Introduction

Master Data Management (MDM) refers to the processes, governance, and technology used to create and maintain a single, accurate view of an organization’s critical data assets, also known as master data. This data includes entities such as customers, products, suppliers, and locations. Effective MDM practices ensure that data is consistent, accurate, and available across the organization to support decision-making, business operations, and analytics.

This training course provides an in-depth exploration of MDM principles, strategies, and tools for managing data across various systems and applications. Participants will learn how to implement an MDM strategy, select the right technologies, and ensure data quality and governance to enable a unified view of master data.

Objectives

By the end of this course, participants will:

• Understand the fundamental concepts and importance of Master Data Management.
• Learn how to define and identify master data and its role in an organization.
• Explore MDM frameworks, methodologies, and best practices.
• Gain hands-on experience with MDM tools and platforms.
• Understand data governance, data quality, and data security in the context of MDM.
• Implement and maintain an MDM solution tailored to organizational needs.
• Explore integration strategies for master data across disparate systems and applications.
• Address real-world challenges in MDM implementation, including data stewardship and change management.

Who Should Attend?

This course is ideal for:

• Data architects, data engineers, and data analysts responsible for implementing MDM solutions.
• IT professionals, software engineers, and system administrators involved in data integration and architecture.
• Business intelligence, data quality, and data governance specialists.
• C-suite executives and managers seeking to leverage MDM for improved decision-making and operational efficiency.
• Consultants and project managers working on data management initiatives.

Day 1: Introduction to Master Data Management (MDM)

Morning Session: Understanding Master Data and Its Importance

• What is Master Data? Definition, scope, and key concepts.
• Types of master data: Customer, product, supplier, and location data.
• The role of master data in business operations, analytics, and decision-making.
• Benefits of MDM: Improved data quality, business agility, operational efficiency, and regulatory compliance.
• Real-world examples of MDM implementations across industries (e.g., finance, retail, healthcare).
• Hands-on: Identify examples of master data within your organization.

Afternoon Session: MDM Frameworks and Methodologies

• Overview of MDM frameworks and methodologies.
• Centralized vs. decentralized vs. hybrid MDM models.
• MDM lifecycle: Data modeling, data governance, data stewardship, and data integration.
• Key MDM processes: Data consolidation, data harmonization, and data synchronization.
• Best practices for MDM strategy development and implementation.
• Hands-on: Discuss MDM frameworks and select the right model for different business needs.

Day 2: Data Governance and Data Quality in MDM

Morning Session: Data Governance in MDM

• What is data governance, and why is it critical for MDM?
• Defining roles and responsibilities in data governance: Data stewards, data owners, and data custodians.
• Data governance frameworks: DAMA, DCAM, and TOGAF.
• Data policies, standards, and procedures for data quality and consistency.
• Monitoring and enforcing governance rules in MDM systems.
• Hands-on: Create a basic data governance policy and define roles for an MDM initiative.

Afternoon Session: Ensuring Data Quality in MDM

• The importance of data quality in MDM: Accuracy, completeness, consistency, timeliness, and reliability.
• Techniques for ensuring data quality in master data: Data profiling, cleansing, and enrichment.
• Implementing data validation rules and data quality metrics.
• Tools for data quality management in MDM systems (e.g., Informatica, Talend, SAP).
• Hands-on: Perform a data quality assessment for master data in your organization.

Day 3: MDM Technologies and Tools

Morning Session: Introduction to MDM Tools and Platforms

• Overview of MDM technologies and platforms: Informatica MDM, SAP Master Data Governance, Oracle MDM, Microsoft MDS, and open-source MDM solutions.
• Architecture and components of an MDM platform: Hub, registry, and workflow.
• How to choose the right MDM platform based on business needs and IT infrastructure.
• Key features to consider in an MDM tool: Data integration, scalability, security, and reporting.
• Hands-on: Explore and evaluate an MDM platform (e.g., Informatica or SAP MDM).

Afternoon Session: Implementing an MDM Solution

• Steps for implementing an MDM solution: Requirements gathering, platform selection, data integration, and deployment.
• Designing an MDM data model: Key attributes, relationships, and hierarchy.
• Data synchronization and integration with enterprise applications (CRM, ERP, etc.).
• Data migration strategies for MDM implementation.
• Hands-on: Design an MDM data model for a specific use case (e.g., customer data management).

Day 4: MDM Integration, Security, and Change Management

Morning Session: MDM Integration Strategies

• Integrating master data across multiple systems (CRM, ERP, data warehouses).
• Real-time vs. batch data integration approaches in MDM.
• Data synchronization and conflict resolution strategies.
• Using APIs, middleware, and ESB (Enterprise Service Bus) for MDM integration.
• Hands-on: Implement data integration between two systems using an MDM platform.

Afternoon Session: Security and Change Management in MDM

• Securing master data: Authentication, authorization, and encryption.
• Data privacy regulations (GDPR, CCPA) and their impact on MDM solutions.
• Managing change and version control in master data management.
• Strategies for change management and user adoption during MDM implementation.
• Hands-on: Implement security and access controls for an MDM platform.

Day 5: Advanced Topics and Real-World MDM Challenges

Morning Session: Advanced MDM Topics

• Multidomain MDM: Managing multiple domains of master data (e.g., product, customer, supplier) in one system.
• Customer Data Integration (CDI): Merging customer data from various systems and touchpoints.
• Product Information Management (PIM): Managing and syndicating product data across channels.
• Data governance in a cloud-based MDM environment.
• Using machine learning and AI to enhance MDM and data quality.
• Hands-on: Explore CDI or PIM strategies for a business scenario.

Afternoon Session: Real-World MDM Challenges and Case Studies

• Common challenges in MDM implementation: Data silos, data quality issues, and stakeholder alignment.
• Change management and user training strategies for MDM adoption.
• Case studies of MDM failures and successes in various industries.
• Lessons learned and best practices for overcoming MDM implementation challenges.
• Final hands-on: Design an end-to-end MDM solution for a real-world business scenario.

Materials and Tools:

• MDM Tools: Informatica MDM, SAP Master Data Governance, Oracle MDM, Microsoft MDS, Talend.
• Key resources: DAMA and TOGAF frameworks, MDM Best Practices.
• Datasets: Example datasets for customer, product, and supplier data management.
• Recommended reading: “Master Data Management and Data Governance” by Alex Berson and Lynn Dunn.