Data Integrity

Enhanced Definition

In the mainframe and z/OS environment, **Data Integrity** refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that data is correct, complete, and remains uncorrupted, reflecting the true state of information, especially critical for high-volume transaction processing and mission-critical applications.

Key Characteristics

- Accuracy: Data is free from errors and faithfully represents the real-world information it's intended to model, including correct values, formats, and relationships.
- Consistency: Data adheres to predefined rules, constraints, and relationships, both within a single data store (e.g., a DB2 table) and across related data stores or systems, often implying ACID properties for transactional systems.
- Reliability: Data is available and can be trusted to be correct over time, even in the face of system failures or concurrent access.
- Validity: Data conforms to specified data types, formats, and ranges, often enforced through application logic or database constraints.
- Referential Integrity: In relational databases like DB2, this ensures that relationships between tables are maintained, preventing orphaned records (e.g., a foreign key always references an existing primary key).
- Concurrency Control: Mechanisms (like locking in DB2 or CICS) are employed to manage simultaneous access to data, preventing conflicting updates and ensuring that transactions see a consistent view of data.

Use Cases

- Financial Transaction Processing: Ensuring that a debit from one account is always matched by a credit to another, maintaining the overall balance and preventing discrepancies in banking applications running on CICS and DB2.
- Inventory Management Systems: Guaranteeing that stock levels are accurately updated after sales or receipts, preventing overselling or incorrect inventory counts in COBOL batch or online systems accessing VSAM or DB2.
- Customer Information Systems (CIS): Maintaining consistent customer details (address, contact info) across various applications (e.g., billing, support, marketing) that might access the same IMS or DB2 databases.
- Batch Data Processing: Verifying that all records in a large sequential file or VSAM KSDS are processed correctly and completely, with robust error handling to prevent partial updates or data loss during JCL-driven jobs.
- Data Replication and Disaster Recovery: Ensuring that replicated data sets or database copies remain consistent with the primary source, allowing for reliable failover and recovery operations.

Related Concepts

Data Integrity is fundamental to Database Management Systems (DB2, IMS), which provide features like primary/foreign keys, unique indexes, check constraints, and logging to enforce it. It is intrinsically linked to Transaction Processing Monitors (CICS, IMS TM), which ensure ACID properties (Atomicity, Consistency, Isolation, Durability) for transactions, guaranteeing that either all steps of a transaction complete successfully or none do. Application Programs (COBOL, PL/I) play a crucial role by implementing business rules and validation logic. Furthermore, z/OS features like data set