NG CMDB Solution – Governance

Governance Overview

In the digital era, high-quality CMDB resource instance configurations and graph data have become critical foundations for efficient IT operations and data-driven decision-making. Data record-actuality consistency stands as a vital benchmark, serving as the cornerstone of successful CMDB data governance.

To comprehensively ensure data consistency, the platform implements a sophisticated governance framework. Strict data validation forms the primary defense, meticulously examining completeness, accuracy, and compliance across multiple dimensions. The triggered manual review mechanism reinforces quality assurance through expert validation against business rules. Version-controlled change tracking enables precise historical auditing and rapid recovery capabilities. Cross-type consistency audits maintain logical integrity between data categories. Automated anomaly flagging ensures immediate visibility of data discrepancies, while scheduled data lifecycle management optimizes storage efficiency and enhances overall data usability.

Governance Methodology

The next-gen CMDB platform delivers targeted governance solutions across key lifecycle stages of resource instance configurations:

Data Generation Phase

• Automated Discovery: Leverages comprehensive discovery technologies to maximize coverage of resource instances. These mechanisms ensure real-time synchronization between source infrastructure states (capacity configurations and relationship changes) and CMDB records, significantly reducing manual entry errors.

• Manual Entry: Supports customizable validation policies for resource fields and relationships. Validations enforce mandatory field completion, format compliance, and business logic constraints during manual data entry.

Data Validation Phase

• Manual Review: Automatically routes new or modified resource instances to dedicated review queues. Configuration administrators perform final approvals (confirm/reject) through centralized audit interfaces, minimizing data quality risks.

Data Maintenance Phase

• Version History: Implements full lifecycle version control with automated change logging, cross-version traceability, and granular rollback capabilities for rapid issue resolution.

• Cross-Data Verification & Anomaly Tagging: Enforces inherent data consistency rules (e.g., validating out-of-band IP status against server operational states). Automated network scanning detects mismatches between actual device statuses and CMDB records, flagging inconsistencies with visual tags.

Data Retirement Phase

• Expired Data Tagging: Automatically identifies obsolete records with expiration markers and routes them to dedicated cleanup modules.

• Data Cleanup: Enables configurable retention policies for automated deletion or archival of low-risk expired data.

Platform Governance Capabilities

Field Validation

Performs real-time validation of field nullability, formatting, and content compliance. Non-compliant fields trigger visual alerts with red highlighting.

Review Workbench

Dedicated interface for auditing new/changed instances with actions including data modification, classification adjustment, approval, and deletion.

Change Audit Trail

Enables selective version tracking for critical data types, supporting multi-version comparisons and targeted restoration.

Data Lifecycle Management

Automates expiration identification and provides centralized management for final verification and cleanup operations.