Solution Overview
In today's digital-first enterprise landscape, IT operational data holds immense strategic value as the backbone of intelligent automation and integrated management systems. Advanced operational models – from AI-driven decision-making to self-healing resource allocation – fundamentally depend on high-quality, large-scale datasets. For instance, predictive maintenance algorithms require comprehensive analysis of historical failure patterns to build accurate resource orchestration models.
Current Challenges: Real-world IT ecosystems face fragmented data landscapes: multi-vendor tools create disjointed datasets with inconsistent formats and quality levels. The absence of unified standards impedes cross-tool data correlation, while prevalent issues in accuracy and completeness severely constrain value extraction. Most organizations struggle with chaotic data pipelines lacking governance frameworks for systematic integration and quality control.
Governance Framework: The IT operational data governance platform establishes end-to-end lifecycle control through:
- Pre-emptive standardization: Enforcing protocol-based data ingestion rules to eliminate format conflicts
- Real-time lineage tracking: Mapping data flow across tools via dynamic metadata tagging
- Automated anomaly detection: Deploying hybrid validation engines (rule-based + ML-driven) for quality monitoring
- Closed-loop remediation: Triggering API-integrated workflows for rapid issue resolution (alert-intervention-repair-verification)
This framework ensures auditable data integrity, reducing governance overhead by 40% while tripling data usability, fully unlocking operational data’s value for enterprise digitization.
01. Standard Metric Type Governance
Centralized management of all baseline metric specifications, including:
- Metric Categories (e.g. Availability/Performance/Security)
- Measurement Dimensions (Device/Application/Service Tier)
- Precision Definitions (Calculation formulas/Data sources/Unit constraints)
02. Operational Data Type Governance
Manages unprocessed source data elements, maintaining:
- Raw Metric Registry: Source-layer metric inventory
- Metric Mapping: Associations between raw and baseline metrics
Automation Framework
1. Data Extraction – Harvesting from multi-source systems
2. Schema Parsing – Auto-identify attributes/metadata
3. Auto-Mapping – ML-powered alignment with baselines
3. Auto-Mapping – ML-powered alignment with baselines
03. Data Instance Model Governance
Manages governed records across processing stages:
Processing Flow Raw-layer aggregation → Transformation → Consumption.
Processing Flow Raw-layer aggregation → Transformation → Consumption.
Metric Instance Model Fields:
– Instance ID
– Display name
– Owner application
– Processing node
– Timestamp
– Metric value
– Data source
04. Master Data Management
Operational master data forms the foundation for cross-system data sharing, primarily sourced from CMDB systems. Key data types include:
- Application systems
- Service components
- Infrastructure nodes (e.g., hosts, network devices)
- Organizations and personnel
05. Quality Validation Strategies
The system centrally manages quality validation strategies for different types of data instances. These strategies are utilized by data anomaly detection programs to detect anomalous data entries.
Example: Metric Data Validation
– Existence validation of metric instances
– Non-null validation for metric fields
– Non-null validation for metric fields
– Validity verification of associated applications/nodes
– Numerical value validity checks
– Timestamp validity validation
06. Anomalous Data Detection & Alerting
The system automatically identifies violations of quality validation strategies through data instance analysis, generating alert records for anomalous data instances.
Alert metadata includes:
– Alert ID
– Alert Category
– Alert Title
– Alert Message
– Severity Level
– Processing Status
– Processing Status
– Detection Timestamp