UK Banking Group
Learn why one of the largest banking groups in Europe selected Ataccama as their enterprise data quality solution, and kicked off cooperation with a regulatory-driven data quality monitoring use case for BCBS 239.
Business objective
The large, complex financial institution aimed to move away from the use of multiple tools for data quality and select a single, comprehensive solution for enterprise data quality management across the organization. Additionally, the banking group was responding to increasing pressure from the BCBS 239 regulation, which requires banks to report on both regulatory metrics and the quality of the data across the whole lineage.
The group therefore sought a tool which would enable compliance with BCBS 239 as a starting point and which could later meet any enterprise data quality needs that would arise.
Requirements for the solution included:
- Ending the practice of aggregating spreadsheets in the datamart for regulatory reporting
- Automating the ingestion of metadata assets to Ataccama from Collibra
- Centralizing the repository of DQ Rules in Ataccama
- Performing ongoing DQ monitoring in Ataccama
- Controls and failed rules (manual issues) in Ataccama
Solution | Phases & benefits
Early cooperation between Ataccama and the financial institution began with another regulatory-driven project for FSCS compliance. It continued with a separate project for their commercial bank, where Ataccama built a DQ monitoring solution to validate all data coming into the newly-built, hadoop-based Data Hub.
The banking group selected Ataccama as its enterprise-wide DQ solution in 2019, when the new phase of cooperation began with the BCBS 239 use case. The solution went live in June 2019, after which the organization entered a stabilization phase and internal review process. The banking group is currently awaiting external regulatory evaluation, and is poised to be among the first major organizations to achieve certified compliance.
Near-term plans with Ataccama include:
- DQ tool rationalization
- Metadata harvesting through Ataccama
- Support enterprise data hub migration to cloud
- Machine learning prototype
- Data ingestion DQ services
- Hybrid (cloud & on-prem DQ services)
