Skip to main content
MDM Business Processes in Business Process Redesign

MDM Business Processes in Business Process Redesign

$199.00
How you learn:
Self-paced • Lifetime updates
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.
When you get access:
Course access is prepared after purchase and delivered via email
Your guarantee:
30-day money-back guarantee — no questions asked
Who trusts this:
Trusted by professionals in 160+ countries
Adding to cart… The item has been added

This curriculum spans the design and operationalization of master data management within complex business process redesign initiatives, comparable in scope to a multi-phase advisory engagement addressing governance, integration, and change management across global business units and hybrid system landscapes.

Module 1: Assessing Current-State Data Governance and Process Maturity

  • Conduct stakeholder interviews to map existing data ownership models and identify conflicting authority across business units.
  • Document data lineage for critical business processes to reveal undocumented dependencies and shadow data sources.
  • Perform gap analysis between current data quality metrics and operational SLAs for customer, product, and supplier domains.
  • Identify legacy system interfaces that bypass central data governance controls and assess technical debt implications.
  • Evaluate the impact of inconsistent master data on regulatory reporting accuracy in multi-jurisdiction operations.
  • Classify business processes by data sensitivity and regulatory exposure to prioritize redesign efforts.

Module 2: Defining Master Data Domains and Ownership Models

  • Select canonical data models for core domains (e.g., customer, material, location) based on enterprise integration requirements.
  • Negotiate stewardship responsibilities between functional leads and IT to formalize escalation paths for data disputes.
  • Define golden record rules for entity resolution, including survivorship logic for conflicting attribute values.
  • Establish data domain boundaries to prevent overlap in responsibility between product and supplier data teams.
  • Implement role-based access controls for data creation, modification, and deactivation workflows.
  • Determine fallback procedures for stewardship coverage during organizational transitions or vacancies.

Module 3: Integrating MDM with Process Reengineering Workflows

  • Redesign onboarding processes to embed MDM validation steps before new customer records enter downstream systems.
  • Modify procurement workflows to require supplier master data certification prior to contract initiation.
  • Integrate MDM match/mismatch alerts into order-to-cash exception handling procedures.
  • Align product lifecycle management stages with MDM publication states (e.g., draft, approved, retired).
  • Replace manual data reconciliation tasks in month-end close with automated MDM-driven validations.
  • Adjust service dispatch processes to use standardized location master data for routing and compliance.

Module 4: Designing Data Quality and Stewardship Operations

  • Configure automated data quality monitors to detect anomalies in critical fields like tax IDs or addresses.
  • Implement steward workbenches with prioritized queues based on business impact severity scoring.
  • Define SLAs for steward response times to data correction requests from operational teams.
  • Establish data quality dashboards accessible to process owners with drill-down to root cause analysis.
  • Develop standard operating procedures for handling duplicate record merges with audit trail requirements.
  • Introduce data quality gates in integration pipelines to block propagation of non-conforming records.

Module 5: Managing Change Across Organizational and System Boundaries

  • Coordinate cutover plans for MDM rollout with business process owners to minimize disruption during peak cycles.
  • Negotiate data model compromises between regional subsidiaries and global headquarters for multinational consistency.
  • Develop data migration validation scripts to verify referential integrity post-system conversion.
  • Implement phased data synchronization to legacy systems where real-time integration is technically unfeasible.
  • Address resistance from power users who rely on local data overrides by redesigning exception workflows.
  • Document fallback data sources and manual processes to be decommissioned post-MDM adoption.

Module 6: Enforcing Governance in Hybrid and Multi-System Landscapes

  • Define data synchronization protocols for systems-of-record versus systems-of-reference in decentralized environments.
  • Configure event-driven notifications for critical data changes to trigger downstream process adjustments.
  • Implement data versioning and audit trails to support compliance audits and rollback requirements.
  • Enforce data standardization at integration points using canonical message formats and transformation rules.
  • Monitor data drift across replicated systems and schedule reconciliation jobs based on transaction volume.
  • Apply metadata tagging to distinguish official master data from derived or temporary datasets.

Module 7: Measuring Business Impact and Sustaining Improvements

  • Track reduction in manual data correction hours across finance, supply chain, and customer service functions.
  • Measure improvement in first-pass yield of cross-system transactions after MDM implementation.
  • Quantify decrease in regulatory findings related to inaccurate customer or product reporting.
  • Monitor adoption rates of MDM-enriched data in analytics and decision support tools.
  • Conduct periodic stewardship reviews to recalibrate ownership and resolve emerging data conflicts.
  • Update business process documentation to reflect new data dependencies and control points.
!