Network Governance / Advisory Mapping
Data model and visualization layer for mapping advisory relationships and network governance structures.
- Role
- Backend & Data Engineer
- Timeline
- 2024
Overview
Project summary and scope.
A data-centric project focused on mapping advisory relationships, asset inventories, and governance signals across a complex network. The work combines structured data modeling, matching logic, and reporting views to improve visibility and reduce noise in advisory workflows.
My Contributions
Key areas of ownership and delivery.
- Designed entity relationship models for assets, advisors, and communications
- Implemented matching logic for advisory email and inventory alignment
- Built API endpoints for reporting and governance visibility views
- Created sample datasets and output formats for stakeholder review
- Documented data quality rules and noise reduction approach
Core Features
Primary capabilities delivered in this project.
- Asset inventory normalization and enrichment pipeline
- Advisory email parsing and entity matching workflow
- Match / no-match result reporting with confidence placeholders
- Noise reduction filters for duplicate and low-signal records
- Governance dashboard data contracts for frontend consumers
Architecture
How the system is structured at a high level.
The architecture centers on a PostgreSQL data store with staging and curated layers. Ingestion jobs normalize raw inputs, matching services produce relationship outcomes, and REST APIs expose filtered reporting datasets. Visualization layers consume stable contracts rather than ad hoc queries.
Impact
Outcomes and value delivered.
- Improved governance visibility across advisory relationship mapping
- Reduced manual reconciliation effort through structured matching outputs
- Delivered reporting-ready datasets for stakeholder review
Challenges
Constraints and difficulties encountered during delivery.
- Reconciling inconsistent identifiers across inventory and communication sources
- Defining match confidence thresholds that balance precision and recall
- Presenting complex relationship data in scannable reporting views
Tradeoffs
Key decisions and the reasoning behind them.
- Prioritized explainable matching rules over black-box ML matching for auditability
- Used batch pipelines instead of real-time streaming to simplify early delivery
- Represented some client-specific fields with anonymized sample data
Future Improvements
Next steps that would strengthen or extend this work.
- Add interactive graph exploration for advisory networks
- Introduce incremental matching for live data feeds
- Expand anomaly detection for governance risk signals
- Integrate role-based access for sensitive relationship data
Problem and Solution
Problem
Advisory and governance teams struggled to reconcile asset inventories, email signals, and relationship data across systems. Manual matching produced noise, missed connections, and limited reporting confidence.
Solution
Built a structured data model, matching pipeline, and reporting layer that maps advisory relationships, surfaces match/no-match outcomes, and reduces noisy inputs through normalization and filtering rules.
Tech Stack
Technologies used across this project.
- Python
- SQL
- PostgreSQL
- REST APIs
- D3.js