A global identifier is used to:
A global identifier is used to link multiple references to the same entity across different systems or datasets. Here's why:
Purpose of Global Identifier:
Unique Identification: Provides a unique identifier that can be used to recognize the same entity across disparate systems and datasets.
Consistency: Ensures that different references or records pointing to the same entity are consistently identified and managed.
Linking Equivalent Reference:
Equivalent Reference: Global identifiers link references that are equivalent, meaning they represent the same real-world entity even if the data is stored differently in various systems.
Entity Resolution: Helps in resolving different records to a single entity, ensuring data consistency and accuracy.
Example:
Customer Records: A customer might be listed in different systems (CRM, billing, support) with slightly different details. A global identifier links these records to recognize them as the same customer.
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
Where is the most time/energy typically spent tor any MDM effort?
In any Master Data Management (MDM) effort, the most time and energy are typically spent on vetting business entities and data attributes through the Data Governance process. This step ensures that the data is accurate, consistent, and adheres to defined standards and policies. It involves significant collaboration and decision-making among stakeholders to validate and approve the data elements to be managed.
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.
Bringing order to your Master Data would solve what?
Definitions and Context:
Master Data Management (MDM): MDM involves the processes and technologies for ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an organization's official shared master data assets.
Data Quality Problems: These include issues such as duplicates, incomplete records, inaccurate data, and data inconsistencies.
Bringing order to your master data, through processes like MDM, aims to resolve data quality issues by standardizing, cleaning, and governing data across the organization.
Effective MDM practices can address and mitigate a significant proportion of data quality problems, as much as 60-80%, because master data is foundational and pervasive across various systems and business processes.
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
Gartner Research, 'The Impact of Master Data Management on Data Quality.'
What MDM style allows data to be authored anywhere?
Master Data Management (MDM) styles define how and where master data is managed within an organization. One of these styles is the 'Coexistence' style, which allows data to be authored and maintained across different systems while ensuring consistency and synchronization.
Coexistence Style:
The coexistence style of MDM allows master data to be created and updated in multiple locations or systems within an organization.
It supports the integration and synchronization of data across these systems to maintain a single, consistent view of the data.
Key Features:
Data Authoring: Data can be authored and updated in various operational systems rather than being confined to a central hub.
Synchronization: Changes made in one system are synchronized across other systems to ensure data consistency and accuracy.
Flexibility: This style provides flexibility to organizations with complex and distributed IT environments, where different departments or units may use different systems.
Benefits:
Enhances data availability and accessibility across the organization.
Supports operational efficiency by allowing data updates to occur where the data is used.
Reduces the risk of data silos and inconsistencies by ensuring data synchronization.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Which of the following is true about MDM?
MDM (Master Data Management) is characterized by formal management with a high degree of diligence and collaboration. Here's why:
Formal Management:
Structured Processes: MDM involves structured processes for managing master data, including data governance, data quality management, and data stewardship.
Policies and Standards: Establishes and enforces policies and standards to ensure data consistency, accuracy, and integrity.
Collaboration:
Cross-Functional Teams: Requires collaboration across different departments, including IT, business units, and data governance teams.
Stakeholder Involvement: Engages various stakeholders in the data management process, ensuring that master data meets the needs of the entire organization.
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
