Planned control activities are not considered master data. Here's why:
* Master Data Examples:
* Categories and Lists: Master data typically includes lists and categorizations that are used repeatedly across multiple business processes and systems.
* Examples: Product categories, account codes, country codes, and currency codes, which are relatively stable and broadly used.
* Planned Control Activities:
* Process-Specific: Planned control activities pertain to specific actions and checks within business processes, often linked to operational or transactional data.
* Not Repeated Data: They are not reused or referenced as a stable entity across different systems.
* 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)"

Reference data plays a crucial role in financial trading. It includes data such as financial instrument identifiers, market data, currency codes, and regulatory classifications. Despite the dynamic nature of financial trades, reference data provides the necessary static information to execute and settle transactions. Industry estimates suggest that approximately 70% of the data used in financial transactions is reference data, underscoring its importance in the financial sector.
References:
* DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.
* "The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" by Ralph Kimball and Margy Ross.
* Industry publications and whitepapers on reference data management in financial services.

A ragged hierarchy is an organizational structure where different branches of the hierarchy can have varying levels of depth. This means that not all branches have the same number of levels. In the given scenario, where a high-level manager has department managers with staff and non-managers without staff as direct reports, the hierarchy does not have a uniform depth across all branches. This kind of structure is best represented and maintained as a ragged hierarchy, which allows for flexibility in representing varying levels of managerial relationships and reporting structures.
References:
* DAMA-DMBOK2 Guide: Chapter 7 - Data Architecture Management
* "Master Data Management and Data Governance" by Alex Berson, Larry Dubov

Master Data Management (MDM) Harmonization ensures that the data changes of one application are synchronized with all other applications that depend on that data.
* MDM Harmonization Definition:This process involves aligning and reconciling data from different sources to ensure consistency and accuracy across the enterprise.
* Synchronization:Ensuring that changes in one application are reflected across all dependent applications prevents data inconsistencies and maintains data integrity.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide

Metrics tied to Reference and Master Data Quality generally include:
* Data Sharing Usage: Measures how often master data is accessed and used across the organization.
* Rate of Change of Data Values: Tracks how frequently master data values are updated or modified.
* Service Level Agreements (SLAs): Monitors adherence to agreed-upon service levels for data availability, accuracy, and timeliness.
* Data Sharing Volume: Measures the volume of data shared between systems or departments.
* Excluded Metric - Operational Functions: While operational functions are important, they are not typically considered metrics for data quality. Operational functions refer to the various tasks and processes performed by systems and personnel but do not directly measure data quality.
* 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)"