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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.
The MDM process step responsible for determining whether two references to real world objects refer to the same object or different objects is known as:
Entity resolution is a critical step in the MDM process that identifies whether different data records refer to the same real-world entity. This ensures that each entity is uniquely represented within the master data repository.
Data Model Management:
Focuses on defining and maintaining data models that describe the structure, relationships, and constraints of the data.
Data Acquisition:
Involves gathering and bringing data into the MDM system but does not deal with resolving entities.
Entity Resolution:
This process involves matching and linking records from different sources that refer to the same entity. Techniques such as deterministic matching (based on exact matches) and probabilistic matching (based on similarity scores) are used.
Entity resolution helps in deduplication and ensuring a single, unified view of each entity within the MDM system.
Data Sharing & Stewardship:
Focuses on managing data access and ensuring that data is shared responsibly and accurately.
Data Validation, Standardization, and Enrichment:
Ensures data quality by validating, standardizing, and enriching data but does not directly address entity resolution.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
When establishing a MOM. what is the benefit of doing data profiling?
Definitions and Context:
Data Profiling: This is the process of examining data from existing data sources and collecting statistics or informative summaries about that data.
Master Data Management (MDM): Establishing MDM involves processes and technologies for managing the non-transactional data entities of an organization.
Data profiling helps to understand the data's characteristics and quality by analyzing data values and comparing them to defined valid values.
This process is crucial in establishing a Master Data Management (MDM) system as it ensures the data adheres to the defined standards and is clean, accurate, and ready for integration into the MDM system.
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
Kimball, R. & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data.
Every process within a MDM framework includes:
Every process within an MDM framework includes a degree of governance. Here's why:
Governance Definition:
Policies and Standards: Governance involves the establishment of policies, standards, and procedures to ensure data quality, consistency, and compliance.
Oversight: Provides oversight and accountability for data management practices.
MDM Processes:
Inherent Governance: All MDM processes, from data integration to data quality management, incorporate governance to ensure the integrity and reliability of master data.
Data Stewardship: Involves data stewards who oversee data governance activities, ensuring adherence to established standards and 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)'
The Reference Data Change Request Process does NOT include which of the following sub processes:
The Reference Data Change Request Process typically involves the following sub-processes:
Receive Change Request:
Initiation: The process begins with the receipt of a change request, formally logged and acknowledged.
Identify Stakeholder:
Stakeholder Identification: Identifying all relevant stakeholders who need to be involved or informed about the change.
Identify Impact:
Impact Analysis: Assessing the potential impact of the requested change on existing systems, processes, and data.
Decide and Communicate:
Decision Making: Reviewing the change request, making a decision, and communicating the outcome to stakeholders.
Excluded Step - Monitor Database Change: While monitoring database changes is important for overall data management, it is not typically part of the specific change request process for reference data. This step pertains more to ongoing operational monitoring rather than the change request workflow.
Data Management Body of Knowledge (DMBOK), Chapter 6: Data Development & Maintenance
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
