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Sharing of Reference and Master data across an enterprise requires which of the following?
Sharing reference and master data across an enterprise requires effective collaboration and communication among various stakeholders within the organization.
Staging Area:
A staging area can be used for intermediate data storage during processing but is not a requirement for sharing data.
Maintaining and Storing History Records:
Historical records are important for auditing and tracking changes but do not directly facilitate the sharing of current reference and master data.
Collaboration Between Multiple Parties Internal to the Organization:
Effective sharing of master and reference data requires collaboration among different departments and stakeholders to ensure data consistency, quality, and governance.
This includes establishing clear communication channels, defining roles and responsibilities, and ensuring alignment on data standards and practices.
Identification of Business Key and Surrogate Keys:
Keys are important for data integration and linking but do not by themselves ensure effective sharing of data.
Creation of Foreign Keys to Support Dimensions:
Foreign keys are used in relational databases to link tables but are not specifically required for the sharing of master data.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
An organization's master data can be acquired from an external third-party?
An organization's master data can indeed be acquired from external third parties. Here's how and why:
Third-Party Data Acquisition:
Enrichment: External data sources can be used to enrich an organization's master data, providing additional details and context.
Accuracy and Completeness: Acquiring data from reputable third-party sources can enhance the accuracy and completeness of master data.
Use Cases:
Market Data: Organizations may purchase market data to complement their internal customer or product data.
Reference Data: Common reference data, such as postal codes or industry classifications, are often obtained from external providers.
Integration:
Data Integration: Master data acquired from third parties needs to be integrated into the organization's MDM system, ensuring it aligns with existing data standards and governance policies.
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
Which of the following Is a characteristic of a probabilistic matching algorithm?
Probabilistic matching algorithms assign a score based on the weight and degree of match, assign weights to variables based on their discriminating power, and use individual attribute matching scores to create a match probability percentage. Additionally, after the matching process, some records typically require manual review and decisioning to ensure accuracy. Therefore, all provided characteristics describe the nature of probabilistic matching algorithms accurately.
DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.
'Master Data Management and Data Governance' by Alex Berson and Larry Dubov
What statement is most accurate about master data metadata?
Master data metadata provides crucial information about the master data, offering context and supporting its management and use within the organization.
Who, What, and Where Context:
Metadata provides descriptive information about the master data, including details about who created or modified the data, what the data represents, and where it is used.
This contextual information is essential for understanding the origins, purpose, and usage of the master data.
Includes a Sample of Content:
While metadata might include examples or samples of the data, this is not its primary purpose.
Improving Fit-for-Purpose Choices:
Metadata helps improve the application and governance of master data by providing context and supporting data management decisions.
Securing the Content:
Metadata itself is not primarily focused on security, though it can support data governance and access control processes.
Technical or Business Perspectives:
Metadata can encompass both technical and business perspectives, providing a holistic view of the data's context and usage.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Which of these metrics can be used to measure metadata documentation quality?
Measuring metadata documentation quality involves several metrics that collectively provide a comprehensive view of the quality and effectiveness of metadata management practices.
Random Survey based on Enterprise Definition of Quality:
Conducting surveys among data users to gather feedback on the perceived quality of metadata documentation. This helps in understanding user satisfaction and identifying areas for improvement.
Currency of Metadata in the Repository:
Ensuring that metadata is up-to-date and accurately reflects the current state of the data. This is crucial for maintaining the relevance and usefulness of metadata.
Collision Logic on Two Sources Measuring How Much They Match:
Comparing metadata from different sources to identify discrepancies and ensure consistency. This metric helps in assessing the alignment and accuracy of metadata across systems.
Percentage of Attributes that have Definitions:
Measuring the completeness of metadata by checking the percentage of attributes that have well-defined descriptions. This ensures that all data elements are clearly documented and understood.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
