Data Governance and Master Data Management (MDM)

In today’s data-driven world, organizations depend on clean, consistent, and well-governed data to support decision-making, operations, and digital transformation. This course explores the policies, structures, tools, and best practices for managing enterprise data assets. Participants will learn to establish governance frameworks, define data ownership, and implement MDM systems to unify key data domains like customers, products, employees, and locations.

Who Should Attend

Data governance officers and data stewards

IT managers and enterprise data architects

BI and analytics professionals

Compliance and data privacy leaders

ERP and CRM project leaders

Business process owners managing core data domains

Objectives

Understand the principles and value of data governance and MDM

Establish governance roles, data stewardship, and accountability

Design and implement MDM strategies for core data domains

Apply standards for data quality, integration, and lifecycle control

Ensure regulatory compliance (GDPR, HIPAA, etc.) in data handling

Use tools and metrics to monitor data governance performance

Course Outline

Foundations of Data Governance

What is data governance? Scope and impact
Key drivers: data quality, risk, compliance, value
Data governance frameworks (DAMA-DMBOK, DCAM)
Principles: ownership, accountability, stewardship
Aligning data governance with organizational strategy

Governance Structures, Roles, and Policies

Data governance councils and operating models
Defining roles: owners, stewards, custodians
Creating data policies, standards, and glossaries
Metadata management and business definitions
Change management for data initiatives

Master Data Management (MDM) Fundamentals

What is master data? Key domains and challenges
MDM architectures: registry, consolidation, coexistence, centralized
Data modeling for master entities
Data synchronization and system integration
Golden record creation and survivorship rules

Data Quality and Lifecycle Control

Dimensions of data quality: completeness, accuracy, consistency
Profiling and cleansing tools and techniques
Lifecycle management: creation, usage, archival, disposal
Data validation rules and automation
Using metrics and dashboards to monitor quality

Compliance, Tools, and Implementation Strategy

Regulatory compliance: GDPR, HIPAA, CCPA, etc.
Data security and privacy in MDM
Toolsets for governance and MDM (e.g., Informatica, Talend, SAP MDG)
Phased implementation roadmap and maturity models
Building a sustainable data governance culture

Competencies

Data ownership and governance policy design

Master data modeling and architecture planning

Data quality assurance and lifecycle management

Regulatory data compliance and audit readiness

MDM tool evaluation and implementation

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