CDMP-RMD Exam Dumps - DAMA CDMP Questions and Answers

MDM programs often face challenges and can fail due to a combination of factors. Here’s a detailed explanation:

Emphasis on Technology:

Technology-Centric Approach: Overemphasis on technology solutions without addressing people and process components can lead to failure. Successful MDM programs require balanced attention to technology, people, and processes.

Positioning within IT:

IT Focus: Poor positioning of the MDM program within the IT organization can lead to it being seen as a purely technical initiative, missing the necessary business alignment and support.

Business Commitment and Engagement:

Lack of Engagement: Insufficient commitment and engagement from the business side can result in inadequate support, resources, and buy-in, leading to failure.

Program vs. Project:

Long-Term Perspective: Treating MDM as a one-time project rather than an ongoing program can limit its effectiveness. MDM requires continuous improvement and adaptation to evolving business needs.

References:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

Key processing steps for successful MDM typically include:

Data Acquisition: The process of gathering and importing data from various sources.

Data Sharing & Stewardship: Involves ensuring data is shared appropriately across the organization and that data stewards manage data quality and integrity.

Entity Resolution: Identifying and linking data records that refer to the same entity across different data sources.

Data Model Management: Creating and maintaining data models that define how data is structured and related within the MDM system.

Excluded Step - Data Indexing: While indexing is a critical database performance optimization technique, it is not a primary processing step specific to MDM. MDM focuses on consolidating, managing, and ensuring the quality of master data rather than indexing, which is more about search optimization within databases.

References:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

A Canonical Data Model (CDM) is a design pattern used in data integration and exchange to create a standardized format for data that will be shared across different systems.

Canonical Data Model Definition:

A CDM provides a common, consistent, and agreed-upon representation of data across different systems within an organization.

It serves as an intermediary format to simplify data exchange and integration.

Standardization:

The primary purpose of a CDM is to standardize the format of data. This ensures that data from different sources can be integrated, understood, and used without needing extensive transformation or reformatting.

It addresses the problem of data heterogeneity, where different systems may have their own data formats and structures.

Data Integration:

By using a CDM, organizations can facilitate easier and more efficient data integration processes. Data from various systems can be mapped to the canonical model, enabling seamless data exchange and interoperability.

It simplifies the maintenance and management of data integration solutions by providing a single, unified data model.

Use Cases:

CDMs are commonly used in enterprise application integration (EAI), service-oriented architectures (SOA), and other environments where data needs to be exchanged between multiple systems.

In the context of the Corporate Information Factory, understanding transactions involves integrating various types of data to get a comprehensive view. Master Data (core business entities), Reference Data (standardized information), and External Data (information sourced from outside the organization) are essential for providing context and enriching transactional data.

References:

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 3: Data Architecture and Chapter 11: Reference and Master Data Management.

"Building the Data Warehouse" by W.H. Inmon, which introduces the Corporate Information Factory concept.