Course Introduction
Software delivery and engineering teams operate in increasingly complex environments where delivery speed, product quality, reliability, developer productivity, customer experience, and operational resilience must be managed together. Software Delivery and Engineering Performance Analytics Training Course provides a practical framework for turning engineering and delivery data into actionable intelligence that supports better planning, prioritization, governance, continuous improvement, and evidence-based technology leadership.
Modern software organizations generate large volumes of data across source-code repositories, issue trackers, CI/CD pipelines, testing platforms, cloud environments, observability tools, service-management systems, project portfolios, and customer channels. The value of this information depends on data quality, consistent definitions, appropriate integration, and analytical discipline. This course examines how to establish reliable engineering datasets, reconcile information across tools, identify meaningful performance measures, and avoid misleading metrics that can distort engineering decisions.
Participants will explore analytics across the software delivery lifecycle, including requirements, planning, development, code review, build and deployment pipelines, testing, release management, production operations, incident response, and continuous improvement. The programme connects operational measures such as lead time, deployment frequency, change failure rate, recovery time, defect trends, backlog health, delivery predictability, flow efficiency, and service reliability with business and customer outcomes.
Effective engineering performance management requires more than collecting metrics. Teams need dashboards, analytical models, trend analysis, segmentation, benchmarking, root-cause analysis, and meaningful reporting structures that distinguish symptoms from underlying constraints. The course develops practical capabilities for designing engineering performance dashboards, interpreting delivery trends, analysing bottlenecks, assessing team and product performance, and communicating technical findings to engineering leaders, product managers, executives, auditors, and other stakeholders.
The programme also addresses emerging technologies and analytical methods shaping modern software engineering, including artificial intelligence-assisted development, machine learning, predictive analytics, process mining, developer experience analytics, value-stream analytics, observability data, cloud engineering metrics, and automated performance monitoring. Participants will consider responsible measurement practices, metric gaming, algorithmic bias, privacy, developer trust, security, and the importance of using analytics to improve systems and workflows rather than simply increasing individual surveillance.
By the end of the course, participants will be better equipped to build practical software delivery and engineering analytics frameworks that connect technical activity with delivery outcomes, product value, reliability, and organizational objectives. Through applied exercises, dashboard design, metric interpretation, performance diagnostics, and an integrated analytics project, participants will develop the confidence to transform engineering data into useful decision support for continuous delivery improvement.
Duration
5 days
Who Should Attend
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Software engineering managers responsible for delivery performance, engineering productivity, and team improvement.
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Heads of engineering, technology directors, and CTO-office professionals overseeing software delivery portfolios.
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DevOps, DevSecOps, and platform engineering professionals responsible for delivery pipelines and operational performance.
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Software developers and technical leads who need to understand and use engineering performance data.
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Product managers and product owners working with engineering teams on delivery planning and prioritization.
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Project and programme managers responsible for software implementation schedules, milestones, and delivery assurance.
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Agile coaches, Scrum Masters, and delivery leads supporting flow, predictability, and continuous improvement.
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Quality assurance and testing professionals analysing defects, test effectiveness, release quality, and quality trends.
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Site reliability engineering and production operations teams monitoring reliability, incidents, and recovery performance.
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Business analysts and systems analysts working with software delivery requirements and performance information.
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IT service management professionals linking development performance with operational service outcomes.
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Engineering performance, PMO, and technology governance teams developing management reporting and performance frameworks.
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Data analysts and business intelligence professionals supporting technology and software engineering analytics.
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Technology risk, audit, compliance, and assurance professionals evaluating software delivery controls and performance.
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Consultants and transformation professionals supporting organizations with digital delivery, engineering effectiveness, and analytics initiatives.
Course Objectives
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Develop a comprehensive understanding of software delivery and engineering performance analytics and its role in improving delivery outcomes, reliability, quality, productivity, and strategic technology management.
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Identify, collect, structure, and integrate relevant data from development, project management, source control, CI/CD, testing, service management, cloud, and observability platforms.
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Apply appropriate engineering performance metrics to assess delivery flow, productivity, quality, predictability, reliability, deployment effectiveness, and customer-oriented outcomes.
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Analyse software delivery bottlenecks and constraints using workflow data, cycle-time analysis, value-stream mapping, trend analysis, segmentation, and root-cause investigation techniques.
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Build practical dashboards and analytical reports that provide engineering teams, delivery managers, and executives with clear and decision-oriented performance information.
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Use quantitative methods to evaluate software quality, defects, testing performance, release stability, technical debt, change risk, and production reliability across products and services.
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Explore predictive analytics, artificial intelligence, machine learning, process mining, observability data, and automation techniques applicable to modern engineering performance management.
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Strengthen the ability to connect engineering metrics with product, customer, operational, financial, and strategic outcomes while avoiding narrow or misleading productivity measurements.
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Evaluate data governance, privacy, security, ethical measurement, metric manipulation, and responsible analytics considerations when analysing engineering and developer performance.
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Design an integrated software delivery analytics framework and translate analytical findings into practical recommendations for continuous improvement, delivery governance, and technology leadership.
Comprehensive Course Outline
Module 1: Foundations of Software Delivery and Engineering Performance Analytics
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Understanding the software delivery lifecycle, engineering value streams, delivery objectives, performance dimensions, and the role of analytics in technology decision-making.
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Exploring engineering performance concepts including flow, quality, reliability, predictability, productivity, developer experience, customer outcomes, and business value.
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Examining leading and lagging indicators, outcome-based metrics, engineering health measures, benchmarking principles, and common weaknesses in performance measurement.
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Establishing an engineering analytics operating model covering metric ownership, reporting cadence, stakeholder needs, performance reviews, continuous improvement, and decision accountability.
Module 2: Software Engineering Data Collection, Quality, Integration, and Management
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Identifying data sources across source-code repositories, issue trackers, agile platforms, CI/CD systems, testing tools, cloud platforms, observability systems, and service-management environments.
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Applying data quality principles covering completeness, consistency, accuracy, timeliness, uniqueness, traceability, standardization, and reconciliation across engineering systems.
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Designing integrated engineering datasets and data pipelines that connect requirements, development activity, testing, deployment, incidents, services, teams, and business outcomes.
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Addressing data definitions, metadata, identifiers, historical records, event timestamps, tool interoperability, API-based collection, and challenges associated with fragmented engineering data.
Module 3: Spreadsheet, Database, SQL, and Business Intelligence Engineering Analytics
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Using spreadsheets and structured analytical models to clean, transform, summarize, validate, and analyse software delivery and engineering performance datasets.
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Applying databases, SQL queries, data models, calculated measures, aggregations, and analytical transformations to support repeatable engineering reporting.
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Designing business intelligence dashboards that combine delivery flow, quality, deployment, reliability, backlog, testing, and operational metrics for different management audiences.
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Developing visualization techniques for trends, distributions, bottlenecks, comparisons, correlations, control limits, exceptions, and performance patterns without creating misleading interpretations.
Module 4: Software Delivery Flow, Agile Performance, and Predictability Analytics
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Analysing backlog health, work-in-progress, throughput, cycle time, lead time, flow efficiency, aging work, blocked items, and delivery bottlenecks across software value streams.
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Evaluating sprint, release, milestone, and roadmap performance using delivery predictability, estimation accuracy, throughput trends, scope change, and completion patterns.
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Examining requirements and work-item analytics to identify prioritization issues, dependency constraints, excessive handoffs, rework, bottlenecks, and process inefficiencies.
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Applying value-stream analytics and process-mining concepts to understand how work moves through development processes and where intervention can improve delivery performance.
Module 5: DevOps, CI/CD, Release, and Software Quality Analytics
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Analysing deployment frequency, build performance, pipeline reliability, release cadence, change failure rates, rollback patterns, and other indicators of delivery effectiveness.
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Evaluating software quality through defect trends, escaped defects, severity patterns, test coverage, automation rates, test execution performance, and quality-related rework.
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Examining code review, pull request, build, integration, and release data to identify delays, bottlenecks, review workload, failed builds, and recurring engineering constraints.
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Connecting delivery and quality analytics with release risk, customer impact, production stability, technical debt, and continuous integration and continuous delivery improvement.
Module 6: Engineering Productivity, Developer Experience, and Technical Performance Analytics
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Exploring responsible approaches to engineering productivity measurement using team-level outcomes, flow metrics, quality indicators, developer experience, and system-level performance rather than simplistic activity counts.
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Analysing developer experience through build times, development friction, tooling reliability, environment availability, dependency delays, cognitive load indicators, and internal engineering feedback.
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Assessing engineering capacity, workload distribution, skills requirements, team dependencies, technical debt, and resource constraints while maintaining appropriate privacy and confidentiality.
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Designing improvement initiatives using performance evidence, developer feedback, workflow analysis, engineering health indicators, and structured experimentation rather than unsupported assumptions.
Module 7: Reliability, Incident, Technical Debt, and Engineering Risk Analytics
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Analysing incidents, outages, service degradation, mean time to detect, mean time to recover, recurrence, severity, customer impact, and reliability trends across technology services.
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Connecting software delivery performance with production observability, service-level indicators, service-level objectives, availability, performance, resilience, and operational risk.
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Developing technical debt and engineering risk analytics using defect patterns, dependency exposure, codebase characteristics, architecture constraints, maintenance workload, and remediation trends.
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Applying geographic, temporal, service, product, and team-level segmentation to identify concentration of operational risk and prioritize evidence-based engineering improvement.
Module 8: AI, Predictive Analytics, Process Mining, and Emerging Engineering Technologies
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Exploring artificial intelligence and machine learning applications for defect prediction, delivery forecasting, anomaly detection, incident intelligence, code analytics, and engineering decision support.
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Applying predictive methods to delivery schedules, pipeline failures, quality risks, operational incidents, capacity constraints, and other engineering performance scenarios.
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Examining process mining, automated observability analytics, AI-assisted software development, developer intelligence platforms, and intelligent engineering workflow automation.
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Evaluating emerging engineering technologies with attention to model reliability, explainability, human oversight, data quality, productivity claims, security, intellectual property, and responsible adoption.
Module 9: Engineering Data Governance, Privacy, Security, Ethics, and Responsible Analytics
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Establishing governance frameworks for engineering data ownership, access control, metric definitions, data lineage, retention, quality assurance, and analytical accountability.
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Addressing developer privacy, confidential source-code information, personnel data, customer information, security telemetry, and appropriate boundaries for engineering performance monitoring.
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Identifying risks associated with metric gaming, target fixation, surveillance-oriented measurement, biased benchmarks, inappropriate individual comparisons, and incentives that undermine engineering quality.
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Developing responsible analytics practices that promote transparency, fairness, security, constructive performance conversations, continuous learning, and appropriate human interpretation of analytical findings.
Module 10: Integrated Software Delivery and Engineering Performance Analytics Project
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Designing an end-to-end engineering performance analytics framework using realistic delivery, development, quality, deployment, reliability, and operational datasets.
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Developing a management dashboard that integrates selected engineering metrics, performance trends, exceptions, root-cause indicators, and decision-support visualizations.
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Conducting an analytical review of delivery performance to identify constraints, risks, improvement opportunities, and relationships between engineering activity and organizational outcomes.
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Presenting findings and recommendations through an executive-ready performance report that demonstrates analytical reasoning, responsible measurement, practical interventions, and continuous improvement priorities.
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.
