Developing a Data Strategy and Roadmap

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.