Description

This curriculum spans the technical, operational, and governance dimensions of building and maintaining a centralized customer data platform, comparable in scope to a multi-phase internal capability program that aligns data architecture with customer service, marketing, and compliance workflows across complex organizations.

Module 1: Defining the Centralized Data Strategy for Customer-Centric Operations

Establishing ownership and accountability for data domains across marketing, sales, and support functions
Selecting a canonical data model that reconciles customer identifiers across legacy systems
Deciding whether to adopt a data mesh or centralized data warehouse based on organizational maturity and latency requirements
Mapping customer journey stages to required data entities and operational touchpoints
Negotiating data sharing agreements between business units with competing KPIs
Defining SLAs for data freshness based on real-time personalization use cases
Aligning data governance council membership with enterprise operating model
Assessing regulatory constraints (e.g., GDPR, CCPA) during initial schema design

Module 2: Integrating Disparate Customer Data Sources

Resolving identity resolution conflicts between CRM, web analytics, and call center logs
Implementing change data capture (CDC) for high-frequency transaction systems without overloading source databases
Designing error handling and retry logic for API-based ingestion from third-party platforms
Choosing between batch and streaming ingestion based on downstream personalization latency needs
Handling schema drift from SaaS applications with frequent updates
Validating data completeness and accuracy during ETL from regional subsidiaries
Configuring secure service accounts and OAuth scopes for cloud data connectors
Standardizing address and phone number formats across global customer records

Module 3: Building a Unified Customer Profile

Determining merge logic for conflicting customer attributes from multiple sources
Implementing probabilistic matching when deterministic keys are unavailable
Defining retention policies for behavioral data in the profile store
Architecting real-time profile updates for use in chatbot and IVR interactions
Isolating PII in secure storage while enabling authorized access for service teams
Versioning customer profile schemas to support backward compatibility
Setting thresholds for data confidence scores used in decisioning systems
Enabling opt-out propagation across systems upon customer request

Module 4: Operationalizing Data for Frontline Teams

Designing API response payloads to minimize latency in agent desktop applications
Implementing caching strategies for high-frequency customer lookups
Embedding customer sentiment scores into service queue prioritization logic
Configuring role-based data access for field technicians and retail staff
Integrating next-best-action recommendations into CRM workflows
Monitoring API usage patterns to identify training gaps or adoption issues
Synchronizing offline customer interactions to the central profile after reconnect
Validating data accuracy through agent feedback loops in support tools

Module 5: Enabling Real-Time Decisioning at Scale

Selecting stream processing framework (e.g., Kafka Streams, Flink) based on state management needs
Designing event schemas that support both immediate actions and historical analysis
Implementing fallback logic when real-time models are unavailable
Calibrating decision thresholds to balance personalization with operational capacity
Managing model drift detection in dynamic customer behavior environments
Orchestrating A/B tests for decision logic without disrupting customer journeys
Logging decision rationale for audit and compliance review
Throttling high-volume event processing during system degradation

Module 6: Governing Data Quality and Compliance

Defining data quality rules per field based on operational criticality
Automating data profiling to detect anomalies in customer attribute distributions
Implementing data lineage tracking for regulatory reporting
Handling data subject access requests (DSARs) across distributed systems
Documenting data classification levels and encryption requirements
Conducting third-party vendor assessments for data processing activities
Enforcing data retention and deletion schedules across backups and archives
Coordinating cross-functional incident response for data breaches