Home > Docs > Industries > Telecommunications

📡 Telecommunications — Network Performance & Churn Prediction¶

Last Updated: 2026-05-05 | Version: 1.0.0

"Telecom operators sit on petabytes of network and subscriber data — the ones that analyze it in real time will win on experience, not just coverage."

📑 Table of Contents¶

• Scenario Overview
• Regulatory Landscape
• Data Flow Architecture
• Why Fabric for Telecommunications
• Getting Started
• References

🎯 Scenario Overview¶

📋 Regulatory Landscape¶

🏗️ Data Flow Architecture¶

flowchart LR
    subgraph Sources["📡 Data Sources"]
        RAN["RAN / eNodeB<br/>Performance Counters"]
        CDR["CDR / xDR<br/>Mediation Platform"]
        OSS["OSS / NMS<br/>(Network Mgmt)"]
        BSS["BSS / CRM<br/>(Billing, Orders)"]
        PROBE["Network Probes<br/>(DPI / QoE)"]
    end

    subgraph Bronze["🥉 Bronze Layer"]
        B1["PM Counters<br/>(Eventstream)"]
        B2["CDR Records<br/>(batch → Delta)"]
        B3["Alarm & Fault<br/>(Eventstream)"]
        B4["Subscriber Events<br/>(CDC → Delta)"]
        B5["QoE Metrics<br/>(Eventstream)"]
    end

    subgraph Silver["🥈 Silver Layer"]
        S1["Cell KPIs<br/>(aggregated, validated)"]
        S2["CDR Rated<br/>(deduplicated, enriched)"]
        S3["Fault Correlation<br/>(root-cause mapped)"]
        S4["Subscriber Master<br/>(PII pseudonymized)"]
    end

    subgraph Gold["🥇 Gold Layer"]
        G1["Network Performance<br/>Star Schema"]
        G2["Churn Prediction<br/>Feature Store"]
        G3["Revenue Assurance<br/>KPIs"]
        G4["5G Capacity<br/>Planning"]
    end

    subgraph BI["📊 Consumption"]
        DTB["Digital Twin<br/>Builder (Tower)"]
        EVH["Eventhouse<br/>(real-time KQL)"]
        DL["Direct Lake<br/>Semantic Model"]
        PBI["Power BI<br/>Dashboards"]
        DA["Data Activator<br/>NOC Alerts"]
    end

    RAN --> B1
    CDR --> B2
    OSS --> B3
    BSS --> B4
    PROBE --> B5

    B1 --> S1
    B2 --> S2
    B3 --> S3
    B4 --> S4
    B5 --> S1

    S1 --> G1
    S1 --> G4
    S2 --> G3
    S2 --> G2
    S4 --> G2
    S3 --> G1

    G1 --> DL --> PBI
    G2 --> DL
    G3 --> DL
    G4 --> DL
    B1 --> EVH --> DTB
    G1 --> DA

💡 Why Fabric for Telecommunications¶

Petabyte-scale CDR analytics without a Hadoop cluster. Call detail records are among the largest datasets in any industry. Fabric's Lakehouse with Delta Lake handles billions of CDR rows with V-Order optimization, while Warehouse provides SQL-based aggregation for billing and revenue assurance — all on OneLake.

Real-time NOC visibility. RAN performance counters and network alarms flow through Eventstreams into Eventhouse, where KQL dashboards and Data Activator alerts give the Network Operations Center sub-5-second visibility into degradation, handover failures, and capacity exhaustion.

Churn prediction with built-in ML. AutoML trains classification models on subscriber behavior, CDR patterns, and network experience features, scoring daily and feeding retention campaigns through the same Direct Lake dashboards that marketing uses.

5G capacity planning with geospatial context. Gold-layer traffic forecasts combined with Maps in Fabric let RF engineers visualize demand hotspots and plan small-cell deployments — no external GIS tooling required.

Cell tower digital twins. Digital Twin Builder models tower sites with live RAN telemetry binding, enabling operators to monitor antenna tilt, power levels, and throughput per sector in a unified Eventhouse experience.

Subscriber privacy by design. Pseudonymized subscriber identifiers in the Silver layer, RBAC-controlled access, and GDPR deletion workflows ensure compliance with ePrivacy and CCPA requirements without sacrificing analytical depth.

🚀 Getting Started¶

1. Ingest network telemetry — Configure Eventstreams to pull RAN counters and alarm feeds into Eventhouse for real-time NOC dashboards.
2. Build the medallion Lakehouse — Follow Medallion Deep Dive for CDR, subscriber, and network data.
3. Apply privacy controls — Enable OneLake Security CLS for PII, configure GDPR deletion workflows, and set up RBAC for CALEA data segregation.
4. Build churn prediction — Create Gold-layer feature tables and train classification models via AutoML.
5. Model cell towers — Use Digital Twin Builder to create tower entities with live RAN telemetry binding.
6. Create NOC and executive dashboards — Connect Direct Lake semantic models to Power BI for network performance, revenue assurance, and capacity planning reports.

📚 References¶