Power BI for Telecommunications: Network Performance and Customer Analytics

<h2>Power BI Analytics for Telecommunications: Transforming Network and Customer Data into Competitive Advantage</h2>

<p>Telecommunications companies generate more data per day than most other industries—network telemetry from millions of cell towers and devices, call detail records, customer interaction logs, billing transactions, field workforce activities, and regulatory compliance metrics. The challenge is not data volume but <strong>turning that data into actionable intelligence</strong> that improves network performance, reduces customer churn, optimizes revenue, and ensures regulatory compliance. <strong>Power BI</strong>, integrated with Microsoft Fabric and Azure, provides the analytics platform that telecom operators need to unify these disparate data streams into a coherent analytical layer consumed by network operations centers, marketing teams, executive leadership, field technicians, and regulatory compliance officers. This guide covers the architecture, dashboards, and use cases our <a href="/services/power-bi-consulting">Power BI consulting</a> and <a href="/services/data-analytics">data analytics</a> teams implement for telecommunications enterprises.</p>

<h2>Network Performance Dashboards</h2>

<h3>Key Network KPIs</h3>

<p>Network performance is the foundation of a telecom business. Every percentage point improvement in network quality correlates to measurable reductions in churn and increases in customer satisfaction. The following KPIs form the core of a Power BI network performance dashboard:</p>

<table> <thead> <tr><th>KPI</th><th>Definition</th><th>Target Threshold</th><th>Data Source</th></tr> </thead> <tbody> <tr><td>Network Availability</td><td>% of time the network is operational</td><td>99.99%</td><td>NMS/OSS</td></tr> <tr><td>Dropped Call Rate (DCR)</td><td>% of calls terminated abnormally</td><td>&lt;1%</td><td>Call Detail Records</td></tr> <tr><td>Call Setup Success Rate (CSSR)</td><td>% of attempted calls successfully established</td><td>&gt;99%</td><td>Signaling data</td></tr> <tr><td>Handover Success Rate</td><td>% of successful handovers between cells</td><td>&gt;98%</td><td>RAN controller logs</td></tr> <tr><td>Average Throughput (DL/UL)</td><td>Average data speeds for download and upload</td><td>Varies by technology</td><td>Performance counters</td></tr> <tr><td>Latency (RTT)</td><td>Round-trip time for data packets</td><td>&lt;30ms (4G), &lt;10ms (5G)</td><td>Network probes</td></tr> <tr><td>Packet Loss Rate</td><td>% of data packets lost in transit</td><td>&lt;0.1%</td><td>Network probes</td></tr> <tr><td>RRC Connection Success Rate</td><td>% of successful Radio Resource Control connections</td><td>&gt;99.5%</td><td>RAN counters</td></tr> <tr><td>VoLTE MOS Score</td><td>Voice quality Mean Opinion Score for VoLTE</td><td>&gt;3.8</td><td>Voice quality probes</td></tr> <tr><td>Cell Utilization</td><td>% of cell capacity in use during peak hours</td><td>Warning at &gt;70%</td><td>RAN performance counters</td></tr> </tbody> </table>

<h3>Dashboard Architecture</h3>

<p>Network performance dashboards require a specific architecture due to the volume and velocity of network data:</p>

<ul> <li><strong>Data pipeline</strong> — Network management systems (NMS) and OSS platforms export performance counters at 15-minute or 1-hour granularity. This data (typically billions of rows per month) flows through <a href="/blog/microsoft-fabric-data-engineering-etl-2026">Fabric Data Factory pipelines</a> or Azure Data Factory into a Fabric Lakehouse using Delta format with time-based partitioning.</li> <li><strong>Aggregation strategy</strong> — Raw 15-minute counters are pre-aggregated into hourly, daily, and weekly aggregation tables. Power BI semantic models use <a href="/blog/power-bi-incremental-refresh-data-partitioning-guide-2026">incremental refresh</a> to keep daily data for the last 90 days and hourly data for the last 7 days, with weekly data for longer-term trending.</li> <li><strong>Semantic model design</strong> — Star schema with a cell site dimension (tower ID, location, technology, market, region), a time dimension, and fact tables for each KPI category (voice, data, signaling). Measures use DAX time intelligence functions for period-over-period comparisons and rolling averages.</li> <li><strong>Real-time layer</strong> — For NOC screens requiring sub-minute updates, use Fabric Eventhouse with Eventstream ingesting network alarms and real-time counters. KQL queries power real-time Power BI dashboards via DirectQuery. See our <a href="/blog/power-bi-anomaly-detection-forecasting-enterprise-2026">anomaly detection guide</a> for implementing KQL-based anomaly detection on network metrics.</li> </ul>

<h3>Network Heat Maps and Geospatial Analysis</h3>

<p>Geospatial visualization is essential for network analytics. Power BI provides several options:</p>

<ul> <li><strong>Azure Maps visual</strong> — The native Azure Maps visual in Power BI displays cell tower locations with color-coding based on KPI performance (green/yellow/red). Bubble size can represent traffic volume or capacity utilization. Layer multiple KPIs on the same map with toggle controls.</li> <li><strong>Shape maps</strong> — For market-level or regional views, shape maps display KPI aggregations by geographic boundary (county, MSA, state). This is useful for executive dashboards showing network performance by market.</li> <li><strong>Custom Mapbox visuals</strong> — For advanced geospatial needs (hexagonal binning of coverage data, sector-level antenna visualization, drive test route mapping), custom visuals built on Mapbox provide flexibility beyond the native Azure Maps visual.</li> <li><strong>Coverage analysis</strong> — Overlay coverage prediction data (from RF planning tools) with actual performance data to identify areas where predicted coverage does not match real-world experience. These gaps indicate RF optimization opportunities.</li> </ul>

<h2>Cell Tower Analytics</h2>

<h3>Per-Site Performance Monitoring</h3>

<p>Each cell tower (or cell site) is a critical asset that must be monitored individually and in aggregate:</p>

<ul> <li><strong>Site-level scorecards</strong> — A Power BI report page showing all KPIs for a selected cell site, with 30-day trending, peer comparison (same technology/market), and threshold violations highlighted. Field engineers access these scorecards on <a href="/blog/power-bi-mobile-analytics-field-reporting-2026">mobile devices</a> before and during site visits.</li> <li><strong>Worst-performing sites</strong> — A ranked table showing the bottom 10% of sites by each KPI, enabling network operations to prioritize optimization efforts. Filters allow slicing by market, technology (4G LTE, 5G NR), and site type (macro, small cell, indoor).</li> <li><strong>Capacity planning</strong> — Track cell utilization trends over time to predict when sites will reach capacity thresholds. Use Power BI forecasting (or Azure ML for more precision) to project when additional capacity (sector splits, carrier additions, small cell deployments) will be needed. This data feeds into CapEx planning processes.</li> <li><strong>Equipment health</strong> — Monitor hardware alarms, equipment age, firmware versions, and maintenance history per site. Correlate equipment health with KPI degradation to identify sites where hardware issues are impacting network performance.</li> </ul>

<h3>RAN Optimization Analytics</h3>

<p>Radio Access Network (RAN) optimization is a continuous process of adjusting parameters to maximize network performance:</p>

<ul> <li><strong>Neighbor list optimization</strong> — Analyze handover failures and ping-pong handovers to identify missing or misconfigured neighbor relationships. Power BI can visualize the handover graph between cells, highlighting problematic connections.</li> <li><strong>Interference analysis</strong> — Track uplink and downlink interference levels across the network. Identify cells with high interference and correlate with physical factors (new construction, seasonal foliage, co-channel issues) using the geospatial visuals.</li> <li><strong>Parameter tuning impact analysis</strong> — When RAN parameter changes are made (tilt adjustments, power changes, handover thresholds), Power BI time series visualizations show the before/after impact on KPIs, validating whether the change achieved the desired improvement.</li> </ul>

<h2>Customer Analytics and Churn Prediction</h2>

<h3>Customer 360 Dashboard</h3>

<p>A unified customer view in Power BI combines data from CRM, billing, network experience, and interaction history:</p>

<ul> <li><strong>Demographic profile</strong> — Segment, tenure, plan type, device type, contract status</li> <li><strong>Revenue metrics</strong> — ARPU (Average Revenue Per User), total billed revenue, payment history, revenue trend</li> <li><strong>Usage patterns</strong> — Data consumption, voice minutes, SMS, streaming usage, peak usage times</li> <li><strong>Network experience</strong> — Average throughput experienced by the customer, dropped call rate on their most-used cells, coverage quality at their home and work locations</li> <li><strong>Interaction history</strong> — Call center contacts, store visits, digital channel interactions, complaint history, NPS scores</li> <li><strong>Churn risk score</strong> — ML-generated churn probability score with key contributing factors (covered below)</li> </ul>

<h3>Churn Prediction with Azure ML</h3>

<p>Customer churn is the most costly problem in telecommunications—acquiring a new customer costs 5-7x more than retaining an existing one. Power BI integrated with Azure ML provides a churn prediction and intervention framework:</p>

<p><strong>Feature engineering for churn models:</strong></p> <ul> <li><strong>Behavioral features</strong> — Change in data usage (declining usage is a strong churn signal), change in call patterns, app usage trends, roaming frequency changes</li> <li><strong>Network experience features</strong> — Average throughput compared to plan speed, frequency of service degradation events, coverage quality at frequently visited locations, number of customer-impacting network incidents</li> <li><strong>Billing features</strong> — Bill shock events (sudden increase in charges), late payment frequency, plan downgrade requests, feature removal requests</li> <li><strong>Interaction features</strong> — Number and sentiment of support interactions, complaint categories, unresolved issue count, time since last positive interaction</li> <li><strong>Competitive features</strong> — Contract expiration proximity, availability of competitor offers in the customer's area, port-out requests from their area code</li> </ul>

<p><strong>Power BI churn dashboard:</strong></p> <ul> <li>Daily-refreshed churn risk scores for all customers, surfaced in a Power BI semantic model</li> <li>Segment-level churn risk distribution (low/medium/high/critical) with trending</li> <li>Drill-through from high-risk customers to their individual Customer 360 profile</li> <li>Retention campaign tracking—which interventions (discounts, plan upgrades, network improvements) were applied to at-risk customers and their retention outcomes</li> <li>ROI analysis of retention programs: cost of retention offers vs. lifetime value of retained customers</li> </ul>

<h3>ARPU Optimization</h3>

<p>Average Revenue Per User (ARPU) is the primary financial metric for telecom operators. Power BI analytics supports ARPU optimization through:</p>

<ul> <li><strong>ARPU decomposition</strong> — Break ARPU into components: base plan revenue, add-on services, device installment payments, overage charges, roaming revenue, and fees. Identify which components are growing or declining by segment.</li> <li><strong>Upsell and cross-sell analysis</strong> — Identify customers whose usage patterns indicate they would benefit from (and likely accept) plan upgrades, additional lines, or add-on services. Azure ML models score upsell propensity; Power BI surfaces these scores to sales and marketing teams.</li> <li><strong>Price elasticity modeling</strong> — Analyze historical plan change data to understand how price changes affect subscriber counts and total revenue by segment. Power BI what-if parameters allow scenario modeling of proposed pricing changes.</li> <li><strong>Bundle optimization</strong> — Analyze which service bundles (mobile + home internet + streaming) have the highest ARPU, lowest churn, and highest customer satisfaction. Power BI cross-filter analysis reveals optimal bundle combinations by customer segment.</li> </ul>

<h2>5G Deployment Tracking</h2>

<h3>5G Rollout Dashboard</h3>

<p>5G deployment is a multi-year, multi-billion dollar investment. Power BI provides the project tracking and performance measurement platform:</p>

<ul> <li><strong>Deployment progress</strong> — Track 5G site deployments against plan: sites planned, permitted, under construction, integrated, on-air, and optimized. Geographic visualization shows coverage expansion over time. Gantt-chart style visuals show deployment timelines by market.</li> <li><strong>Capital expenditure tracking</strong> — Monitor CapEx spend per site, per market, and against budget. Identify cost overruns early with variance analysis. Track equipment procurement lead times and costs by vendor (Ericsson, Nokia, Samsung).</li> <li><strong>Spectrum utilization</strong> — Monitor spectrum band usage (low-band, mid-band, mmWave) across the network. Track the deployment of Dynamic Spectrum Sharing (DSS) that allows 4G and 5G to share the same spectrum band, monitoring the split between technologies.</li> <li><strong>5G performance benchmarking</strong> — Compare 5G KPIs against targets and against competitor benchmarks (from drive test data or crowdsourced data like Ookla Speedtest Intelligence). Track 5G download speeds, latency, and availability by market.</li> <li><strong>Device ecosystem tracking</strong> — Monitor 5G device penetration in the subscriber base. Track which 5G devices are on the network, their capabilities (SA vs NSA, mmWave support), and the adoption rate over time. Device mix directly impacts 5G utilization and revenue potential.</li> </ul>

<h3>5G Use Case Analytics</h3>

<p>Beyond consumer mobile broadband, 5G enables enterprise use cases that require dedicated analytics:</p>

<ul> <li><strong>Fixed Wireless Access (FWA)</strong> — Track FWA subscriber growth, speed performance, capacity consumption per tower, and revenue. Monitor the impact of FWA traffic on mobile broadband performance at shared cell sites.</li> <li><strong>Network slicing</strong> — For operators deploying 5G network slicing (dedicated virtual networks for different use cases), track SLA compliance per slice, resource utilization, and revenue per slice.</li> <li><strong>Private 5G / CBRS</strong> — For enterprise private network deployments using CBRS spectrum, track deployment status, spectrum availability, performance metrics, and customer satisfaction.</li> </ul>

<h2>Regulatory Compliance (FCC)</h2>

<h3>FCC Reporting Requirements</h3>

<p>US telecommunications operators must comply with extensive FCC reporting requirements. Power BI serves as the analytics and reporting platform for compliance:</p>

<ul> <li><strong>Form 477 / BDC (Broadband Data Collection)</strong> — Report broadband deployment data including coverage areas, technology types, maximum speeds, and subscription counts. Power BI aggregates network coverage data with subscriber data to produce the required geographic filings. The geospatial capabilities enable visualization and validation of coverage claims before submission.</li> <li><strong>CPNI (Customer Proprietary Network Information)</strong> — Track access to customer proprietary data for compliance with Section 222 of the Communications Act. Power BI dashboards monitor who accesses CPNI data, for what purpose, and whether proper authorization was obtained. Anomaly detection can flag unusual access patterns.</li> <li><strong>911/E911 compliance</strong> — Monitor the accuracy and performance of emergency call routing, location delivery, and call completion rates. Power BI tracks compliance metrics against FCC mandates for wireless E911 location accuracy (50m for 80% of calls at the county level for dispatchable location).</li> <li><strong>Robocall mitigation (STIR/SHAKEN)</strong> — Track the implementation and effectiveness of caller ID authentication. Monitor the percentage of calls signed with STIR/SHAKEN attestation levels (A, B, C) and the rate of fraudulent call blocking.</li> <li><strong>Outage reporting (NORS)</strong> — The FCC requires reporting of significant network outages. Power BI monitors outage events, their duration, affected customers, and root causes. Automated alerts trigger when an outage meets FCC reporting thresholds (affecting 900,000+ user-minutes or impacting 911 service).</li> </ul>

<h3>Compliance Dashboard Architecture</h3>

<p>Regulatory compliance dashboards have specific requirements:</p>

<ul> <li><strong>Audit trails</strong> — All compliance data must have a verifiable chain of custody from source systems to the Power BI report. Use <a href="/blog/microsoft-fabric-data-engineering-etl-2026">Fabric lineage tracking</a> and Purview to document data provenance.</li> <li><strong>Historical preservation</strong> — Compliance data must be preserved for the retention period specified by each regulation (typically 5-7 years). Use <a href="/blog/power-bi-incremental-refresh-data-partitioning-guide-2026">incremental refresh with archival partitions</a> to maintain historical data in the semantic model.</li> <li><strong>Access controls</strong> — Compliance dashboards contain sensitive data and must be restricted to authorized compliance officers. Use Power BI row-level security combined with Azure AD group membership to enforce access policies.</li> <li><strong>Export capabilities</strong> — Compliance teams need to export data in specific formats for FCC filings. Power BI <a href="/blog/power-bi-report-builder-paginated-reports-2026">paginated reports</a> generate formatted regulatory filings, while underlying data can be exported through the Power BI REST API for automated filing systems.</li> </ul>

<h2>NOC (Network Operations Center) Dashboards</h2>

<h3>Real-Time NOC Monitoring</h3>

<p>Network Operations Center dashboards are the most demanding Power BI use case in telecommunications—they require real-time data, 24/7 availability, and instant visual identification of issues:</p>

<ul> <li><strong>Alarm management</strong> — Display active network alarms with severity coding (critical/major/minor/warning), affected equipment, duration, and acknowledgment status. Integrate with alarm management systems (NetCool, ServiceNow) through Fabric Eventstream for real-time updates.</li> <li><strong>Network health scorecard</strong> — A single-page view showing the health of every major network domain (RAN, core, transport, IT platforms) using RAG (Red/Amber/Green) indicators. NOC operators scan this page every few minutes to identify emerging issues.</li> <li><strong>Incident correlation</strong> — When multiple alarms fire simultaneously, NOC operators need to identify the root cause. Power BI visuals that group alarms by time, location, and equipment hierarchy help operators distinguish between a single root cause triggering cascading alarms and multiple independent issues.</li> <li><strong>Capacity and traffic monitoring</strong> — Real-time traffic levels across the network displayed as time series with threshold lines. Identify congestion points before they impact customer experience. Use KQL-based <a href="/blog/power-bi-anomaly-detection-forecasting-enterprise-2026">anomaly detection</a> to flag unusual traffic patterns automatically.</li> <li><strong>Display optimization</strong> — NOC dashboards are displayed on large-screen video walls (typically 4K or higher resolution). Design for readability at distance: large fonts, high contrast, minimal text, dominant use of color coding and iconography. Auto-rotating pages cycle through different dashboard views every 30-60 seconds.</li> </ul>

<h3>NOC Dashboard Technical Requirements</h3>

<ul> <li><strong>Refresh frequency</strong> — Real-time dashboards using Fabric Eventhouse DirectQuery update on every visual interaction. For push-dataset based dashboards, configure 1-5 second push intervals. For standard Import-based dashboards, the minimum automatic refresh is 30 minutes (but page auto-refresh can be enabled with Premium capacity at intervals as low as 1 second).</li> <li><strong>High availability</strong> — NOC dashboards must be available 24/7. Use Power BI Embedded or Fabric capacity to ensure dedicated resources. Implement a fallback: if the primary Power BI dashboard is unavailable, a secondary monitoring tool (Grafana, native NMS UI) takes over.</li> <li><strong>Low latency</strong> — Visual queries must return in under 2 seconds for a responsive NOC experience. This requires careful semantic model optimization, pre-aggregated tables, and appropriate use of DirectQuery vs. Import based on freshness requirements.</li> </ul>

<h2>Customer Experience Management (CEM)</h2>

<h3>Experience-Based Analytics</h3>

<p>Modern telecom analytics is shifting from network-centric KPIs (measuring equipment performance) to customer-centric KPIs (measuring the experience individual customers receive):</p>

<ul> <li><strong>Per-subscriber quality metrics</strong> — Instead of measuring average cell throughput, measure the throughput each subscriber actually experiences during their sessions. Aggregate by customer segment, plan type, and location to identify experience disparities.</li> <li><strong>Customer journey mapping</strong> — Track a customer's experience across every touchpoint: network quality, billing accuracy, store interactions, call center contacts, digital app usage, and service activations. Power BI visualizes the end-to-end journey, identifying friction points that drive dissatisfaction and churn.</li> <li><strong>NPS and CSAT correlation</strong> — Correlate Net Promoter Score and Customer Satisfaction surveys with network experience data, billing events, and interaction history. Identify which factors most strongly influence customer satisfaction using the <a href="/blog/power-bi-anomaly-detection-forecasting-enterprise-2026">Key Influencers visual</a>.</li> <li><strong>Service quality index (SQI)</strong> — Create a composite metric that combines multiple experience dimensions (network quality, billing accuracy, support responsiveness, digital channel usability) into a single score per customer. Track SQI trends by segment and market.</li> </ul>

<h2>Field Workforce Optimization</h2>

<h3>Field Technician Analytics</h3>

<p>Telecommunications companies deploy thousands of field technicians daily for installations, repairs, and maintenance. Power BI optimizes field operations:</p>

<ul> <li><strong>Dispatch efficiency</strong> — Track first-time fix rates, average job duration, travel time vs. work time, and jobs per technician per day. Identify bottlenecks in the dispatch process (parts availability, scheduling conflicts, travel inefficiencies).</li> <li><strong>Skill-based routing</strong> — Analyze job outcomes by technician skill certification to optimize skill-based dispatch routing. Track which job types have the highest repeat visit rates and correlate with technician experience and training.</li> <li><strong>Predictive maintenance</strong> — Use network equipment health data (alarm frequency, performance degradation trends, equipment age) to predict equipment failures before they cause outages. Azure ML models score equipment failure probability; results feed into Power BI dashboards that prioritize preventive maintenance visits.</li> <li><strong>Parts and inventory</strong> — Monitor parts consumption by job type and geography. Track van stock levels and warehouse inventory. Forecast parts demand based on planned maintenance schedules and historical failure rates. Identify parts that are frequently backordered and impact first-time fix rates.</li> <li><strong>Mobile field reporting</strong> — Field technicians access <a href="/blog/power-bi-mobile-analytics-field-reporting-2026">Power BI mobile dashboards</a> showing their assigned jobs, site-specific network data (KPIs, alarm history, equipment inventory), and work order status. Post-job, technicians update work order data that flows back into the analytics platform.</li> </ul>

<h3>Workforce Planning</h3>

<ul> <li><strong>Demand forecasting</strong> — Forecast field workforce demand based on new customer installations, network expansion plans, seasonal maintenance schedules, and historical incident rates. Power BI what-if analysis models the impact of hiring, training, and contractor utilization decisions.</li> <li><strong>Contractor management</strong> — Track contractor performance (quality, speed, cost) alongside internal technicians. Identify optimal contractor allocation by geography, job type, and volume period.</li> <li><strong>Safety analytics</strong> — Monitor safety incidents, near-misses, safety training completion, and compliance with safety protocols. Identify trends and high-risk activities that require intervention.</li> </ul>

<h2>Implementation Architecture for Telecom</h2>

<h3>Data Architecture</h3>

<p>Telecom Power BI implementations require a robust data architecture to handle the volume and variety of data sources:</p>

<ol> <li><strong>Source systems</strong> — OSS/BSS platforms (network management, provisioning, billing, CRM, workforce management), call detail record repositories, network probes, drive test tools, regulatory databases</li> <li><strong>Data lake</strong> — Fabric OneLake (or Azure Data Lake Storage) as the centralized repository, organized into Bronze (raw), Silver (cleaned/conformed), and Gold (business-ready) layers using <a href="/blog/power-bi-semantic-model-best-practices-datasets-2026">semantic model best practices</a></li> <li><strong>Real-time layer</strong> — Fabric Eventhouse for streaming network telemetry, alarms, and real-time customer events</li> <li><strong>Semantic models</strong> — Domain-specific certified Power BI semantic models: Network Performance, Customer 360, Revenue Analytics, Field Operations, Regulatory Compliance, 5G Deployment</li> <li><strong>Report layer</strong> — Thin reports connected to certified semantic models, organized by audience (NOC operators, network engineers, marketing analysts, executives, compliance officers, field managers)</li> </ol>

<h3>Implementation Roadmap</h3>

<ol> <li><strong>Phase 1 (Months 1-2): Network Performance</strong> — Implement core network KPI dashboards and NOC monitoring. This delivers immediate value to network operations teams and establishes the data pipeline foundation.</li> <li><strong>Phase 2 (Months 3-4): Customer Analytics</strong> — Build Customer 360 and churn prediction capabilities. Integrate CRM, billing, and network experience data into a unified customer semantic model.</li> <li><strong>Phase 3 (Months 5-6): Revenue and Field Operations</strong> — Implement ARPU optimization dashboards, field workforce analytics, and 5G deployment tracking.</li> <li><strong>Phase 4 (Months 7-8): Advanced AI and Real-Time</strong> — Deploy Azure ML churn prediction and anomaly detection models. Implement Fabric real-time intelligence for streaming NOC dashboards. Build predictive maintenance capabilities.</li> <li><strong>Phase 5 (Ongoing): Optimization and Expansion</strong> — Regulatory compliance reporting, model refinement based on operational feedback, expansion to additional use cases (IoT analytics, enterprise services analytics, wholesale analytics).</li> </ol>

<p><a href="/contact">Contact EPC Group</a> for a telecommunications analytics assessment. Our <a href="/services/power-bi-consulting">Power BI consulting</a> and <a href="/services/data-analytics">data analytics</a> teams specialize in implementing enterprise-scale analytics platforms for telecommunications operators—from network performance dashboards and NOC monitoring to customer churn prediction and 5G deployment tracking. We bring deep experience in the telecom data landscape (OSS/BSS integration, CDR analytics, RAN optimization) combined with Power BI and Microsoft Fabric architecture expertise.</p>