Description

This curriculum spans the design and operationalization of data governance across complex, cross-system business processes, comparable in scope to a multi-phase integration program involving ERP harmonization, M&A data consolidation, and enterprise-wide compliance alignment.

Module 1: Establishing Governance Frameworks in Cross-Functional Environments

Define scope boundaries for data governance when integrating finance, HR, and supply chain systems to prevent overreach and ensure executive buy-in.
Select between centralized, decentralized, or federated governance models based on organizational maturity and system interdependencies.
Negotiate data ownership responsibilities with business unit leaders who resist ceding control over their operational datasets.
Document escalation paths for data disputes involving conflicting definitions between sales and marketing departments.
Align governance charter with existing enterprise architecture standards to ensure compliance with IT policy mandates.
Integrate data governance roles (e.g., stewards, custodians) into RACI matrices for shared business processes.
Implement governance operating rhythm with monthly stewardship council meetings tied to business process review cycles.
Assess regulatory exposure across jurisdictions when designing global data classification schemes.

Module 2: Data Quality Integration Across Disparate Systems

Configure data quality rules in ETL pipelines to reconcile customer address formats between CRM and ERP systems.
Set thresholds for acceptable match rates during master data synchronization between legacy and cloud platforms.
Deploy data profiling tools to identify root causes of duplicate supplier records in procurement workflows.
Balance real-time validation against system performance when embedding data quality checks in order entry processes.
Map data quality metrics (completeness, accuracy, timeliness) to KPIs in service level agreements with third-party vendors.
Design exception handling procedures for rejected transactions due to data quality failures in payment processing.
Coordinate data cleansing initiatives with business process reengineering timelines to minimize operational disruption.
Implement data quality dashboards accessible to process owners in logistics and inventory management.

Module 3: Metadata Management for Process Transparency

Standardize business glossary terms for “customer lifetime value” used inconsistently across analytics and billing systems.
Automate technical metadata extraction from SAP and Salesforce to map field-level lineage in invoice reconciliation processes.
Resolve conflicts between source system metadata and data warehouse semantic models during M&A integration.
Link process documentation in ARIS or Signavio to corresponding data elements in the metadata repository.
Enforce metadata tagging requirements for new API endpoints exposed to external partners.
Implement version control for data models when upgrading core banking systems with backward compatibility constraints.
Configure metadata access controls to restrict sensitive field documentation to authorized compliance personnel.
Use metadata lineage to trace root cause of discrepancies in monthly financial close reports.

Module 4: Master and Reference Data Synchronization

Select a system of record for product hierarchy data when consolidating data from regional subsidiaries.
Design golden record resolution logic for customer MDM that reconciles conflicting phone numbers from call center and e-commerce sources.
Implement change data capture to propagate updates to global employee reference data across payroll and timekeeping systems.
Negotiate data stewardship authority for master data domains between corporate HQ and autonomous business units.
Configure fallback mechanisms for reference data unavailability during peak transaction processing in retail POS systems.
Validate country code mappings against ISO standards in international shipping workflows.
Manage lifecycle states for discontinued products in master data to prevent erroneous reordering.
Enforce validation rules for GL account codes during journal entry to ensure consistency with chart of accounts.

Module 5: Regulatory Compliance in Integrated Workflows

Map GDPR data subject rights fulfillment processes to data flows in customer onboarding and support systems.
Implement data retention policies in document management systems aligned with SEC Rule 17a-4 for trade records.
Configure audit logging for access to PII in HRIS and benefits administration platforms.
Design data minimization controls in forms used for loan origination to collect only legally required information.
Conduct data protection impact assessments (DPIAs) for new cloud-based procurement solutions.
Enforce encryption standards for PHI transmitted between EHR and billing systems under HIPAA requirements.
Document data residency requirements for customer data stored in multi-tenant SaaS environments.
Integrate regulatory change management into governance workflows to update controls when new privacy laws take effect.

Module 6: Data Access and Security Governance

Define role-based access controls for financial data in consolidation systems based on organizational hierarchy and job function.
Implement attribute-based access control (ABAC) for pricing data in CRM systems based on territory and product line.
Negotiate data masking rules for test environments used in integration testing with external vendors.
Enforce segregation of duties between users who can create vendors and those who can approve payments.
Integrate data access requests with IT service management tools like ServiceNow for auditability.
Configure dynamic data masking in reporting tools to hide sensitive salary information from non-HR users.
Review access entitlements quarterly for shared service center roles handling multiple business processes.
Implement just-in-time access for third-party support personnel during integration maintenance windows.

Module 7: Data Governance in Mergers and System Consolidation

Conduct data domain gap analysis between acquiring and target company customer data models during due diligence.
Establish interim data reconciliation processes for overlapping supplier records during ERP harmonization.
Design data migration validation rules to ensure transactional continuity in order-to-cash processes post-merger.
Resolve conflicting data ownership models when integrating autonomous divisions with different governance histories.
Implement temporary data quality monitoring for merged employee datasets during benefits system cutover.
Harmonize data classification policies for intellectual property across R&D systems from both organizations.
Coordinate data governance timelines with merger integration program milestones to avoid rework.
Document data lineage for legacy system extracts used during parallel run periods.

Module 8: Performance Monitoring and Continuous Improvement

Define SLAs for data availability in intercompany settlement processes with measurable uptime thresholds.
Track data incident resolution times and correlate with process delays in month-end reporting.
Implement automated alerts for deviations in data volume or timing during daily batch integrations.
Conduct root cause analysis for recurring data errors in inventory adjustment workflows.
Benchmark data rework rates across business units to identify governance maturity gaps.
Integrate data health metrics into executive dashboards for supply chain and financial operations.
Adjust stewardship workloads based on volume of data change requests during peak planning cycles.
Refine data quality rules quarterly based on false positive rates in fraud detection systems.

Module 9: Stakeholder Engagement and Change Management

Develop tailored data governance communication plans for process owners in manufacturing, sales, and finance.
Conduct data impact workshops prior to launching new integration middleware to surface hidden dependencies.
Address resistance from regional managers by demonstrating data governance benefits in local performance metrics.
Train super users in procurement teams to use data stewardship tools for resolving catalog mismatches.
Align data governance milestones with major business process change initiatives to leverage momentum.
Establish feedback loops from help desk tickets to identify recurring data-related process failures.
Incorporate data governance KPIs into performance reviews for process managers in shared services.
Facilitate joint problem-solving sessions between IT and business teams for cross-system data issues.

Module 10: Technology Selection and Integration Architecture

Evaluate metadata management tools based on native connectors for existing MDM and ETL platforms.
Assess scalability of data governance platforms to handle high-frequency data updates in real-time trading systems.
Design API contracts for governance services (e.g., data validation, lineage lookup) consumed by integration middleware.
Integrate data quality monitoring into CI/CD pipelines for data warehouse deployment automation.
Negotiate licensing models for governance tools based on number of data domains versus user seats.
Implement event-driven architecture to trigger stewardship workflows upon detection of critical data anomalies.
Configure data catalog auto-discovery schedules to minimize performance impact on production databases.
Ensure governance tooling supports audit trail export requirements for regulatory examinations.