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Ensuring high-quality reference data is critical for maintaining data accuracy, consistency, and reliability across an organization. The best way to achieve this is through robust data governance and stewardship practices.

Government Sources:

While government sources can be reliable, they are not the only sources of high-quality reference data. Relying solely on them may limit the comprehensiveness of reference data.

Drop-Down Menus:

Drop-down menus can help prevent invalid data entry but do not address the overall quality and governance of reference data.

Data Governance and Stewardship:

Implementing data governance and stewardship ensures that reference data is managed according to defined policies, standards, and procedures.

Data governance involves establishing a framework for decision-making, accountability, and control over data management processes.

Data stewardship assigns responsibility for data quality, ensuring that data is accurate, consistent, and fit for purpose.

Standard Reference Data (ISO):

Using standard reference data from organizations like ISO can enhance data quality, but it should be part of a broader governance strategy.

External Data Providers:

External data providers can offer high-quality reference data, but relying solely on them without proper governance can lead to inconsistencies and data quality issues.

Data profiling is a crucial activity in mapping source system data for MDM efforts. Data profiling involves analyzing data from source systems to understand its structure, content, and quality. Key steps include:

Data Assessment: Evaluating the data to identify patterns, inconsistencies, and anomalies.

Data Quality Analysis: Measuring the quality of data in terms of accuracy, completeness, consistency, and uniqueness.

Metadata Extraction: Extracting metadata to understand data definitions, formats, and relationships.

Data Cleansing: Identifying and correcting data quality issues to ensure that the data is suitable for integration into the MDM system.

By performing data profiling, organizations can gain insights into the current state of their data, identify potential issues, and develop strategies for data integration and quality improvement.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

"Data Quality: The Accuracy Dimension" by Jack E. Olson.

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.

References:

DMBOK Guide, sections on Data Governance and Organizational Structures.

CDMP Examination Study Materials.

For effective Master Data Management, an organization must agree on a single, standard definition of master data. Here's why:

Consistency:

Single Definition: A standardized definition ensures consistency across different departments and systems.

Avoids Confusion: Prevents discrepancies and misunderstandings regarding what constitutes master data.

Data Quality and Governance:

Unified Approach: A single definition supports unified data governance policies and data quality standards.

Data Integration: Facilitates easier data integration and interoperability across various systems and processes.

Business Efficiency:

Aligned Objectives: Ensures all parts of the organization are aligned in their understanding and use of master data, leading to more efficient operations and decision-making.

References:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"