Master Data Management

Training course

Overview

Master Data Management (MDM) is a comprehensive professional training course designed to equip participants with the knowledge and practical skills required to establish, manage, govern, and continuously improve critical master data across an organization. The course explores how organizations can create reliable, consistent, complete, and trusted master records for customers, products, suppliers, employees, locations, assets, and other core business entities. Participants will develop a strong understanding of how master data supports operational efficiency, reporting, analytics, digital transformation, regulatory compliance, customer experience, and strategic decision-making.

This practical Master Data Management training course covers the complete MDM lifecycle, from identifying critical master data domains and defining business requirements to data modeling, data standards, governance, matching, merging, cleansing, enrichment, synchronization, and ongoing monitoring. Participants will examine important concepts including golden records, single source of truth, reference data, metadata, data ownership, stewardship, data lineage, data quality, business rules, hierarchies, survivorship rules, and master data integration. The course also introduces relevant governance and data management practices that support scalable enterprise MDM programs.

The training emphasizes practical application through real-world scenarios, case studies, exercises, data-quality investigations, master record design activities, matching and deduplication exercises, governance simulations, workflow design, and MDM implementation planning. Participants will learn how to identify duplicate and conflicting records, define authoritative sources, establish data standards, develop stewardship workflows, create quality controls, design master data processes, and coordinate data synchronization across multiple systems. Practical tools such as data dictionaries, RACI matrices, profiling techniques, matching rules, survivorship rules, validation checklists, issue registers, dashboards, and governance frameworks are incorporated throughout the course.

By the end of the Master Data Management course, participants will be able to contribute effectively to the design, implementation, governance, and continuous improvement of enterprise master data environments. They will understand how to establish trusted master records, improve data consistency across systems, manage data quality risks, define governance responsibilities, and support sustainable MDM operating models. The course is suitable for organizations seeking to reduce duplicate records, improve data accuracy, strengthen business processes, establish trusted sources of information, and create a reliable foundation for analytics, automation, reporting, and enterprise decision-making.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Master Data Management professionals and data management specialists

• Data governance professionals, data stewards, and data owners

• Data quality analysts and information management professionals

• Business analysts and business intelligence professionals

• Data architects and enterprise architects

• Database administrators and information systems professionals

• IT managers and application integration specialists

• Data analysts and reporting professionals

• Professionals responsible for customer, product, supplier, employee, asset, or location data

• Finance, procurement, sales, marketing, supply chain, human resources, and operations professionals

• Risk, compliance, audit, and internal control professionals involved in data governance

• Managers and team leaders responsible for enterprise data quality and information processes

Course Objectives

By the end of the training, participants will be able to:

• Explain the principles, purpose, scope, and business value of Master Data Management

• Distinguish master data, transactional data, reference data, metadata, and analytical data

• Identify critical master data domains and their relationships with business processes

• Assess the quality, completeness, consistency, uniqueness, and reliability of master data

• Define master data requirements, standards, business rules, and quality criteria

• Understand golden records, single sources of truth, authoritative sources, and survivorship concepts

• Design practical master data governance structures, roles, responsibilities, and decision rights

• Apply data profiling, matching, deduplication, cleansing, standardization, and enrichment techniques

• Develop data models, hierarchies, taxonomies, identifiers, and classification structures for master data

• Establish validation, approval, stewardship, workflow, and change-management processes

• Manage master data across multiple applications, databases, departments, and business units

• Understand master data integration, synchronization, APIs, ETL processes, and system interoperability

• Apply data lineage, metadata management, traceability, and auditability principles

• Develop master data quality metrics, KPIs, dashboards, and monitoring processes

• Identify and manage master data risks, exceptions, conflicts, and remediation activities

• Develop a practical Master Data Management implementation roadmap and continuous improvement strategy

Course Content

Day 1: Foundations of Master Data Management

Module 1: Master Data Concepts, Business Value, and Data Domains

Topics

  1. Introduction to Master Data Management
  2. The Strategic and Operational Importance of Master Data
  3. Master Data, Transactional Data, Reference Data, Metadata, and Analytical Data
  4. Identifying Core Master Data Domains: Customer, Product, Supplier, Employee, Location, Asset, and Others
  5. Master Data Lifecycle from Creation and Maintenance to Archiving
  6. Master Data Quality Dimensions: Accuracy, Completeness, Consistency, Validity, Uniqueness, and Timeliness
  7. Single Source of Truth, Golden Records, and Authoritative Data Sources
  8. Common Master Data Problems, Duplicates, Conflicts, Inconsistencies, and Fragmentation
  9. Master Data Requirements, Business Rules, Standards, and Data Definitions
  10. Practical Exercise: Identifying Critical Master Data Domains and Quality Risks in an Organization

Day 2: Master Data Governance, Modeling, and Standards

Module 2: Governance, Data Architecture, and Master Data Design

Topics

  1. Master Data Governance Principles and Operating Models
  2. Data Ownership, Stewardship, Custodianship, Accountability, and RACI Frameworks
  3. Designing Master Data Governance Committees, Roles, and Decision Rights
  4. Master Data Policies, Standards, Procedures, and Business Rules
  5. Data Dictionaries, Metadata, Business Glossaries, and Common Definitions
  6. Master Data Modeling, Entities, Attributes, Relationships, and Identifiers
  7. Hierarchies, Taxonomies, Classifications, and Organizational Structures
  8. Reference Data Management and Standard Code Sets
  9. Data Lineage, Traceability, Auditability, and Master Data Change History
  10. Case Study: Designing a Governance and Master Data Model for a Multi-Department Organization

Day 3: Master Data Quality, Matching, and Remediation

Module 3: Data Quality Management and Master Record Consolidation

Topics

  1. Master Data Profiling and Quality Assessment
  2. Identifying Duplicate, Incomplete, Invalid, Conflicting, and Outdated Master Records
  3. Data Standardization and Normalization Techniques
  4. Record Matching, Similarity Analysis, and Duplicate Detection
  5. Deterministic and Probabilistic Matching Concepts
  6. Record Merging, Survivorship Rules, and Golden Record Creation
  7. Data Cleansing, Enrichment, Validation, and Remediation
  8. Exception Management, Data Quality Issues, and Stewardship Workflows
  9. Master Data Quality KPIs, Thresholds, Dashboards, and Monitoring
  10. Practical Exercise: Profiling, Matching, Deduplicating, and Creating Trusted Master Records

Day 4: Master Data Integration, Technology, and Operational Management

Module 4: Enterprise Integration, Synchronization, and MDM Technology

Topics

  1. Master Data Integration Across Applications, Databases, and Business Units
  2. Source Systems, Target Systems, Systems of Record, and Authoritative Sources
  3. Data Synchronization, Replication, and Distribution Models
  4. ETL, ELT, APIs, Integration Platforms, and Data Pipelines for Master Data
  5. Centralized, Registry, Consolidation, Coexistence, and Hybrid MDM Architectures
  6. Master Data Workflows, Approvals, Change Requests, and Stewardship Processes
  7. Master Data Security, Access Control, Privacy, and Data Protection Considerations
  8. Monitoring Master Data Integration, Exceptions, Failures, and Reconciliation
  9. Evaluating MDM Platforms, Automation, Cloud Data Environments, and Technology Requirements
  10. Practical Simulation: Designing an End-to-End Master Data Synchronization and Governance Process

Day 5: Advanced Master Data Strategy, Implementation, and Continuous Improvement

Module 5: Enterprise MDM Strategy, Maturity, and Sustainable Data Management

Topics

  1. Developing an Enterprise Master Data Management Strategy
  2. Aligning MDM with Business Strategy, Data Governance, Digital Transformation, and Analytics
  3. Identifying Critical Master Data Elements and Prioritizing MDM Initiatives
  4. Developing MDM KPIs, Quality Scorecards, Performance Dashboards, and Management Reports
  5. Master Data Risk Management, Compliance, Audit, and Internal Controls
  6. MDM Maturity Assessment, Capability Models, and Benchmarking
  7. Building Master Data Stewardship, Accountability, and Organizational Data Culture
  8. Developing MDM Implementation Roadmaps, Business Cases, Resources, and Change Plans
  9. Capstone Exercise: Designing a Complete Enterprise Master Data Management Framework and Implementation Roadmap
  10. Final Case Study, Practical Assessment, Lessons Learned, and Sustainable MDM Improvement Strategy

 

Course Schedules:

Dates Fees Location Apply