Course Introduction
IT Service Management and Support Analytics Training Course equips IT professionals, service managers, support teams, and technology leaders with practical capabilities to use data and analytics to improve IT service quality, operational efficiency, user experience, service availability, and technology decision-making. As organizations become increasingly dependent on digital services, effective analytics is essential for understanding service demand, measuring performance, identifying operational bottlenecks, and prioritizing continuous improvement across the IT service environment.
Effective IT service management analytics requires reliable information from service management platforms, incident and request systems, configuration management databases, monitoring tools, asset management systems, change management records, problem management repositories, service-level agreements, customer feedback platforms, and financial systems. Participants will learn how to collect, validate, classify, integrate, and analyze these datasets while addressing challenges such as inconsistent categorization, incomplete records, duplicate tickets, inaccurate timestamps, fragmented service information, and weak data governance.
The course examines the IT service lifecycle from service demand and request intake through incident management, problem management, change enablement, service availability, service continuity, configuration management, and service improvement. Participants will learn how to analyze incident volumes, request trends, response and resolution times, service-level performance, recurring problems, change outcomes, availability patterns, capacity pressures, and support workloads across services, users, locations, teams, and technology environments.
A major focus is placed on translating IT service data into actionable management intelligence. Participants will learn to develop IT service dashboards, operational scorecards, KPI frameworks, trend analyses, service-level reports, root-cause views, capacity indicators, and executive summaries. The course emphasizes using analytical evidence to distinguish routine service activity from significant operational problems and to support decisions about staffing, service improvement, automation, technology investments, process redesign, and resource allocation.
Emerging technologies are reshaping IT service analytics through artificial intelligence, machine learning, predictive analytics, AIOps, natural language processing, intelligent automation, observability platforms, digital experience monitoring, and automated service management. Participants will explore how these technologies can support anomaly detection, event correlation, predictive incident management, intelligent ticket routing, automated resolution, and proactive service improvement while considering explainability, cybersecurity, privacy, data quality, resilience, and human oversight.
By the end of the course, participants will be able to analyze IT service performance across operational and strategic dimensions, identify service risks and improvement opportunities, evaluate support effectiveness, and communicate technology performance insights to technical and executive stakeholders. The practical orientation supports stronger service governance, better user experience, improved operational resilience, more effective resource planning, and continuous improvement driven by reliable IT service management evidence.
Duration
5 days
Who Should Attend
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IT service managers responsible for service performance, operational governance, and continuous service improvement.
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IT support managers overseeing service desk, technical support, incident management, and user support operations.
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IT service desk professionals seeking stronger analytical capabilities for managing service performance and demand.
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IT operations managers responsible for infrastructure, applications, availability, capacity, and operational performance.
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IT business analysts working with service management, operational, and technology performance data.
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Service-level managers monitoring service commitments, performance targets, service availability, and SLA compliance.
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Problem managers analyzing recurring incidents, root causes, known errors, and service improvement opportunities.
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Change managers evaluating change performance, change-related incidents, and technology transition outcomes.
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Configuration and asset management professionals integrating service data with technology assets and configuration information.
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IT governance, risk, and compliance professionals evaluating technology service controls and operational performance.
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Digital transformation professionals analyzing technology service adoption, experience, performance, and improvement opportunities.
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Enterprise and technology architects interested in data-driven service performance and operational intelligence.
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Customer experience and employee experience professionals analyzing digital service quality and technology user experience.
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Consultants and advisers supporting ITSM transformation, service analytics, operational improvement, and technology governance.
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CIOs, IT directors, and senior technology managers seeking reliable intelligence for service performance, investment, resilience, and strategic decision-making.
Course Objectives
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Develop a comprehensive understanding of IT service management and support analytics and its role in improving technology service performance.
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Apply practical techniques for collecting, cleaning, validating, classifying, and integrating IT service management and support datasets.
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Analyze incidents, service requests, problems, changes, availability, capacity, and service-level performance using appropriate analytical methods.
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Develop meaningful ITSM KPIs that measure service quality, responsiveness, reliability, operational efficiency, user experience, and continuous improvement.
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Identify recurring incidents, service bottlenecks, root-cause patterns, capacity pressures, change-related risks, and operational performance gaps.
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Build IT service dashboards, scorecards, visualizations, and management reports that communicate operational and strategic technology insights.
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Analyze support workloads, service demand, staffing requirements, escalation patterns, and resource utilization to improve service capacity planning.
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Evaluate service-level performance, customer experience, service availability, change outcomes, and problem-management effectiveness using reliable evidence.
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Explore AI, AIOps, predictive analytics, automation, observability, and emerging technologies that can enhance IT service intelligence.
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Strengthen responsible IT service analytics through data governance, privacy, cybersecurity, ethical automation, auditability, and appropriate human oversight.
Comprehensive Course Outline
Module 1: Foundations of IT Service Management and Support Analytics
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Principles, scope, and strategic importance of ITSM analytics for service quality, operational performance, resilience, and user experience.
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IT service lifecycle analytics covering service requests, incidents, problems, changes, availability, capacity, configuration, continuity, and continual improvement.
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ITSM performance frameworks, service KPIs, operational indicators, experience measures, service-level metrics, and evidence-based management practices.
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Common IT service analytics challenges involving fragmented systems, inconsistent classifications, incomplete records, attribution issues, and changing technology environments.
Module 2: IT Service Data Collection, Quality, Classification, and Integration
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Service management platforms, incident and request systems, CMDBs, monitoring tools, asset systems, change records, knowledge bases, and feedback platforms.
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Data quality assessment covering completeness, accuracy, consistency, duplication, categorization, priority, timestamps, service relationships, and resolution information.
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Integrating ITSM data with configuration, asset, financial, workforce, application, infrastructure, and business service information for broader service intelligence.
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Service taxonomies, incident classifications, configuration relationships, data dictionaries, metadata, documentation, data lineage, and analytical workflows.
Module 3: Spreadsheet, Database, and Business Intelligence ITSM Analytics
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Spreadsheet techniques for IT service data preparation, validation, aggregation, filtering, pivot analysis, calculations, exception testing, and reporting.
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Database concepts for organizing services, incidents, requests, users, teams, assets, configurations, changes, problems, and service performance measures.
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Business intelligence dashboards for service demand, incident trends, SLA performance, resolution times, availability, backlog, and user experience.
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Designing executive-ready ITSM reports that highlight significant trends, service risks, operational exceptions, improvement opportunities, and resource requirements.
Module 4: Incident, Request, Problem, and Resolution Analytics
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Incident analytics covering volumes, priorities, categories, affected services, response times, resolution times, escalations, and recurring events.
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Service request analytics covering request types, fulfilment times, volumes, automation opportunities, demand patterns, and user service requirements.
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Problem management analytics for recurring incidents, root-cause relationships, known errors, problem ageing, resolution effectiveness, and service impact.
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Resolution performance analytics using first-contact resolution, reopen rates, escalation patterns, backlog ageing, and service complexity indicators.
Module 5: Service-Level, Availability, Capacity, and Performance Analytics
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Service-level agreement analytics covering response targets, resolution targets, breaches, exceptions, trends, service commitments, and performance variations.
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Availability and reliability analytics covering service uptime, outages, downtime, incident duration, recurrence, service dependencies, and operational resilience.
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Capacity analytics for infrastructure, applications, support teams, service channels, workloads, and technology resources relative to demand.
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Service performance benchmarking across services, locations, user groups, support teams, periods, and operational environments using appropriate context.
Module 6: Change, Release, Configuration, and Operational Risk Analytics
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Change analytics covering change volumes, success rates, failed changes, emergency changes, change-related incidents, and implementation performance.
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Release and deployment analytics for implementation frequency, deployment outcomes, defects, rollback patterns, service disruption, and post-release performance.
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Configuration and asset analytics linking incidents, problems, changes, service dependencies, technology assets, and configuration information.
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Operational risk analytics for identifying fragile services, recurring failures, critical dependencies, unresolved risks, control weaknesses, and resilience priorities.
Module 7: User Experience, Support Workforce, Geographic, and Service Intelligence
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Digital service and user experience analytics using satisfaction feedback, service interactions, complaints, experience surveys, and service adoption information.
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Support workforce analytics covering workload, ticket allocation, utilization, productivity, skill requirements, escalation patterns, and capacity requirements.
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Service performance analysis across locations, departments, business units, user groups, service channels, technology environments, and support teams.
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Service portfolio intelligence for prioritizing improvement initiatives according to business impact, user impact, service criticality, frequency, cost, and operational risk.
Module 8: AI, AIOps, Predictive Analytics, Automation, and Emerging Technologies
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Artificial intelligence, machine learning, and AIOps applications for event correlation, anomaly detection, incident prediction, root-cause assistance, and operational intelligence.
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Predictive analytics for incident volumes, service-level breaches, capacity pressures, recurring failures, workload requirements, and emerging operational risks.
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Intelligent automation, virtual service agents, automated ticket classification, routing, knowledge recommendations, and self-service technologies.
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Responsible adoption of AI-enabled ITSM technologies, including model validation, explainability, cybersecurity, privacy, human escalation, resilience, and governance.
Module 9: ITSM Data Governance, Privacy, Cybersecurity, and Responsible Analytics
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IT service data governance frameworks covering ownership, access controls, retention, classification, quality standards, accountability, auditability, and stewardship.
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Privacy and confidentiality considerations involving user information, support tickets, device data, service interactions, employee records, and digital experience information.
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Cybersecurity risks affecting ITSM platforms, privileged accounts, ticket attachments, integrations, APIs, remote-support tools, monitoring systems, and service databases.
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Responsible analytics covering appropriate employee performance measurement, transparent automation, ethical monitoring, secure data handling, and evidence-based technology decisions.
Module 10: Integrated IT Service Management and Support Analytics Project
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Designing an end-to-end ITSM analytics framework using realistic incident, request, problem, change, service-level, availability, capacity, and user feedback datasets.
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Developing an IT service performance dashboard containing demand, backlog, resolution, SLA, availability, change, capacity, experience, and operational risk indicators.
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Interpreting service analytics findings to identify recurring problems, performance gaps, resource pressures, technology risks, user experience issues, and improvement priorities.
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Presenting an evidence-based ITSM analytics report with corrective actions, service improvement initiatives, automation opportunities, resource recommendations, and continuous monitoring measures.
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.
