The initiative is structured in three core phases:
Phase 1: Diagnostic Assessment & Benchmarking
- Maturity assessment using DAMA DMBoK and comparable frameworks.
- Review of EDW architecture, metadata practices, and quality controls.
- Consultations with key departments (Bank Supervision, Research, IT, Payments, Financial Markets).
- Peer benchmarking with other central banks.
Phase 2: Data Strategy Development
- Articulation of a strategic data vision anchored on accountability, quality, and value.
- Design of data governance structures including role definitions (e.g., Chief Data Officer, Data Council).
- Specification of validation protocols for GDI data flows and AI-readiness.
- Framework for deploying AI/ML for risk monitoring, predictive analytics, and LLM use cases.
- Recommendations for ethical and secure use of internal LLMs and natural language tools.
Phase 3: Roadmap & Implementation Plan
- Practical short-, medium- and long-term roadmap with milestones.
- Immediate focus areas: validation protocols, data governance structures, and AI/ML pilots.
- Institutionalization of GDI data into supervisory analytics workflows.
- Co-created capacity building on data governance, visualization, and AI/ML techniques.
- Partnership exploration with local universities, peer regulators, and tech ecosystem actors.
