TNI Data Model Navigator
A Data Model Navigator is a CMDB framework feature that presents a curated, domain-specific view of the CMDB. With TNI (Telecommunications Network Inventory) context, it organizes thousands of CMDB CI classes into a focused, hierarchical structure relevant to telecom operations.
Data Model Navigator framework stores and organizes metadata in four interconnected components: Contexts: Hierarchical Organization Tables: CI Class Descriptions Fields: Critical Attributes Relationships: How tables connect with one another.
- Contexts: Hierarchical Organization
- Tables: CI Class Descriptions
- Fields: Critical Attributes
- Relationships: How tables connect with one another
Context
A model context is a curated view of the Configuration Management Database (CMDB) that organizes CI classes, fields, and relationships around a specific operational domain. Instead of requiring you to navigate thousands of CI classes in the full CMDB hierarchy, a model context shows only the data relevant to your work.
Contexts are hierarchical. A parent context defines the broadest domain grouping. Child contexts subdivide it into focused subdomains, each carrying its own mapped tables, documented fields, and defined relationships.
Telecom Network Inventory (TNI) Context Hierarchy.
- TNI Physical: Fiber infrastructure (cables, strands, splice closures, cross-connect panels)
- TNI Logical: Logical paths that ride over physical (PON paths, VLAN circuits, MPLS LSPs)
- TNI Services: Service instances that contain logical paths
- TNI Site: Site-level containers and racks
- TNI Facilities: Power, cooling, physical plant infrastructure
- TNI Topology: Network topology views and relationships
Tables — CI Class Descriptions
Metadata describing what each CMDB CI class tracks, its purpose, and variations across contexts.
Example: Optical Fiber Cable Table.
| Field | Description |
|---|---|
| Table name | cmdb_ci_optical_fiber_cable |
| Purpose | Physical cable containing individual fiber strands for telecom transmission. Cables bundle multiple fibers for protection and routing through infrastructure. |
| Context | TNI Physical layer |
| GPON Planning use case | Determines splitter capacity and service provisioning |
| Inventory Management use case | Tags equipment and tracks deployment |
| Capacity Planning use case | Aggregates service capacity by cable |
Fields — Critical Attributes
Field-level metadata describing critical attributes, their importance across use cases, and sample values.
Example: Fiber Cable Fields.
Field 1: strand_count.
| Aspect | Details |
|---|---|
| Description | Number of individual fiber strands contained in this cable |
| GPON Planning (CRITICAL) | Determines how many circuits this cable can support |
| Inventory (INFORMATIONAL) | Tags equipment specification |
| Sample data | "24", "48", "96" |
Field Metadata = Describes individual fields within a CI class table, their purpose across use cases, and sample values.
Importance of Field Metadata: An agent knows which fields matter for which decisions and can provide realistic examples to users.
Relationships
Metadata describing how CI classes relate to each other, why those relationships exist, and their importance in different use cases.
Relationship 1: Service Contains Logical.
| Field | Value |
|---|---|
| Source | cmdb_ci_connection_service_instance (service layer) |
| Target | cmdb_ci_nl_logical_path (logical layer) |
| Modeling reason | Customer services ride over logical connectivity paths |
| Critical for | End-to-end service mapping, customer billing |
Relationship 2: Logical Rides Over Physical.
| Field | Value |
|---|---|
| Source | cmdb_ci_nl_logical_path (PON path) |
| Target | cmdb_ci_nl_physical_link (fiber span) |
| Modeling reason | Logical connectivity requires physical fiber to carry signals |
| Critical for | Network topology mapping, troubleshooting, capacity planning |
Relationship Metadata: Describes how CI classes connect across TNI layers and why those relationships matter.
Understanding Relationship Mapping: An agent understands how to traverse the data model (Service → Logical → Physical → Equipment) to answer complex questions.