Description

This curriculum spans the technical and governance dimensions of data harmonization at the scale of a multi-workshop integration program, addressing the same data modeling, pipeline design, and compliance challenges encountered in enterprise-wide process integration initiatives.

Module 1: Defining Cross-System Data Semantics

Select canonical data models for customer, product, and transaction entities across ERP, CRM, and supply chain systems.
Map legacy field definitions (e.g., “order_status”) to unified business glossaries with version-controlled metadata.
Resolve conflicting data types (e.g., date formats, currency precision) between source systems during schema alignment.
Implement controlled vocabularies for categorical fields using ISO or industry-specific standards (e.g., UNSPSC, ISO 4217).
Document ownership and stewardship roles for each canonical entity across business units.
Establish conflict resolution protocols for divergent definitions proposed by different departments.
Design backward-compatible schema evolution paths for shared data models.
Integrate data semantics into CI/CD pipelines using schema registry tools.

Module 2: Real-Time vs. Batch Integration Patterns

Choose between event-driven streaming (Kafka, Pulsar) and scheduled ETL based on SLA requirements for data freshness.
Configure message serialization formats (Avro, Protobuf) to balance schema enforcement and payload efficiency.
Implement idempotency in event consumers to handle duplicate messages during retries.
Set up dead-letter queues and monitoring for failed message processing in asynchronous pipelines.
Size and partition topics based on throughput projections and retention policies.
Evaluate cost and operational overhead of maintaining real-time pipelines versus nightly batch windows.
Design compensating transactions for rollback scenarios in eventual consistency models.
Orchestrate hybrid workflows where master data syncs in batch and transactional data streams in real time.

Module 3: Identity Resolution and Entity Matching

Configure probabilistic matching algorithms to link customer records across systems with partial overlaps.
Define match thresholds that balance precision and recall based on use-case tolerance for false positives.
Integrate deterministic rules (e.g., SSN, tax ID) with fuzzy matching (name, address) in identity graphs.
Handle merge conflicts when reconciling conflicting attribute values (e.g., different email addresses).
Implement golden record promotion with audit trails for lineage and rollback capability.
Deploy survivorship rules that prioritize source systems based on data quality SLAs.
Scale matching jobs using distributed computing frameworks (Spark) for large master data sets.
Expose resolved identities via API with rate limiting and access controls.

Module 4: Data Quality Monitoring at Scale

Define measurable data quality dimensions (completeness, accuracy, timeliness) per critical data object.
Embed data profiling jobs in ingestion pipelines to detect schema drift and anomalies.
Set up automated alerts for threshold breaches (e.g., null rates exceeding 5% in key fields).
Instrument lineage tracking to trace data quality issues to root source systems.
Configure dynamic baselines for metrics that vary by business cycle (e.g., weekend vs. weekday volumes).
Integrate data quality scores into operational dashboards used by business analysts.
Assign remediation workflows to data stewards based on domain ownership.
Log and version data quality rules to support audit and regulatory compliance.

Module 5: Master Data Management Architecture

Select between centralized MDM hubs and registry-based federated models based on organizational autonomy.
Deploy MDM hubs with support for multi-domain governance (customer, product, supplier).
Configure data synchronization modes: publish/subscribe, request/response, or batch extract.
Implement role-based access controls to restrict sensitive master data modifications.
Design approval workflows for high-impact changes (e.g., product classification updates).
Integrate MDM with enterprise data catalogs for discoverability and context.
Manage cross-system dependencies during master data updates to prevent downstream failures.
Plan for disaster recovery and data consistency across geographically distributed MDM instances.

Module 6: Handling Data Lineage and Provenance

Instrument ETL and streaming jobs to emit lineage metadata for each data transformation.
Map field-level lineage from source systems to business intelligence reports.
Store lineage data in graph databases to support impact analysis queries.
Automate lineage capture using parser-based tools for SQL and stored procedures.
Expose lineage information via API for compliance and audit reporting.
Handle lineage gaps in legacy systems lacking instrumentation capabilities.
Define retention policies for lineage data based on regulatory requirements.
Visualize end-to-end data flows for stakeholder review during system decommissioning.

Module 7: Cross-System Reference Data Synchronization

Identify authoritative sources for reference data (e.g., country codes, payment terms).
Design distribution mechanisms: push-based notifications or pull-based polling.
Version reference data sets to support backward compatibility during updates.
Implement validation rules at consumption points to reject outdated reference values.
Handle time-zone-sensitive reference data (e.g., fiscal calendars) across regions.
Coordinate updates during maintenance windows to minimize process disruption.
Log reference data changes for audit and reconciliation purposes.
Cache reference data in application layers with cache-invalidation strategies.

Module 8: Governance and Compliance in Data Integration

Classify data assets by sensitivity (PII, financial, health) for access control enforcement.
Implement data masking and tokenization in non-production environments.
Enforce consent management policies for customer data shared across systems.
Document data flows for GDPR, CCPA, and other regulatory impact assessments.
Conduct data protection impact assessments (DPIAs) before launching new integrations.
Integrate with enterprise identity providers for centralized authentication and auditing.
Define data retention and deletion rules aligned with legal hold requirements.
Generate compliance reports showing data handling practices across the integration landscape.

Module 9: Operational Monitoring and Incident Response

Define SLAs for data pipeline uptime, latency, and error rates.
Configure centralized logging and correlation IDs across integration components.
Set up synthetic transactions to proactively test end-to-end data flows.
Establish escalation paths for data incidents based on business impact severity.
Conduct root cause analysis for data mismatches using lineage and log data.
Maintain runbooks for common failure scenarios (e.g., source system downtime).
Implement automated failover mechanisms for critical data synchronization jobs.
Review integration performance metrics quarterly to identify technical debt and optimization opportunities.