, we optimized and developed Sberbank's DQM system to meet the challenges posed by the growing volume and complexity of data. During the development, we placed a strong emphasis on automation, increasing efficiency, and designing 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 by business users.

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Banking, Financial, and Insurance Sectors
 / 
Risk Management

Challenge

The verification of data loaded into the data warehouse is conducted according to predefined rules, ensuring data accuracy and compliance. The system contains thousands of rules that we have optimized for efficient operation. The automated checks cover data integrity, consistency, completeness, and accuracy.

To enhance the effectiveness of the DQM system, it was necessary to optimize thousands of rules. During the optimization process, we ensured that the development and modification of rules were quick and efficient, allowing the system to adapt flexibly to changing business requirements.

Previously, the manual correction of data quality issues in the business domain posed significant challenges. To improve this process, we supplemented the system with a detailed error list that data stewards use during the month-end closing. This error list enables them to more quickly identify and rectify erroneous rows and fields using record identifiers and key values.

How we helped?

  • Automated Data Validation and Rule System
  • The rule system is flexible and easily extendable, allowing for quick adaptation to new business requirements and data structures.
  • Scheduled Execution and Rapid Error List Generation
  • The error list contains record identifiers and key values, making it easy to identify erroneous rows and fields.
  • 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 DB2
  • DataStage
  • Cognos