Master and Reference Data are forms of Data Architecture. Here's why:

Data Architecture Definition:

Structure and Design: Data architecture involves the structure and design of data systems, including how data is organized, stored, and accessed.

Components: Encompasses various components, including data models, data management processes, and data governance frameworks.

Role of Master and Reference Data:

Core Components: Master and Reference Data are integral components of an organization's data architecture, providing foundational data elements used across multiple systems and processes.

Organization and Integration: They play a critical role in organizing and integrating data, ensuring consistency and accuracy.

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

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

MDM (Master Data Management) is characterized by formal management with a high degree of diligence and collaboration. Here's why:

Formal Management:

Structured Processes: MDM involves structured processes for managing master data, including data governance, data quality management, and data stewardship.

Policies and Standards: Establishes and enforces policies and standards to ensure data consistency, accuracy, and integrity.

Collaboration:

Cross-Functional Teams: Requires collaboration across different departments, including IT, business units, and data governance teams.

Stakeholder Involvement: Engages various stakeholders in the data management process, ensuring that master data meets the needs of the entire 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)'

Implementing Master Data Management (MDM) involves several challenges, but the disparity between data sources is often the most significant.

Disparity Between Sources:

Different systems and applications often store data in varied formats, structures, and standards, leading to inconsistencies and conflicts.

Data integration from disparate sources requires extensive data cleansing, normalization, and harmonization to create a single, unified view of master data entities.

Data Quality Issues:

Variability in data quality across sources can further complicate the integration process. Inconsistent or inaccurate data must be identified and corrected.

Defining Requirements for Master Data:

While defining requirements is crucial, it is typically a manageable step through collaboration with business and technical stakeholders.

DBA Cooperation:

Getting Database Administrators (DBAs) to share table structures can pose challenges, but it is not as critical as dealing with disparate data sources.

Complex Queries and Indexes:

While important for performance optimization, complex queries and indexing issues are more technical hurdles that can be resolved with appropriate database management practices.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

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

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