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Why is a historical perspective of Master Data important?
Historical Perspective of Master Data: Maintaining historical data about master data objects is crucial for various reasons.
Reasons for Importance:
Provides an audit trail: Keeping historical data allows organizations to track changes and understand the evolution of data over time, which is essential for auditing purposes.
May be required in litigation cases: Historical data can serve as evidence in legal disputes, demonstrating the state of data at specific points in time.
Attributes about Master Data subjects evolve over time: As entities change, such as customers moving or changing names, maintaining historical data allows for accurate tracking of these changes.
Enables business analytics to determine the root cause of behavioral changes: Historical data can help in analyzing trends and identifying reasons for changes in business metrics or customer behavior.
Conclusion: All the provided reasons collectively highlight the importance of maintaining a historical perspective of master data.
DMBOK Guide, sections on Master Data Management and Data Governance.
CDMP Examination Study Materials.
What is a trait of a Consolidated style MDM approach?
In a Consolidated style MDM (Master Data Management) approach, data from multiple source systems is integrated into a single consolidated repository. This consolidated repository acts as the authoritative source for master data, often referred to as the 'system of record.' The system of record maintains the most accurate, up-to-date, and comprehensive view of master data. Key traits of this approach include:
Centralization: All master data is centralized in one repository, which simplifies data management and governance.
Consistency: Ensures that all users and systems access the same consistent set of master data.
Data Quality: Enhances data quality through data cleansing, deduplication, and validation processes.
Single Source of Truth: Serves as the definitive source for master data, reducing discrepancies and inconsistencies across the organization.
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
'Master Data Management and Data Governance' by Alex Berson and Larry Dubov.
Choosing unreliable sources for data, which can cause data quality issues, is a result of:
Choosing unreliable sources for data can lead to significant data quality issues. This problem is often a symptom of underlying issues in data management practices.
Too Much Data:
While having excessive data can create challenges, it is not directly related to the reliability of data sources.
Immature Data Architecture:
An immature data architecture can contribute to various data issues, but it specifically relates to the overall design and infrastructure rather than the selection of data sources.
Weak Master Data Management (MDM):
MDM is crucial for ensuring data quality and consistency. Weak MDM practices can lead to poor data governance, lack of standardization, and the use of unreliable data sources.
Effective MDM involves establishing strong governance policies, data stewardship, and validation processes to ensure data is sourced from reliable and authoritative sources.
Too Little Data:
Insufficient data can be problematic but is not directly related to choosing unreliable data sources.
No Chance Controls:
This option is not a standard term in data management and does not directly address the issue of data source reliability.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Master Data Curation is used for improving the overall quality of the data throughout the business by doing the following:
Master Data Curation is a process aimed at improving the overall quality of data throughout the business. Here's how:
Data Quality Improvement:
De-duplication: The process involves identifying and eliminating duplicate records to ensure a single, accurate version of each data entity.
Data Cleaning: Removes inaccuracies and inconsistencies, enhancing the reliability of the data.
Benefits of De-duplication:
Accuracy: Ensures that each entity (e.g., customer, product) is represented only once, improving data accuracy and reducing redundancy.
Operational Efficiency: Streamlines operations by eliminating duplicate records that can cause confusion and errors in business processes.
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'
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
