Customer Identity Resolution

Problem

Customer data is often fragmented across multiple systems, creating duplicate or inconsistent records for the same individual.

Objective

Build a scalable solution that links related records into a single customer profile and supports downstream analytics and engagement.

Core Approach

• Ingest data from multiple sources
• Store raw records in a staging layer
• Apply metadata-driven matching rules
• Create master profiles and link records
• Support both real-time and scheduled processing

Key Components

• Ingestion layer: Kafka / Pub/Sub / RabbitMQ
• Storage: PostgreSQL 16+
• Metadata: profile attributes and matching rules
• Processing: Python-based resolver service

Benefits

• Improved customer data quality
• Reduced duplicate profiles
• Better support for analytics and personalization
• Flexible and extensible architecture

Conclusion

Customer identity resolution is a foundational capability for building a reliable Single Customer 360 View.