
Master Data Management (MDM) Training Course
Master Data Management is the backbone of reliable enterprise data, and this course gives you the end-to-end expertise to design, govern, and scale it. From data modelling and duplicate matching to governance frameworks and cloud-native integration, every critical MDM skill is covered. Whether you're stepping into an MDM role or leading a programme, this training delivers the practical knowledge you need to make an immediate impact.
What you will learn:
This course explores the full Master Data Management (MDM) lifecycle, beginning with core concepts such as golden records, survivorship rules, and data domains. You’ll learn to design and assess MDM architecture styles, build party and product data models, and apply data‑quality rules and scorecards. The curriculum also covers matching algorithms, merge strategies, and governance frameworks that ensure data accuracy over time. Hands‑on labs teach MDM integration patterns, API design, and event‑driven distribution. Advanced topics include cloud‑native MDM platforms, AI‑assisted matching, industry‑specific applications, and privacy controls. By course end you’ll be able to evaluate MDM maturity, craft a business case, and lead a phased MDM programme from strategy to execution.
How you study in practice Master Data Management (MDM) Training Course
How you practise Master Data Management (MDM) Training Course
For companies looking to train their teams
With Dedika for businesses, the course includes exercises and examples tailored to your company and its specific needs.
Course content
8 Chapters • 40 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsFoundations of Master Data Management
Foundations of Master Data Management
Lesson 1 • Core MDM Concepts and Terminology
Introduces golden records, survivorship, stewardship, and data domains. Provides shared vocabulary used throughout the entire course.
Lesson 2 • MDM Lifecycle Overview
Maps the end-to-end MDM lifecycle from data ingestion to distribution. Shows students how later course chapters fit into this lifecycle.
Lesson 3 • MDM Stakeholders and Organisational Roles
Identifies key roles—data owners, stewards, architects, and consumers—and their responsibilities. Prepares students to navigate MDM governance structures.
Lesson 4 • Business Drivers for MDM
Examines why organisations invest in MDM, linking data quality problems to measurable business pain. Motivates the need for structured MDM programmes.
Lesson 5 • What Is Master Data
Defines master data and distinguishes it from transactional, reference, and metadata types. Anchors the chapter by establishing the core subject of MDM.
Chapter 2HideHide detailsSee detailsMDM Architecture and Implementation Styles
MDM Architecture and Implementation Styles
Lesson 1 • Registry Style MDM
Explains how registry-style MDM indexes master data without centralising storage. Introduces the lightest-weight implementation pattern as a baseline.
Lesson 2 • Centralised (Transaction) Style MDM
Examines the hub as the authoritative system of record for all master data writes. Represents the most rigorous implementation pattern in the chapter.
Lesson 3 • Selecting the Right MDM Style
Provides a decision framework for matching implementation style to business maturity and goals. Synthesises all four styles into a comparative evaluation model.
Lesson 4 • Consolidation Style MDM
Describes how consolidation MDM aggregates data into a read-only hub for analytics. Builds on registry concepts by adding a physical master store.
Lesson 5 • Coexistence Style MDM
Covers bidirectional synchronisation between the MDM hub and source systems. Extends consolidation by introducing write-back and conflict resolution.
Chapter 3HideHide detailsSee detailsData Modelling for Master Data
Data Modelling for Master Data
Lesson 1 • Hierarchies and Relationships in MDM
Explains how to model organisational, product, and geographic hierarchies. Addresses recursive relationships and multi-parent hierarchy challenges.
Lesson 2 • Reference Data Modelling
Distinguishes reference data from master data and models code lists and lookup tables. Prepares students to manage reference data as a supporting domain.
Lesson 3 • Modelling the Party Domain
Designs customer and supplier master data models using the party pattern. Demonstrates how a single model handles multiple entity subtypes.
Lesson 4 • Modelling the Product Domain
Covers product master data structures including classifications, attributes, and variants. Connects product modelling to catalog and supply chain use cases.
Lesson 5 • MDM Data Modelling Fundamentals
Reviews entity-relationship modelling concepts as applied to master data. Establishes the modelling vocabulary used throughout this chapter.
Chapter 4HideHide detailsSee detailsData Quality Management in MDM
Data Quality Management in MDM
Lesson 1 • Data Quality Scorecards and KPIs
Designs scorecards that aggregate quality metrics into actionable dashboards. Connects quality measurement to stewardship workflows and executive reporting.
Lesson 2 • Data Profiling Techniques
Covers column, structure, and relationship profiling to expose data quality issues. Profiling results feed directly into rule design in subsequent sections.
Lesson 3 • Data Remediation and Cleansing Workflows
Builds automated and manual remediation workflows to correct quality failures. Closes the quality loop by feeding corrected data back into the MDM hub.
Lesson 4 • Data Quality Dimensions
Introduces the six core data quality dimensions: accuracy, completeness, consistency, timeliness, uniqueness, and validity. Provides the measurement framework for the chapter.
Lesson 5 • Defining and Implementing Quality Rules
Teaches how to translate business requirements into executable data quality rules. Rules defined here drive the automated validation pipelines in MDM hubs.
Chapter 5HideHide detailsSee detailsMatching, Merging, and Survivorship
Matching, Merging, and Survivorship
Lesson 1 • Duplicate Detection Fundamentals
Explains why duplicates arise and the business impact of unresolved duplicates. Sets the problem context before introducing technical matching solutions.
Lesson 2 • Matching Algorithms and Techniques
Covers deterministic, probabilistic, and machine-learning-based matching methods. Students select and tune algorithms appropriate to their data characteristics.
Lesson 3 • Survivorship Rules and Golden Record Creation
Designs survivorship policies that select the best attribute values across merged records. Produces the authoritative golden record that downstream systems consume.
Lesson 4 • Match Threshold Configuration
Teaches how to set and tune match score thresholds to balance precision and recall. Threshold decisions directly affect golden record quality and steward workload.
Lesson 5 • Merge Rules and Link Management
Defines how matched records are linked or merged and how links are managed over time. Introduces unmerge and re-link operations for error correction.
Chapter 6HideHide detailsSee detailsMDM Governance and Data Stewardship
MDM Governance and Data Stewardship
Lesson 1 • Data Policies and Standards Management
Covers authoring, publishing, and enforcing data policies and naming standards. Policies defined here are enforced by quality rules and stewardship workflows.
Lesson 2 • Data Stewardship Models
Compares centralised, federated, and hybrid stewardship operating models. Students select the model that fits their organisational structure and MDM style.
Lesson 3 • MDM Governance Framework Design
Defines the components of an MDM governance framework including policies, standards, and accountability. Provides the structural foundation for all stewardship activities.
Lesson 4 • Governance Metrics and Reporting
Designs governance dashboards that track stewardship performance and policy compliance. Reporting connects governance activities to executive-level business outcomes.
Lesson 5 • Stewardship Workflow Design
Builds task-based stewardship workflows for exception handling, approval, and enrichment. Workflows operationalise governance policies into daily steward activities.
Chapter 7HideHide detailsSee detailsMDM Integration and Data Distribution
MDM Integration and Data Distribution
Lesson 1 • Event-Driven Master Data Distribution
Implements event streaming to propagate golden record changes to downstream systems in near real time. Extends API distribution with asynchronous publish-subscribe patterns.
Lesson 2 • MDM Integration Architecture Patterns
Surveys batch, real-time, and event-driven integration patterns for MDM hubs. Establishes the integration vocabulary and pattern catalog for the chapter.
Lesson 3 • Data Lineage and Integration Monitoring
Tracks data flow from source to golden record to consumer using lineage tools. Monitoring ensures integration pipelines meet SLAs and data arrives with quality intact.
Lesson 4 • Source System Onboarding
Covers the process of connecting a new source system to the MDM hub for ingestion. Includes mapping, transformation, and initial load procedures.
Lesson 5 • API Design for MDM Data Access
Designs RESTful and event-based APIs that expose golden records to consuming applications. APIs are the primary distribution channel in modern MDM architectures.
Chapter 8HideHide detailsSee detailsMDM Programme Strategy and Roadmap
MDM Programme Strategy and Roadmap
Lesson 1 • Change Management for MDM Programmes
Applies change management principles to drive adoption of MDM processes and tools. Addresses resistance, training, and communication strategies for sustained success.
Lesson 2 • Vendor and Technology Selection
Provides a structured process for evaluating and selecting MDM platform vendors. Covers RFP design, proof-of-concept criteria, and total cost of ownership.
Lesson 3 • MDM Roadmap and Phasing
Designs a phased MDM roadmap that sequences domains, capabilities, and integrations. Phasing balances quick wins with long-term architectural goals.
Lesson 4 • Building the MDM Business Case
Quantifies MDM benefits and costs to construct a compelling executive business case. Connects data quality improvements to revenue, cost, and risk metrics.
Lesson 5 • MDM Maturity Assessment
Applies a maturity model to evaluate an organisation's current MDM capabilities. Assessment results drive the prioritisation decisions in the roadmap section.
Your valid completion certificate
This course is for you:
Data analysts who want to move into enterprise data management roles.
IT architects seeking structured knowledge of MDM hub design patterns.
Business intelligence professionals frustrated by inconsistent, unreliable source data.
Data governance officers who need to formalise stewardship processes and policies.
ERP or CRM consultants who regularly encounter master data quality problems.
Career changers from database administration aiming to specialise in MDM.
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