Refine: Data Refinement
Overview of the Refine phase in the CORE framework
Refine: Data Refinement
Section titled “Refine: Data Refinement”Refining data into actionable insights requires unified profiles and intelligent decision-making systems. This phase covers building customer profiles and decision engines.
Overview
Section titled “Overview”The Refine phase transforms organized data into unified customer profiles and enables intelligent decision-making through rule-based logic and machine learning. It bridges the gap between raw data and actionable insights.
Key Concepts
Section titled “Key Concepts”Unified Profiles Store
Section titled “Unified Profiles Store”Create a single source of truth for customer data:
- Identity resolution: Merge data from multiple sources
- Profile enrichment: Continuously update profiles with new data
- Privacy compliance: Respect user preferences and regulations
Decision & Intervention Engine
Section titled “Decision & Intervention Engine”Automate decision-making:
- Rule-based logic: Define clear business rules
- Machine learning: Use ML models for complex decisions
- A/B testing: Validate interventions before full rollout
Outcomes
Section titled “Outcomes”- Identity resolution system implemented
- Unified customer profiles created
- Decision engine architecture designed
- Business rules defined and documented
- ML models trained and deployed (if applicable)
- A/B testing framework in place
Artifacts
Section titled “Artifacts”- Identity Graph: Mapping of customer identities across touchpoints
- Unified Profile Schema: Structure for customer profiles
- Decision Rules: Business logic for automated decisions
- ML Models: Trained models for predictive decisions
- A/B Test Results: Validation data for interventions
Pitfalls
Section titled “Pitfalls”- Identity fragmentation: Failing to properly resolve identities leads to duplicate profiles
- Stale data: Not keeping profiles updated with latest information
- Over-automation: Automating decisions that require human judgment
- Bias in models: ML models that perpetuate existing biases
- No validation: Deploying interventions without proper testing