The next generation CMDB data management platform is positioned as the "Unified Resource Data Hub" within IT ecosystems. Serving dual roles as data stewards and providers, it accelerates value realization from resource data through consumption-driven design. The core objective of the solution is to maximize consumption, so the architecture is generally “flat” in order to facilitate data call and data interaction with other third-party tools.
- The Standards Authority: A unified, model-driven, and easily extensible governance entity for data standard management.
- The Data Fabrication Hub: A trusted, highly automated, and consumption-oriented supplier of dynamic massive-scale data streams.
- The Interoperability Nexus: A streamlined, efficient, and proactive service framework that accelerates scenario evolution, enables cross-platform data fluidity, and fully energizes unified operational ecosystems through intelligent third-party integration orchestration.
01. Modeling
The platform features flexible and open-ended data models, enabling unified definition of resource configurations and graph data while interrelating massive volumes of multi-source, multi-format operational data including resources, configurations, alerts, changes, and logs.
Supported modeling capabilities include: data types, data fields, data dictionaries, data labels, data permissions, read-only logic, required logic, algorithmic logic, display logic, display lists, display forms, and more.
02. Discovery
Maximize the adoption of automated discovery technologies to fully replace traditional manual registration and maintenance practices.
The platform has built-in automatic discovery capabilities for more than 200 types of resources, covering types include: segment scanning, application discovery, infrastructure discovery, third-party trusted source discovery, and so on. In addition, the system supports data reconciliation, which integrates data from multiple data sources according to a predetermined strategy.
03. Governance
Data accounting consistency is a key evaluation metric for CMDB configuration management. The platform adopts a variety of means to ensure the accuracy of data that is automatically found to be accounted for or manually maintained by users.
Supported data governance mechanisms include: rigorous data validation, triggered manual review processes, version-controlled change tracking and rollback capabilities, cross-type consistency audits, automated anomaly flagging, and scheduled data lifecycle management, among others.
04. Service
The platform, grounded in resource data architecture, employs multi-modal service engines to provision resource configurations and graph intelligence across heterogeneous third-party ecosystems, strategically optimizing consumption scenario enablement through federated data interoperability frameworks.
Supported service models include: secure and model-decoupled REST APIs, the “Data Delivery” service for proactive data pushing, “Dynamic Grouping” for flexible provisioning of multidimensional dynamic datasets, and the “Federated Data” service for on-demand third-party data provisioning.
05. Scenario
The vitality of the CMDB stems from the consumption, feedback, and iterative evolution of its application scenarios. By continuously mining demands for resource configuration and graph data across IT operational domains – including service operations, fault monitoring, risk assessment, security auditing, analytical reporting, and visualization – we cultivate high-fidelity, high-velocity consumption patterns that drive continuous value realization.
Consumption scenarios include: Resource Perspective, Topology Exploration, Application Profiling, Network Segment Management, Data Center Panoramic View, Cabinet Capacity, Change Audit, Manual Reporting, and Analysis Reports.
06. Intelligent Assistant
The platform’s Version 3.0 officially launches the “CMDB Intelligent Assistant” module by integrating LLM + MCP technologies. Built upon high-quality CMDB data, this intelligent assistant enables multi-dimensional consumption scenarios including data querying, analysis, and statistical operations through flexible and user-friendly conversational interfaces.
Supported scenarios include: Capacity Analysis, Impact Assessment, Dynamic Reporting , Autonomous Discovery
Future updates will expand intelligent scenarios with enhanced LLM/MCP integration.