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

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.

The ISO definition of Master Data quality focuses on the degree to which the data's characteristics meet the requirements of individual users. This implies that quality is subjective and depends on whether the data is suitable and adequate for its intended purpose, fulfilling the specific needs of its users.

References:

ISO 8000-8:2015 - Data quality — Part 8: Information and data quality: Concepts and measuring.

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 13: Data Quality Management.

Planned control activities are not considered master data. Here’s why:

Master Data Examples:

Categories and Lists: Master data typically includes lists and categorizations that are used repeatedly across multiple business processes and systems.

Examples: Product categories, account codes, country codes, and currency codes, which are relatively stable and broadly used.

Planned Control Activities:

Process-Specific: Planned control activities pertain to specific actions and checks within business processes, often linked to operational or transactional data.

Not Repeated Data: They are not reused or referenced as a stable entity across different systems.

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)"