TDWI Master Data Management Fundamentals Training

Top-performing businesses need high-quality, low-redundancy reference data. You can’t manage a supply chain with disparate and unreliable product and customer data, service your customers effectively with inconsistent customer views, or confidently report to stockholders when financial data is in disarray. Master data is important because it is used by many groups and processes throughout the enterprise. It is challenging because it is collected–often redundantly and inconsistently–by many groups and processes.

Master data management (MDM) is the process of collecting, consolidating, quality assuring, and distributing master data. MDM tools are abundant and diverse, but technology alone can’t solve the problem. The nature of identification, matching, consolidation, conflict resolution, and hierarchy management makes MDM complex and challenging. The right knowledge is an essential element of MDM success.

There are no prerequisites for this course.

MDM program managers, project managers, architects, and implementers; BI program managers, project managers, architects, and implementers; data warehousing program and project managers, architects, and implementers; data and technology architects; data quality professionals.

• The what and why of MDM
• Architectural options for MDM: repository, registry, engine, and broker
• Identity management issues and techniques
• Hierarchy management issues and techniques
• MDM considerations for global and multinational businesses
• The human and organizational aspects of MDM
• The role of data governance in MDM
• Relationships of MDM with BI and data warehousing

Module One : MDM Concepts

• Defining MDM
• Master data
• Master data management
• Customer data integration (CDI)
• Product information management (PIM)
• other domain specifics …

The Need for MDM 

• business view
• information systems view

The Basics of MDM

• Enterprise view of reference entities
• Enterprise view of relationships among things
• It isn’t about “one right answer” or “single version of the truth”

Dimensions of MDM

• People (organizations)
• Processes (governance, business, metadata, etc.)
• Data (and metadata)
• architecture
• technology
• programs & projects

MDM Prerequisites

• Technical infrastructure
• Collaborative culture
• Organizational readiness
• Buy-in and data sharing culture
• Data quality
• Data governance

Module 2: MDM Processes and Architectures

MDM Processes

• Identity management
• Hierarchy management
• Data integration
• Application integration

MDM Architectures

• Repository
• Registry
• Repository-Registry Hybrid
• Engine
• Broker
• Something here about repository vs. registry and about services vs.
• transactions

Module Three: Identity Management

Identity management defined

• Uniqueness of things

Identity management functions

• Search and Resolution
• Record Linkage
• Merge & Consolidation
• Grouping and Splitting

Identity Management in MDM Architecture

• Repository
• Registry
• Engine
• Broker

Module Four: Hierarchy Management

Hierarchy management defined

• Relationships of things
• Multiple hierarchies (contrast to entity uniqueness)

Hierarchy management functions

• Identification and matching
• Consolidation
• Record linkage
• Versioning

Hierarchy Management in MDM Architecture

• Repository
• Registry
• Engine
• Broker

Module Five: Implementing and Operating MDM

Roles and Responsibilities

• Stakeholders
• Master data creators/providers
• Master data consumers
• program management
• MDM development
• MDM operations
• Data governance

Data Governance for MDM

• Ownership
• Stewardship
• Custodianship

Data Quality and MDM

• Data profiling
• Data quality assessment
• Data cleansing
• Process improvement

Data modeling and MDM

• Exchange data model
• Persistence data model

The MDM Program

• Business case
• Program management
• Change management

MDM Projects

• Implementation within a program framework
• Kinds of projects

Architecture and infrastructure

Master data assessment

Data migration

Application migration

• Project guidelines

MDM Operations

• consolidation
• synchronization
• data quality management
• administration and configuration

Sustaining and Maturing MDM

• Feedback
• Evolving services
• data and metadata management
• data governance
• maturity models

Module Six: Summary and Conclusion

• Summary of Key Points
• References & Resources