Historical Data

Enhanced Definition

Historical data, in the mainframe context, refers to information collected and retained over a period to track system performance, application activity, security events, or business transactions. This data is typically used for auditing, compliance, trend analysis, capacity planning, and problem determination, rather than real-time operational processing.

Key Characteristics

- Volume: Often comprises extremely large datasets, accumulating over months or years, requiring significant storage capacity.
- Retention Policies: Subject to strict retention policies dictated by regulatory requirements, business needs, and internal auditing standards.
- Access Patterns: Predominantly read-only or read-heavy once created, with infrequent updates or deletions, primarily accessed for analysis or reporting.
- Storage Tiering: Frequently moved from high-performance online storage (DASD) to more cost-effective, slower archival media like tape or hierarchical storage management (HSM) systems.
- Data Sources: Originates from various mainframe components, including system management facilities (SMF) records, Resource Measurement Facility (RMF) data, application log files (e.g., CICS journals, DB2 logs, IMS logs), and security audit trails.
- Immutability: Often treated as immutable once written, ensuring data integrity for audit and compliance purposes.

Use Cases

- Performance Monitoring and Capacity Planning: Analyzing SMF and RMF data to identify performance bottlenecks, understand resource consumption trends, and forecast future hardware requirements.
- Auditing and Compliance: Retaining security logs (e.g., RACF or ACF2 audit trails) and application transaction logs to meet regulatory requirements and internal audit mandates.
- Problem Determination: Reviewing historical system logs, application dumps, or CICS transaction journals to diagnose intermittent issues or understand the state of the system at a past point in time.
- Business Intelligence and Reporting: Extracting historical transaction data from DB2 or IMS databases to populate data warehouses for long-term business analysis and strategic decision-making.
- Chargeback and Billing: Using resource consumption data (e.g., SMF records) to accurately allocate computing costs to different departments or applications.

Related Concepts

Historical data is intrinsically linked to System Management Facilities (SMF) and Resource Measurement Facility (RMF), which are the primary mechanisms for collecting system-level performance and accounting data on z/OS. It often undergoes archiving processes, where it's moved to Hierarchical Storage Management (HSM) or tape libraries for long-term, cost-effective storage. This data is crucial for compliance and auditing, forming the basis for demonstrating adherence to regulations. Furthermore, historical operational data can be transformed and loaded into data warehouses (often running on z/OS or offloaded to distributed platforms) for complex analytical queries and business intelligence.

Best Practices:

Define Clear Retention Policies: Establish and enforce specific retention periods for different types of historical data based on regulatory requirements, business value, and cost considerations.
Implement Efficient Archiving Strategies: Utilize HSM or tape management systems to automate the migration of aged historical data from expensive DASD to lower-cost storage tiers.
Leverage Data Compression: Apply compression techniques (e.g., zEDC, DFSORT compression) to historical datasets to reduce storage footprint and improve I/O efficiency during retrieval.
Ensure Data Integrity and Security: Implement robust security controls (e.g., RACF, ACF2) to protect historical data from unauthorized access or modification, and use checksums or digital signatures where appropriate.
Plan for Efficient Retrieval: Organize archived data with appropriate indexing or metadata to facilitate quick and targeted retrieval when specific historical information is required for analysis or audit.
Regularly Purge Obsolete Data: Periodically review and purge historical data that has exceeded its retention period to manage storage costs and reduce the scope of data governance.