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Which of the following is NOT ,1 characteristic of n deterministic matching algorithm?
Deterministic matching algorithms rely on exact matches between data fields to determine if records are the same. These algorithms require high-quality data because any discrepancy, such as typographical errors or variations in data entry, can prevent a match.
Characteristics of deterministic matching:
It has a discrete all or nothing outcome (C).
It matches exact character to character of one or more fields (D).
All identifiers being matched have equal weight (E).
Since deterministic matching is highly dependent on the quality of the data being matched, option B is incorrect.
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.
An authoritative system where data consumers can obtain reliable data as an alternative to the system of record to support transactions and analysis is known as:
An authoritative system where data consumers can obtain reliable data as an alternative to the system of record is known as a 'Trusted System.'
System of Record:
The system of record (SOR) is the authoritative data source for a particular data element or dataset. It ensures data integrity, accuracy, and consistency.
Trusted System:
A trusted system provides reliable data that consumers can use for transactions and analysis. It acts as a reference point and may serve as an alternative to the system of record.
It ensures that users have access to high-quality, consistent, and trustworthy data, which is essential for decision-making and operational processes.
Other Options:
System of Reference: Generally refers to a system used for lookup and reference purposes but not necessarily authoritative for transactions.
System of Origin: The original source of data before it is integrated into other systems.
Source System: Any system that contributes data to an enterprise system but is not specifically a trusted or authoritative source.
System of Use: The system where data is actively used and consumed for various business processes.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
A 'Curation Zone' is a data architecture component used to:
A 'Curation Zone' is a data architecture component used to semantically formalize source system content. This involves:
Data Curation: The process of organizing, integrating, and enriching raw data to make it meaningful and useful.
Semantic Formalization: Applying semantic models, ontologies, and metadata to standardize and contextualize the data.
Data Quality Enhancement: Ensuring the data meets quality standards through cleansing and validation processes.
Metadata Management: Capturing and managing metadata to provide context and meaning to the data.
The curation zone plays a critical role in transforming raw data into high-quality, semantically enriched information that can be effectively used for analysis, decision-making, and operational processes.
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
'Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program' by John Ladley.
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
What role would you expect Data Governance to play in the development of an enterprise wide MDM strategy?
Data Governance plays a pivotal role in the development of an enterprise-wide Master Data Management (MDM) strategy. Here's how:
Role of Data Governance:
Policy Development: Data Governance establishes policies and standards for data management to ensure data quality, security, and compliance.
Data Stewardship: Assigns roles and responsibilities to manage and oversee data assets across the organization.
MDM Strategy Support:
Conceptual Data Model:
Producing and managing an enterprise conceptual data model helps align the organization's data architecture with its business processes.
It provides a unified view of data entities, their relationships, and how data flows through various systems, ensuring consistency and accuracy.
Alignment with Business Goals: Ensures that MDM efforts support business objectives by providing a clear framework for data usage and governance.
Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
