Executive Summary
Telecom data models historically centered on OSS/BSS—customers, products, orders, inventory, usage (CDRs), billing, trouble tickets, and network inventory. That foundation still matters, but modern operators run software-defined, cloud-native networks (5G core, ORAN, MEC) that emit high-volume telemetry and support AI agents for assurance, optimization, and customer care. This article reframes the classic telecom model into an AI-first blueprint where facts → metrics → features → decisions → audit form a governed loop across the business and the network.
Core Principles of an AI-Native Telco Model
1) Productize Canonical Domains as Data Products
Treat each domain as a versioned, contract-backed data product with owners and SLAs:
Party / Customer / Account (B2C/B2B, hierarchies, contacts, consents)
Product Catalog / Offer / Price / Discount
Order / Fulfillment / Provisioning (decomposed to service orders)
Service / Subscription / SLA (fixed, mobile, enterprise, slice, IoT)
Usage / Session / CDR / xDR (voice, data, messaging, API calls)
Network Resource / Function / Topology (cells, sites, gNB/eNB, UPF, PCF, AMF, routers)
Inventory (logical/physical, SIM/IMSI/ICCID/IMEI, IPs, VLANs)
Policy & Charging (QoS profiles, PCC rules, credit control)
Assurance (fault/performance events, alarms, KPIs)
Care & Experience (tickets, interactions, NPS/CES)
Field Service (work orders, dispatch, skills, spares)
Downstream consumers—dashboards, ML pipelines, NWDAF analytics, LLM agents—integrate via contracts, not bespoke ETL.
2) Separate Facts, Metrics, Features, and Narratives
Facts: immutable records (posted CDRs, accepted orders, alarms).
Metrics: curated business logic (ARPU, CSSR, drop-rate, P95 throughput).
Features: ML-ready signals (handover failure streaks, SIM-swap risk, QoS degradation rate, device entropy).
Narratives: LLM outputs with evidence (NOC shift handover, customer-visible outage summaries).
Track lineage across layers so every recommendation is explainable.
3) Event-Centric by Default
Model business and network moments as events: OrderAccepted , ProvisionComplete , SliceQoSDegraded , SessionEstablished , Handover , CellDown , IncidentOpened , WorkOrderDispatched , TicketResolved . Events enable streaming features, low-latency assurance, and accurate replay.
4) Governance, Privacy, and Jurisdiction Built-In
First-class fields for owner, purpose, CPNI/GDPR flags, retention, consent, lawful-intercept constraints , and jurisdiction routing . Apply policy-aware retrieval to LLMs and log tool calls for audit.
Target Architecture: Lakehouse + Streams + Time-Series + Graph + Vectors + Digital Twin
Lakehouse for durable facts (orders, billing, inventory, reference data) with versioned schemas.
Streaming bus for mediation/telemetry (xDRs, alarms, KPIs) feeding online features.
Time-series store for high-granularity RAN/Core KPIs and device telemetry.
Knowledge graph connecting customer ↔ subscription ↔ service ↔ slice ↔ resource ↔ site/topology for RCA and impact analysis.
Vector indexes for RAG over playbooks, SOPs, tickets, change records, contracts, and policies.
Digital twin of the network/slices/capacity used by planners and agents to simulate changes before execution.
Feature store + Model/Prompt registry for reproducibility, A/B and safe rollout.
Modernized Canonical Domains (Telecom-Specific)
Customer, Account, Consent
Unify parties, hierarchies (B2B billing accounts, cost centers), and consent/CPNI preferences. Connect to interactions and device graphs.
Product, Offer, and Pricing
Decouple commercial offers from technical services. Link to eligibility rules, discounts, and contract obligations (SLA KPIs, credits).
Order, Decomposition, and Provisioning
Decompose orders into service orders with dependencies (e.g., fixed access + CPE + IP transit). Track orchestration states and rollback.
Service, Subscription, and Slice
Represent mobile plans, fixed services, network slices (SST/SD), QoS profiles, and SLA objectives per tenant or enterprise site.
Usage and Sessions (xDRs)
Normalize CDRs/EDRs/UDRs (voice/data/SMS/API). Capture location/time, RAT, device, cell, QoS , and charging outcomes.
Network Resource, Function, Topology
Model RAN sites/cells (NR/LTE), spectrum bands, transport, and 5GC functions (AMF/SMF/UPF/PCF). Keep versioned topology snapshots for impact analysis.
Assurance: Fault & Performance
Alarms, counters, and derived KPIs (CSSR, HOF, ERAB/PDUSession success, RSRP/RSRQ/SINR distributions, throughput percentiles). Link faults → resources → affected subscribers/services.
Experience & Care
Tickets, chat/call/email interactions, agent actions, promised remediation, and customer-experience scores (NPS, CES, MOS).
Workforce & Field Ops
Sites, access rules, work orders, skills, parts, SLAs to restore. Telemetry from technician apps for ETAs and first-time-fix analytics.
AI-Native Entities to Add
FeatureDefinition (id, owner, inputs_contract, transform_ref, pii/bias_notes, tests)
FeatureValue (entity_key, feature_id, ts, value, online/offline_source)
EmbeddingIndex (index_id, domain: sop/ticket/change/contract, dim, partitions)
EmbeddedChunk (index_id, chunk_id, vector, text_ref, source_uri, masking_policy)
PromptTemplate (id, purpose, inputs_schema, guardrails_ref, eval_suite)
ToolDefinition (id, name, contract, rate_limits, privacy_scope)
ModelCard (model_id, version, training_refs, risks, intended_use, approvals)
ModelRun (run_id, model_id, dataset_ref, metrics, fairness_audit, lineage_hash)
Obligation (id, source_clause/SLA, control_mapping, jurisdiction, status)
These make ML/LLM behaviors traceable and governable end-to-end.
High-Value AI Use Cases
1. Proactive Assurance & Self-Healing
Detect QoS drift and predict impact; recommend closed-loop actions (tilt/power tweaks, neighbor adds, slice policy change) with guardrails and approvals. Store reason codes and outcomes for learning.
2. Churn & Experience Management
Fuse network experience (RSRP/SINR, drop rate, throttling events) with billing, usage, and care history to predict churn and trigger next-best-action offers or tech interventions.
3. Fraud & Revenue Assurance
Identify SIM-swap patterns, interconnect fraud, roaming anomalies, and charging leakage by combining xDR features, device graphs, and policy logs.
4. Capacity & Energy Optimization
Forecast busy-hour load; schedule sleep modes and energy-aware policies without missing SLAs. Simulate in the digital twin before execution.
5. Enterprise Slices & SLAs
Real-time scoring of slice KPIs; automated RCA from alarms → functions → sites → customers; credit calculation and proactive notifications with citations.
6. Care Copilot & Field Tech Assistant
RAG over topology, change logs, and SOPs to draft RCA updates, outage comms, and guided repair steps; geofence to site resources and mask CPNI by default.
Example Minimal Schemas (Illustrative)
// Usage event (xDR fact)
{ "cdr_id":"u-9c12", "imsi":"310410123456789", "imei":"356789101112131",
"rat":"NR", "cell_id":"gNB-145:cell3", "start":"2025-09-01T18:03:11Z",
"duration_sec":124, "bytes_ul":1823412, "bytes_dl":9432841,
"qos":"5qi-9", "charge":"1.23", "roaming":false }
// Slice KPI (time-series fact)
{ "slice_id":"SST1-SD2", "ts":"2025-09-01T18:05:00Z",
"metric":"p95_latency_ms", "value":18.4, "site_id":"SFO-22" }
// Feature value (online)
{ "entity_key":"imsi:310410123456789",
"feature_id":"v3_handovers_failed_last_24h", "ts":"2025-09-01T18:10:00Z", "value":7 }
// Trouble ticket (experience fact)
{ "ticket_id":"INC-20488", "account_id":"A-77221", "opened":"2025-09-01T17:50Z",
"category":"MobileData", "status":"Open", "related_cells":["gNB-145:cell3"] }
// Narrative (LLM) with citations
{ "narrative_id":"noc_handover_issue_summary",
"citations":[{"source":"alarm:ALM-55321"},{"source":"change:CHG-8842"},{"source":"kb:SOP-RAN-023#section-4"}],
"redactions":["subscriber_pii"] }
Metrics That Matter
Network: CSSR, drop rate, P95/99 throughput, latency/jitter, packet loss, HOF, HO success, SRVCC/VoNR success, MTTR, energy per bit.
Business: ARPU, LTV, churn, acquisition cost, NPS/CES, first-contact resolution, credit payouts vs. SLA breaches.
Operations: Alarm volume, false-positive rate, closed-loop success rate, first-time-fix, truck rolls avoided, time-to-detect vs. time-to-resolve.
AI Quality: Feature freshness, drift, model/prompt pass rates, RAG citation coverage, red-team findings cleared.
Operating Model: MLOps + LLMOps + NetOps
Contracts & tests at every interface (mediation feeds, KPIs, features, prompts, tool calls).
Lineage from raw xDRs/alarms → feature views → decisions → executed changes.
Monitoring for data/feature drift, concept drift, hallucination or leakage risk, and guardrails on network-changing actions.
Privacy: CPNI/GDPR tagging, masking, minimization; jurisdiction routing for cross-border data and model execution.
Change governance: simulation in digital twin; staged rollout with canaries and automatic rollback.
Implementation Roadmap
Phase 1 (0–90 Days): Foundation
Lakehouse domains for orders, inventory, usage; streaming ingest for alarms/KPIs.
Feature store MVP (experience, assurance signals).
First vector index (SOPs, change logs, KB) and care/NOC RAG with citations.
Basic dashboards for slice/service health aligned to SLAs.
Phase 2 (90–180 Days): Intelligence
Churn/experience models; proactive assurance scoring with closed-loop suggestions.
Knowledge graph for topology and customer impact; ticket ↔ network linkage.
Model/prompt registry; evaluation suites; policy-aware tool execution logs.
Phase 3 (180+ Days): Agentic Operations
Self-healing playbooks (tilt/power/neighbor/policy) with approvals.
Energy-aware optimization and capacity planning in digital twin.
Enterprise slice management with real-time SLA credits and proactive comms.
Conclusion
An AI-first telecom data model elevates OSS/BSS and network inventory into an intelligent, governed nervous system. Facts, metrics, features, vectors, and narratives interlock to deliver proactive assurance, superior customer experiences, efficient operations, and trustworthy automation—so networks predict, adapt, and heal while every decision remains auditable.