CDMP-RMD Exam Dumps : Reference And Master Data Management Exam

A global identifier is used to link multiple references to the same entity across different systems or datasets. Here’s why:

Purpose of Global Identifier:

Unique Identification: Provides a unique identifier that can be used to recognize the same entity across disparate systems and datasets.

Consistency: Ensures that different references or records pointing to the same entity are consistently identified and managed.

Linking Equivalent References:

Equivalent References: Global identifiers link references that are equivalent, meaning they represent the same real-world entity even if the data is stored differently in various systems.

Entity Resolution: Helps in resolving different records to a single entity, ensuring data consistency and accuracy.

Example:

Customer Records: A customer might be listed in different systems (CRM, billing, support) with slightly different details. A global identifier links these records to recognize them as the same customer.

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)"

Data profiling is a crucial activity in mapping source system data for MDM efforts. Data profiling involves analyzing data from source systems to understand its structure, content, and quality. Key steps include:

Data Assessment: Evaluating the data to identify patterns, inconsistencies, and anomalies.

Data Quality Analysis: Measuring the quality of data in terms of accuracy, completeness, consistency, and uniqueness.

Metadata Extraction: Extracting metadata to understand data definitions, formats, and relationships.

Data Cleansing: Identifying and correcting data quality issues to ensure that the data is suitable for integration into the MDM system.

By performing data profiling, organizations can gain insights into the current state of their data, identify potential issues, and develop strategies for data integration and quality improvement.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

"Data Quality: The Accuracy Dimension" by Jack E. Olson.

Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.

JSON Mapping:

JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.

ETL Toolset:

ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.

Columnar Database:

Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.

Complex Queries:

While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.

Data Virtualization:

Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.

It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.