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)'
The format and allowable ranges of Master Data values are dictated by:
The format and allowable ranges of Master Data values are primarily dictated by business rules.
Business Rules:
Business rules define the constraints, formats, and permissible values for master data based on the organization's operational and regulatory requirements.
These rules ensure that data conforms to the standards and requirements necessary for effective business operations.
Semantic Rules:
These rules pertain to the meaning and context of the data but do not directly dictate formats and ranges.
Processing Rules:
These rules focus on how data is processed but not on the allowable values or formats.
Engagement Rules:
These rules govern interactions and workflows rather than data formats and ranges.
Database Limitations:
While database limitations can impose constraints, they are typically secondary to the business rules that drive data requirements.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Depending on the granularity and complexity of what the Reference Data represents. it may be structured as a simple list, a cross-reference or a taxonomy.
Reference data can be structured in various ways depending on its granularity and complexity.
Simple List:
Reference data can be a simple list when it involves basic, discrete values such as country codes or product categories.
Cross-Reference:
When reference data needs to map values between different systems or standards, it can be structured as cross-references. For example, mapping old product codes to new ones.
Taxonomy:
For more complex hierarchical relationships, reference data can be structured as a taxonomy. This involves categorizing data into parent-child relationships, like an organizational hierarchy or biological classification.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Master Data and metadata ran both he used to aggregate dat
a. Master Data require* that the organization:
Master data and metadata are both used to aggregate data, but master data requires that the organization identifies or develops a trusted version of truth for each of its entities.
Trusted Version of Truth:
For effective master data management, an organization must establish a single, trusted version of truth for each master data entity (e.g., customer, product).
This involves harmonizing data from various sources, resolving duplicates, and ensuring consistency and accuracy.
Master Data:
Master data includes critical business information that provides context for business transactions and analysis. It must be consistent, accurate, and up-to-date to support operational and analytical processes.
Other Options:
Transaction Activity Data: Important for operational processes but not the focus for creating master data.
One Set of Data as Source and Target: Not sufficient for managing master data.
Specific Application Solutions: While useful, they do not ensure the creation of a trusted version of truth for master data.
Transaction Audit Data: Important for auditing but not central to master data creation.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
Depending on the granularity and complexity of what the Reference Data represents. it may be structured as a simple list, a cross-reference or a taxonomy.
Reference data can be structured in various ways depending on its granularity and complexity.
Simple List:
Reference data can be a simple list when it involves basic, discrete values such as country codes or product categories.
Cross-Reference:
When reference data needs to map values between different systems or standards, it can be structured as cross-references. For example, mapping old product codes to new ones.
Taxonomy:
For more complex hierarchical relationships, reference data can be structured as a taxonomy. This involves categorizing data into parent-child relationships, like an organizational hierarchy or biological classification.
DAMA-DMBOK (Data Management Body of Knowledge) Framework
CDMP (Certified Data Management Professional) Exam Study Materials
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