Course Introduction
Telecommunications Data Analytics and Network Intelligence Training Course provides a practical framework for transforming network, subscriber, service, traffic, and operational data into actionable intelligence for telecommunications operators and technology organizations. The course examines how data analytics can improve network visibility, service quality, capacity planning, fault management, customer experience, and operational decision-making. Modern telecom environments generate large volumes of telemetry, logs, performance counters, events, usage records, and customer information, making scalable analytics essential for understanding network behavior and identifying emerging issues. Platforms such as Azure Data Explorer are specifically designed for high-volume, near-real-time analysis of time-series, log, IoT, and other operational data.
The program develops practical capabilities for integrating data from radio access networks, core networks, transport infrastructure, OSS and BSS platforms, network management systems, customer systems, billing platforms, probes, device telemetry, service platforms, and operational databases. Participants learn how to structure network datasets around cells, sites, network elements, interfaces, subscribers, devices, services, locations, timestamps, and performance indicators. The course emphasizes data quality, timestamp consistency, identifier management, dimensional modeling, event correlation, and reconciliation because network intelligence depends on connecting multiple operational data sources into a coherent analytical view.
A major focus is network performance analytics. Participants learn how to analyze availability, accessibility, retainability, throughput, latency, utilization, congestion, handovers, signaling activity, dropped sessions, failed connections, and other network performance indicators. Rather than simply monitoring individual KPIs, participants learn to examine relationships among indicators and identify the operational conditions behind degraded service. Time-series analytics, segmentation, geographic analysis, baseline comparison, threshold monitoring, and anomaly detection are used to distinguish normal network variation from potentially significant performance degradation.
The course also addresses customer and service intelligence by connecting network behavior with subscriber experience, usage patterns, service consumption, complaints, churn indicators, revenue, and customer segments. Participants examine how network events can affect customer experience and how customer-level analytics can help prioritize network investment and operational interventions. The program also covers traffic and usage analytics across voice, messaging, mobile data, broadband, enterprise services, and digital services, enabling participants to investigate demand patterns, peak periods, geographic hotspots, application behavior, and changes in network utilization.
Advanced network intelligence techniques are incorporated throughout the program, including anomaly detection, trend analysis, forecasting, event correlation, geospatial analysis, capacity modeling, predictive maintenance concepts, and machine-learning-assisted investigation. GSMA identifies AI and advanced analytics as increasingly important across mobile networks and customer operations, while current industry initiatives emphasize the importance of specialized telecom data and network intelligence for reliable AI applications. Participants therefore learn how to use advanced analytics responsibly, with appropriate validation, explainability, data governance, privacy protection, and human oversight.
By the end of the course, participants will be able to develop an integrated telecommunications analytics and network intelligence framework covering network performance, service quality, traffic behavior, customer experience, faults, capacity, operational efficiency, and predictive intelligence. The course also develops practical reporting skills using tools such as Kusto Query Language and Power BI. Azure Data Explorer supports interactive analytics over high-volume telemetry and offers native Power BI connectivity, including Import and DirectQuery options, making it suitable for scenarios requiring exploration of large operational datasets and interactive reporting. The final capstone integrates network and customer data into a comprehensive intelligence solution with dashboards, analytical findings, anomaly analysis, forecasts, and operational recommendations.
Duration
5 days
Who Should Attend
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Telecommunications network engineers seeking stronger data analytics and network intelligence capabilities.
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Network performance engineers responsible for monitoring and improving service quality and network KPIs.
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Radio access network professionals analyzing cellular performance, coverage, capacity, and utilization.
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Core network engineers working with signaling, traffic, service, session, and infrastructure data.
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Network operations center professionals responsible for monitoring events, alarms, incidents, and service degradation.
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Telecom data analysts developing network, customer, traffic, and operational intelligence solutions.
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OSS and BSS professionals integrating operational, customer, billing, and network datasets.
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Service assurance professionals responsible for quality-of-service and quality-of-experience analytics.
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Telecom planners analyzing capacity, demand, traffic growth, coverage, and network investment requirements.
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Customer experience professionals connecting network performance with subscriber behavior and service experience.
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Telecom business intelligence professionals developing dashboards, reports, KPIs, and analytical models.
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Technology and operations managers seeking data-driven visibility into network performance and telecommunications operations.
Course Objectives
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Develop a comprehensive understanding of telecommunications data analytics and network intelligence across network, service, customer, traffic, and operational environments.
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Integrate network telemetry, performance counters, alarms, events, usage records, customer information, billing data, and operational datasets for analytical purposes.
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Apply data-quality, transformation, reconciliation, timestamp, identifier, and dimensional-modeling techniques to create reliable telecom analytics datasets.
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Analyze network performance indicators including availability, accessibility, retainability, throughput, latency, congestion, handovers, and service failures.
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Detect network anomalies, unusual behavior, recurring faults, performance degradation, and emerging service-quality issues using analytical techniques.
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Connect network performance with customer experience, subscriber behavior, usage patterns, complaints, service consumption, and commercial indicators.
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Analyze traffic demand, utilization, geographic patterns, peak loads, capacity constraints, and growth trends to support network planning and optimization.
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Apply forecasting, anomaly detection, event correlation, predictive analytics, and scenario analysis to improve network operations and capacity decisions.
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Develop interactive network intelligence dashboards using appropriate data models, KQL queries, Power BI visualizations, KPIs, drilldowns, and analytical filters.
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Build an integrated telecom network intelligence solution that supports operational monitoring, service assurance, customer experience, capacity planning, and strategic decision-making.
Comprehensive Course Outline
Module 1: Telecommunications Data Analytics and Network Intelligence Foundations
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Understanding the role of data analytics in telecommunications network operations, service assurance, customer experience, capacity planning, and strategic investment.
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Examining telecom data domains including RAN, core, transport, OSS, BSS, billing, customer, device, usage, alarm, event, and network telemetry data.
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Mapping relationships among network elements, cells, sites, subscribers, devices, services, locations, events, timestamps, and performance indicators.
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Establishing a network intelligence framework connecting raw operational data, analytical KPIs, events, customer experience, operational decisions, and management reporting.
Module 2: Telecom Data Integration, Engineering and Quality Management
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Integrating network counters, alarms, logs, call-detail records, usage records, configuration data, customer information, billing data, and service-quality measurements.
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Standardizing network identifiers, cell and site codes, device identifiers, subscriber dimensions, geographic attributes, timestamps, units, technologies, and vendor-specific fields.
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Applying data-quality checks for missing measurements, duplicate events, invalid identifiers, inconsistent timestamps, incomplete periods, and unexpected network values.
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Building scalable data preparation and ingestion workflows for high-volume telemetry, structured records, semi-structured events, and time-series network information.
Module 3: Network Performance and KPI Analytics
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Analyzing network availability, accessibility, retainability, throughput, latency, utilization, signaling performance, handovers, session failures, and other operational KPIs.
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Establishing network baselines and performance thresholds for identifying deviations across cells, sites, regions, technologies, network elements, and service categories.
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Performing time-series, trend, comparative, percentile, aggregation, and segmentation analysis to identify recurring and emerging network performance patterns.
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Correlating multiple KPIs to distinguish isolated metric changes from broader network conditions that may affect service quality or operational performance.
Module 4: Traffic, Usage and Capacity Intelligence
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Analyzing voice, messaging, mobile-data, broadband, enterprise, and application traffic to understand demand patterns and network utilization.
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Identifying peak-hour behavior, geographic hotspots, seasonal demand, subscriber concentration, traffic growth, and technology-specific utilization patterns.
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Developing capacity indicators that compare available network resources with current and projected traffic demand across sites, regions, and network layers.
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Building traffic forecasts and capacity scenarios to support infrastructure planning, spectrum utilization, network expansion, optimization, and investment prioritization.
Module 5: Fault, Alarm and Anomaly Analytics
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Analyzing network alarms, incidents, outages, performance degradation, configuration changes, and event sequences to identify operational patterns.
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Applying anomaly-detection concepts to distinguish unusual network behavior from normal seasonal, geographic, technology-specific, and workload-related variations.
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Correlating alarms, KPIs, topology information, configuration changes, and historical incidents to investigate potential root causes and recurring network problems.
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Developing fault-prioritization models that consider severity, duration, affected users, affected services, geographic scope, recurrence, and business impact.
Module 6: Customer Experience and Service Intelligence
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Connecting network performance indicators with customer complaints, usage behavior, service consumption, churn indicators, revenue information, and customer segments.
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Analyzing quality-of-experience indicators for voice, data, broadband, messaging, digital services, and other telecommunications products.
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Segmenting subscribers by usage, location, device, service, value, behavior, and experience to identify groups affected by specific network conditions.
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Developing customer-impact analytics that help prioritize service-quality interventions based on affected subscribers, services, locations, and business significance.
Module 7: Geospatial, Event and Network Correlation Analytics
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Applying geographic analysis to cells, sites, coverage areas, service incidents, traffic concentrations, customer populations, and network performance patterns.
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Correlating events across network layers to identify dependencies between radio, transport, core, service, application, and infrastructure conditions.
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Using time-window analysis and event sequencing to identify relationships among configuration changes, alarms, traffic changes, outages, and performance degradation.
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Developing network intelligence views that combine topology, geography, time-series performance, events, and customer impact for faster operational investigation.
Module 8: Advanced Network Analytics, Forecasting and Predictive Intelligence
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Applying time-series forecasting to traffic, utilization, service demand, capacity requirements, faults, incidents, and other operational network indicators.
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Developing anomaly-detection and predictive models for identifying emerging degradation, unusual utilization, recurring failures, and potential capacity constraints.
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Using high-volume telemetry platforms and KQL-based analytics to explore operational data, identify patterns, perform aggregations, and investigate network behavior at scale.
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Evaluating predictive and AI-assisted telecom analytics using appropriate validation, explainability, privacy, governance, and human-review practices as network AI adoption expands.
Module 9: Network Intelligence Dashboards and Operational Reporting
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Designing network dashboards that communicate availability, accessibility, throughput, latency, congestion, utilization, incidents, alarms, and service-quality trends.
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Developing operational KPIs for network performance, fault management, customer impact, capacity utilization, service assurance, incident resolution, and network reliability.
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Creating interactive Power BI reports connected to large-scale telemetry and analytical datasets, using appropriate Import or DirectQuery approaches based on scale and reporting requirements.
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Building executive network intelligence reports that connect technical performance with customer impact, operational priorities, capacity requirements, investment considerations, and business outcomes.
Module 10: Capstone Telecommunications Network Intelligence Project
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Building an end-to-end telecom analytics solution integrating network performance data, alarms, events, traffic, subscriber information, service indicators, geographic data, and operational records.
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Preparing and validating the analytical dataset, establishing network dimensions and KPIs, developing analytical queries, and creating a scalable network intelligence model.
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Performing network performance analysis, anomaly detection, fault correlation, customer-impact analysis, traffic forecasting, capacity assessment, and predictive intelligence using the integrated dataset.
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Presenting a final network intelligence dashboard and analytical report that explains performance trends, major anomalies, service impacts, capacity requirements, operational priorities, and areas requiring further investigation.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
