Advisory and architecture services for finance-grade data platforms
Carlton Brooke focuses on a concise set of high-value services for organizations that need stronger financial data foundations, clearer architecture, and governed analytics.
Common challenges in financial data environments
Many organizations struggle with fragmented systems, inconsistent metrics, and data foundations not designed for modern analytics or AI.
Fragmented financial data across systems
ERP, procurement, treasury, budgeting, and reporting environments often evolve separately, making alignment and reconciliation difficult.
Lack of trust in reporting and metrics
Inconsistent definitions, unclear lineage, and manual adjustments create uncertainty around the numbers leaders rely on.
Difficulty scaling analytics and AI initiatives
Data environments may support isolated reporting, but not the governed structures needed for broader analytics or AI-driven workflows.
Legacy ETL and reporting constraints
Older pipelines and warehouse designs limit flexibility, increase maintenance effort, and slow modernization.
A structured approach to modern financial data platforms
Carlton Brooke applies an architecture-first approach focused on alignment, governance, and scalable analytics foundations.
Align data across financial systems
Integrate ERP, procurement, treasury, and reporting data into unified structures with consistent definitions and reconciliation logic.
Establish governed data models
Design conceptual and logical models that preserve business meaning, enforce integrity, and support long-term scalability.
Build trusted semantic layers
Create curated metrics and structures that ensure consistency across reporting, dashboards, and analytics.
Modernize data platforms
Transition legacy ETL and reporting into scalable, cloud-aligned architectures designed for flexibility and performance.
Financial Data Platform Architecture
Design target-state architectures for finance, accounting, treasury, procurement, and operational data that support reporting, planning, and executive decision support.
Enterprise Data Modeling
Develop conceptual and logical data models that bring consistency to complex environments while preserving business meaning, lineage, and auditability.
Analytics & Semantic Layer Design
Structure curated analytics layers and governed metrics so BI tools, executive dashboards, and AI systems can reason over trusted data.
Finance Data Modernization
Modernize legacy ETL, reporting, and warehouse environments into scalable platforms aligned to cloud data engineering and governed delivery.
Discuss your financial data architecture and analytics strategy
If these challenges reflect your environment, the next step is a focused conversation.