Data Model

Enhanced Definition

A data model is an abstract representation that defines the structure, organization, relationships, and constraints of data within an information system. In the mainframe and z/OS context, it serves as the blueprint for designing databases (like DB2 or IMS), file systems (like VSAM), and application data structures, ensuring data integrity and consistency.

Key Characteristics

- Abstraction Levels: Can exist at conceptual (high-level business view), logical (technology-independent structure), and physical (technology-specific implementation) levels.
- Schema Definition: Explicitly defines data elements, their types, lengths, and relationships, forming the schema for databases or files.
- Data Integrity Rules: Incorporates rules and constraints (e.g., primary keys, foreign keys, check constraints) to maintain the accuracy and consistency of data.
- Normalization Principles: Often applies normalization forms (e.g., 1NF, 2NF, 3NF) to reduce data redundancy and improve data integrity and efficiency.
- Technology-Specific Implementation: While logical models are generic, physical data models are tailored to specific mainframe technologies such as DB2 tablespaces/tables, IMS segments/hierarchies, or VSAM record layouts.
- Foundation for COPYBOOKs: Directly translates into COBOL COPYBOOKs or PL/I INCLUDE files, which define the program's view of data.

Use Cases

- Database Design: Creating the foundational structure for DB2 relational databases, including tables, columns, indexes, and relationships, or defining IMS hierarchical databases with segments and pointers.
- File System Layouts: Designing the record layouts and key structures for VSAM (KSDS, ESDS, RRDS) or sequential files (QSAM) used by COBOL or PL/I applications.
- Application Development: Providing COBOL or PL/I developers with precise COPYBOOK definitions for accessing and manipulating data, ensuring data consistency across programs.
- Data Integration and Exchange: Facilitating the mapping and transformation of data between different mainframe applications or between mainframe and distributed systems.
- System Documentation: Serving as critical documentation for understanding an organization's data assets, their meaning, and their interdependencies.

Related Concepts

A data model is fundamental to DB2, IMS, and VSAM, as it dictates their physical storage and access mechanisms. COBOL and PL/I programs directly consume data models via COPYBOOKs to define their working storage and file I/O areas. JCL DD statements refer to datasets whose structure is governed by an underlying data model, and data dictionaries or catalogs (like the DB2 catalog) store and manage metadata derived from data models.

Best Practices:

Start with Business Requirements: Begin data modeling by thoroughly understanding business processes and requirements to ensure the model accurately reflects real-world entities.
Iterative Refinement: Develop data models iteratively, starting with conceptual, then logical, and finally physical models, involving stakeholders at each stage.
Apply Normalization Judiciously: While normalization reduces redundancy, denormalization might be considered for specific z/OS performance requirements, especially in IMS or DB2 for frequently accessed data.
Comprehensive Documentation: Document all aspects of the data model, including entity definitions, attributes, relationships, data types, lengths, and constraints, using tools like ERwin or IBM Data Architect.
Performance Considerations: Design the physical data model with z/OS access methods and DBMS characteristics in mind, optimizing for data access patterns, indexing strategies, and storage efficiency.
Version Control and Governance: Implement robust version control for data models and establish governance processes to manage changes and ensure consistency across the enterprise.