Banking Sector – A Future-Ready Process Model for Growing Analytics Requirements
Data Governance for Complex Data Analytics
Starting point
- Defined data strategy and implemented data governance organization
- Existing enterprise analytics platform as the technical backbone
- Rapidly increasing volume of highly complex and regulatory ad-hoc requests
- Expansion of governance with scalable processes and precise role definitions
Approach
- Comprehensive project management and strategic oversight
- In-depth current-state analysis including gap assessment and recommendations
- Conducting interviews and workshops
- Coordination with business units, analytics, and data governance
- Development of a communication and rollout plan
- Design and conceptualization of target processes, including BPMN models
Features/Project outcome
- Target Vision: Development of a concept for complex data analysis processes
- Robust Process Model: Definition and alignment of target processes, including roles and responsibilities
- Decision Templates: Creation of clear recommendations for action based on gathered insights
- Operationalization: Support in the implementation and rollout of the new process landscape
Customer benefits
- Establishment of clear, binding, and robust process structures
- More efficient coordination and reduced lead times
- Significant increase in traceability and transparency
- Secure handling of highly complex regulatory and internal ad-hoc requests
- Maximum compliance assurance for data requests
Benjamin Armbrüster | Senior Manager