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

ISO 22745 is an international standard for defining and exchanging master data.

ISO 22745:

This standard specifies the requirements for the exchange of master data, particularly in industrial and manufacturing contexts.

It includes guidelines for the structured exchange of information, ensuring that data can be shared and understood across different systems and organizations.

Standards for Master Data:

Standards like ISO 22745 help ensure consistency, interoperability, and data quality across different platforms and entities.

They provide a common framework for defining and exchanging master data, facilitating smoother data integration and management processes.

Other Options:

ETL:Refers to the process of Extract, Transform, Load, used in data integration but not a standard for defining master data.

Corporation-specific Methods:Many organizations may have their own methods, but standardized frameworks like ISO 22745 provide a common foundation.

No Standards:While not all organizations use master data, standards do exist for those that do.

Master data refers to the critical data that is essential to the operations of a business. It typically includes entities such as customers, products, employees, suppliers, and other key business entities. The kinds of information treated as master data can vary widely between industries and even between companies within the same industry.

Industry-Specific Master Data:

Different industries have distinct core data entities critical to their operations. For example, in the healthcare industry, patient and provider data are crucial, whereas, in the retail industry, product and customer data are paramount.

Companies in regulated industries may have specific master data requirements mandated by regulatory bodies.

Company-Specific Master Data:

Within the same industry, different companies may prioritize different sets of master data based on their unique business processes, strategies, and operational needs.

Organizational size, structure, and business model can influence what is considered master data.

Customization and Flexibility:

Master data management (MDM) systems and practices are designed to be flexible to accommodate the unique needs of different organizations.

Customizing MDM allows companies to manage and maintain the integrity of the specific data entities that are critical to their success.

Choosing unreliable sources for data can lead to significant data quality issues. This problem is often a symptom of underlying issues in data management practices.

Too Much Data:

While having excessive data can create challenges, it is not directly related to the reliability of data sources.

Immature Data Architecture:

An immature data architecture can contribute to various data issues, but it specifically relates to the overall design and infrastructure rather than the selection of data sources.

Weak Master Data Management (MDM):

MDM is crucial for ensuring data quality and consistency. Weak MDM practices can lead to poor data governance, lack of standardization, and the use of unreliable data sources.

Effective MDM involves establishing strong governance policies, data stewardship, and validation processes to ensure data is sourced from reliable and authoritative sources.

Too Little Data:

Insufficient data can be problematic but is not directly related to choosing unreliable data sources.

No Chance Controls:

This option is not a standard term in data management and does not directly address the issue of data source reliability.

Tracking the number of MDM subject areas and source system attributes refers to defining the scope and coverage of the subject areas and attributes involved in an MDM initiative. This process includes identifying all the data entities (subject areas) and the specific attributes (data elements) within those entities that need to be managed across the organization. By establishing a clear scope and coverage, organizations can ensure that all relevant data is accounted for and appropriately managed.

References:

DAMA-DMBOK2 Guide: Chapter 10 – Master and Reference Data Management

"Master Data Management and Data Governance" by Alex Berson, Larry Dubov

Definitions and Context:

False Positive: This occurs when a match is incorrectly identified, meaning records are deemed to match when they should not.

True Positive: This is a correct identification of a match, meaning records that should match are correctly identified as matching.

False Negative: This occurs when a match is not identified when it should have been, meaning records that should match are not matched.

True Negative: This is a correct identification of no match, meaning records that should not match are correctly identified as not matching.

Anomaly: This is a generic term that could refer to any deviation from the norm and does not specifically address the context of matching records.

Explanation:

The question asks about a scenario where two records should have matched but did not. This is the classic definition of aFalse Negative.

In data matching processes, this is a critical error because it means that the system failed to recognize a true match, which can lead to fragmented and inconsistent data.

References:

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

ISO 8000-2:2012, Data Quality - Part 2: Vocabulary.