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Master Data Management in Application Management

Master Data Management in Application Management

$299.00
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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Course access is prepared after purchase and delivered via email
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This curriculum spans the technical, organisational, and operational challenges of master data management as encountered in multi-year enterprise integration programs, covering the same breadth and depth as a cross-functional MDM advisory engagement supporting global application landscapes.

Module 1: Defining Data Ownership and Stewardship in Complex Enterprises

  • Establish RACI matrices for data domains across business units with conflicting ownership claims
  • Negotiate data stewardship responsibilities between application owners and central data governance teams
  • Resolve disputes over master data ownership in mergers and acquisitions with overlapping systems
  • Implement role-based access controls for stewardship workflows in hybrid cloud environments
  • Define escalation paths for data quality issues when stewards lack authority over source systems
  • Document data lineage accountability when multiple teams contribute to a single golden record
  • Balance local business unit autonomy with enterprise data consistency requirements
  • Integrate stewardship workflows into existing ITIL change management processes

Module 2: Designing Scalable Master Data Models for Heterogeneous Systems

  • Select canonical model granularity based on integration latency requirements and update frequency
  • Determine attribute inheritance rules when consolidating customer data from CRM and ERP systems
  • Model temporal data for product hierarchies with regional pricing and availability variations
  • Implement polymorphic entity patterns for unified party management across customer and supplier domains
  • Design versioning strategies for reference data subject to regulatory changes
  • Choose between centralized and federated model ownership based on system coupling constraints
  • Map legacy system identifiers to enterprise keys without disrupting batch processing schedules
  • Handle schema divergence when integrating SaaS applications with on-premise databases

Module 3: Implementing Identity Resolution and Matching Logic

  • Configure probabilistic matching thresholds to balance false positives and false negatives in customer deduplication
  • Deploy configurable match rules that accommodate regional name formatting conventions
  • Integrate third-party identity verification services without introducing real-time latency
  • Handle fuzzy matching for unstructured data fields like free-text addresses or descriptions
  • Design survivorship rules that preserve critical attributes from conflicting source systems
  • Implement match code extensibility for new data sources without reprocessing historical records
  • Manage matching performance for billion-record datasets using partitioning and indexing strategies
  • Validate match accuracy through statistical sampling when ground truth data is unavailable

Module 4: Building Enterprise Data Hubs with Hybrid Integration Patterns

  • Select between hub-and-spoke, registry, and virtualization architectures based on SLA requirements
  • Implement change data capture from source systems with inconsistent transaction log access
  • Design reconciliation processes for batch integrations with daily cutoff windows
  • Orchestrate data flows across MDM, ETL, and streaming platforms using workflow engines
  • Handle referential integrity when dependent master data arrives out of sequence
  • Configure retry logic and dead-letter queues for failed synchronization events
  • Implement data virtualization layers when source systems prohibit data extraction
  • Optimize payload size and frequency for high-volume supplier data exchanges

Module 5: Governing Data Quality Across the Application Lifecycle

  • Define measurable data quality rules tied to business outcomes, not just technical accuracy
  • Enforce data quality gates in CI/CD pipelines for application deployments
  • Configure real-time validation rules that don't block critical transaction processing
  • Track data quality KPIs across source systems, MDM, and consuming applications
  • Implement automated remediation workflows for common data defects
  • Balance strict validation against business continuity during system migrations
  • Integrate data quality monitoring with enterprise observability platforms
  • Establish feedback loops from downstream analytics to upstream data entry points

Module 6: Securing Sensitive Master Data in Multi-Tenant Environments

  • Implement attribute-level masking for PII fields based on user roles and jurisdictions
  • Design encryption key management strategies for data at rest and in transit
  • Enforce data residency requirements for global customer records
  • Configure audit logging for sensitive data access without degrading performance
  • Implement dynamic data masking in test environments using synthetic data
  • Manage consent flags for marketing data across multiple legal bases
  • Handle data subject access requests (DSARs) in distributed MDM architectures
  • Validate security controls when third-party vendors access master data APIs

Module 7: Managing Metadata and Lineage in Evolving Landscapes

  • Automate metadata harvesting from 20+ source systems with varying API capabilities
  • Map business terminology to technical attributes across legacy and modern applications
  • Track data lineage through ETL transformations when documentation is incomplete
  • Implement impact analysis for schema changes affecting downstream reporting
  • Maintain metadata consistency during application modernization projects
  • Integrate business glossary updates with data model version control
  • Handle metadata drift when business units customize SaaS applications
  • Expose lineage information to non-technical stakeholders through simplified views

Module 8: Operationalizing MDM in Production Application Ecosystems

  • Design service level agreements for MDM response times based on business process criticality
  • Implement monitoring for golden record availability during peak transaction periods
  • Configure failover strategies when the primary MDM system becomes unavailable
  • Manage configuration drift between development, test, and production MDM environments
  • Plan capacity for MDM infrastructure based on projected data growth and access patterns
  • Execute zero-downtime upgrades for MDM software with active integrations
  • Troubleshoot data synchronization delays in high-latency global networks
  • Optimize garbage collection and archiving for historical master data

Module 9: Aligning MDM Initiatives with Business Transformation Programs

  • Sequence MDM deliverables to support phased ERP implementation timelines
  • Adapt data models to accommodate new business models like subscription services
  • Integrate MDM outcomes into business process reengineering efforts
  • Manage data dependencies during application rationalization and sunsetting
  • Coordinate data migration scope with business unit change readiness
  • Align master data KPIs with executive scorecards and operational metrics
  • Handle data governance exceptions during urgent digital transformation projects
  • Scale MDM capabilities to support new market entries with localized data requirements
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