Detailed Phase Breakdown

Phase 1: Data Analysis and Discovery
Analyze source systems, data structures, and business requirements to establish the foundation for the Canonical Data Model (CDM) implementation. This phase focuses on understanding source data, identifying business entities, defining mapping requirements, and preparing the metadata required for automated code generation and downstream processing.
  • Assess source system schemas, data volumes, and data quality characteristics.
  • Identify customer, campaign, flowchart, and related business entities for onboarding.
  • Define source-to-canonical mappings and transformation requirements.
  • Analyze relationships between source systems and target CDM layers.
  • Identify historical data requirements, retention policies, and incremental load strategies.
  • Define Customer 360, Campaign 360, and Flowchart 360 business requirements and KPIs.
  • Document data quality rules, validation requirements, and exception handling scenarios.
  • Prepare metadata definitions required for automated ETL generation and orchestration.
  • Review security, governance, and compliance requirements for data movement and storage.
  • Produce source-to-target mapping (STM) documents and implementation specifications for bespoke ETL implementation.
Phase 2: ETL Development

Build upon foundation setup by refining and optimizing generated code. This phase takes the generated code from Foundation Setup and makes it production-ready through performance optimization and error handling.

  • Optimize generated SQL and DBT models for performance
  • Implement advanced error handling and recovery mechanisms
  • Add comprehensive logging and monitoring
  • Implement restart from checkpoint logic
  • Performance tune indexes and queries
  • Create operational run books and documentation
  • Implement and optimize Aggregate Layer processing using metadata-driven dbt incremental models to efficiently pre-compute Customer 360, Campaign 360, and Flowchart 360 metrics while minimizing direct query load on the RDV layer.
  • Implement and optimize ML feature generation pipelines using Customer 360, Campaign History (CH), and Response History (RH) data to support STO, NBC.
Phase 3: Testing and Validation

Execute comprehensive testing using generated and optimized code.

  • Unit testing of individual transformations
  • Integration testing of full data flows (Source → LDZ → RDV → Unica 360)
  • Data quality testing with all DQ rules
  • Volume testing with production-scale data
  • UAT testing with business users
  • Validate Aggregate Layer outputs for correctness of rolling window metrics, aggregation accuracy, and consistency with underlying RDV data.
Phase 4: Production Deployment

Deploy code and execute first production loads with comprehensive monitoring and validation.

Phase 5: Ongoing Operations (Post-Deployment)
  • Monitor and maintain the ETL pipelines.
  • Handle schema extensions, optimize performance.
  • Plan future onboarding of new subject areas leveraging the canonical framework.