A division of power approach to master data governance provides the benefit of:
Division of Power in Data Governance: This approach distributes decision-making authority across different levels or areas within the organization.
Benefits:
Better alignment of decisions: By distributing power, decisions can be made that are better suited to the specific needs and contexts of different parts of the organization. This ensures that decisions about data management are relevant and effective for each particular area.
Avoids centralization issues: Centralized decision-making can often be disconnected from the needs of different departments or functions.
Improved responsiveness:
Decentralized governance can enable faster and more contextually appropriate responses to data management issues.
Other Options Analysis:
Spreads the blame for bad decisions: This is not a strategic benefit but rather a negative consequence.
Centralizing responsibility: This contradicts the concept of division of power.
Lower expense: While decentralization might lead to better decision-making, it doesn't inherently mean lower costs.
Facilitating a decision by committee model: This can lead to slower decision-making processes and isn't the primary benefit of a division of power.
Conclusion: The key benefit of a division of power approach in master data governance is the better alignment of decisions based on varying levels of organizational data sharing.
DMBOK Guide, sections on Data Governance and Organizational Structures.
CDMP Examination Study Materials.
All of the following methods arc a moans to protect and secure master data In a production environment except for which of the following?
Protecting and securing master data in a production environment can be achieved through various methods. Encryption ciphers, static masking, trust model technologies, and dynamic masking are all techniques used to safeguard data. However, usage agreements, while important for data governance and legal compliance, are not a technical method for securing data in the same way that the other options are. Usage agreements define the terms under which data can be accessed and used, but they do not directly protect the data itself.
DAMA-DMBOK2 Guide: Chapter 11 -- Data Security Management
'Data Masking: A Key Component of a Secure Data Management Strategy' by Anjali Kaushik
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.
One of the main guiding principles for Reference and Master Data is the one related to ownership, which states that:
Ownership is a crucial principle in managing Reference and Master Data. Here's an in-depth look at why:
Organizational Ownership:
Unified Responsibility: Reference and Master Data are assets that span across various functions and departments within an organization.
Consistency and Accuracy: Ensuring that data ownership is attributed to the organization prevents silos and ensures data is consistently accurate and available across all departments.
Data Governance: Proper governance frameworks ensure that data is managed in a way that meets the organization's needs and complies with relevant regulations and standards.
Avoiding Departmental Silos:
Cross-functional Use: Different departments use and rely on Reference and Master Data, so ownership by a single department can lead to conflicts and inconsistencies.
Holistic Management: Centralized ownership enables holistic data management practices, enhancing data quality and usability across the 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)'
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)'
