Data as Infrastructure: Building the Foundations for AI-Driven Banking
Data infrastructure is the foundation for AI-driven banking - but most institutions are still building on shaky ground. What it takes to get the data layer right.
Every bank wants to be "data-driven". Very few have built the foundations to make it possible. Global Banking Monitor's 70-page report explores why banks must treat data as critical infrastructure - not a support function - and what it takes to build the architecture, governance, and quality foundations that enable advanced analytics, machine learning, and enterprise-wide AI adoption.
Key findings
- Data architecture is the bottleneck for AI ambitions. Banks investing in AI models and tooling without first addressing data fragmentation, quality, and accessibility find that their initiatives cannot scale beyond isolated experiments.
- Legacy data architectures are fundamentally incompatible with modern AI requirements. Batch-oriented, siloed data warehouses cannot support the real-time, cross-functional data access that production AI systems demand.
- Data governance remains immature in most institutions. Ownership structures are unclear, policies are inconsistent, and the gap between documented governance frameworks and actual practice is significant.
- Real-time data platforms are becoming essential. Streaming architectures and event-driven systems enable the low-latency data access that fraud detection, personalisation, and real-time decisioning require.
- The investment required is substantial but unavoidable. Building modern data infrastructure is expensive and complex, but the cost of not doing so - in failed AI programmes, poor decision-making, and competitive disadvantage - is higher.
What the report covers
- Executive Summary - Data as a strategic asset and key priorities
- The Data Imperative - Why data matters now and competitive advantage
- Current Architectures - Legacy vs modern and fragmentation issues
- Governance Models - Ownership structures and data policies
- Data Quality Challenges - Accuracy issues and inconsistency
- Real-Time Data Platforms - Streaming architectures and event-driven systems
- Enabling AI - Data pipelines and model readiness
- Case Studies - Leading implementations
- Implementation Challenges - Cost and complexity
- Strategic Roadmap - Building data capability
Who should read this
This report is designed for Chief Data Officers, CIOs, enterprise architects, and data platform leaders responsible for building the foundations for data-driven banking. It is equally relevant for AI and analytics leaders whose programmes depend on data infrastructure they do not control.
For enquiries about accessing this report, contact [email protected]
