Course Introduction
HR Data Analytics and Talent Intelligence Training Course is a comprehensive professional program designed to equip HR professionals, people analysts, talent managers, HR business partners, workforce specialists, and business leaders with practical capabilities for transforming employee and organizational data into actionable workforce intelligence. The course examines how HR data can be used to understand workforce composition, employee lifecycle patterns, recruitment, retention, performance, skills, engagement, mobility, organizational structures, and talent risks while supporting more informed and evidence-based people decisions.
The course establishes a strong foundation in HR analytics by connecting organizational objectives with measurable workforce outcomes. Participants learn how to define HR metrics, establish analytical questions, structure employee datasets, calculate workforce indicators, and interpret trends across departments, roles, locations, employment types, tenure groups, and other relevant dimensions. Contemporary HR analytics reporting can include headcount, turnover, diversity, workforce distribution, hiring trends, historical employee information, FTE measures, positions, and attrition, providing a broad foundation for people intelligence.
A major focus of the program is HR data preparation and analytical modeling. Participants learn how to work with employee master records, organizational hierarchies, job and position data, recruitment records, compensation information, performance information, learning records, absence data, employee surveys, and separation information. The course emphasizes consistent definitions, unique identifiers, historical employee records, time dimensions, data-quality validation, and appropriate aggregation so that HR dashboards and analytical models provide reliable information for management decisions.
The program develops practical techniques for analyzing the employee lifecycle from recruitment through onboarding, development, internal movement, engagement, retention, and separation. Participants learn to analyze hiring trends, workforce composition, tenure, attrition, retention, internal mobility, performance patterns, skills, and employee experience indicators. They also learn to distinguish descriptive relationships from causal conclusions and to interpret people data responsibly, particularly when analyzing sensitive workforce populations or small employee groups.
Talent intelligence is a central component of the course. Participants learn how to combine workforce data, skills information, organizational structures, employee experience indicators, and business requirements to identify talent capabilities, critical roles, skill gaps, mobility opportunities, workforce risks, and development priorities. Modern employee-experience analytics can also connect employee sentiment with workplace behavior data; Microsoft documents integrations between Viva Glint and Viva Insights that allow organizations to examine relationships between survey sentiment and workplace patterns while applying confidentiality thresholds.
By the end of the program, participants will be able to develop an integrated HR analytics and talent intelligence framework that connects workforce data, HR KPIs, employee lifecycle analysis, talent insights, dashboards, and strategic decision-making. They will learn how to communicate analytical findings to HR leaders and business stakeholders through clear visualizations, evidence-based narratives, and practical recommendations. The course concludes with an applied capstone in which participants analyze a workforce dataset, develop HR metrics, investigate talent patterns, identify workforce risks and opportunities, and present a management-ready talent intelligence solution.
Duration
5 days
Who Should Attend
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HR professionals responsible for workforce reporting, HR analytics, and evidence-based people management.
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People analytics specialists developing employee, talent, engagement, retention, and workforce intelligence.
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HR business partners seeking stronger analytical capabilities for strategic workforce and talent decisions.
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Talent management professionals analyzing skills, performance, succession, mobility, and employee development.
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Talent acquisition professionals evaluating recruitment pipelines, hiring patterns, candidate outcomes, and workforce requirements.
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Learning and development professionals analyzing skills, learning participation, capability gaps, and development outcomes.
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Compensation and rewards professionals working with employee structures, workforce costs, and compensation-related datasets.
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Organizational development professionals using HR data to understand organizational effectiveness and workforce patterns.
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Business analysts working with employee, recruitment, performance, engagement, and organizational datasets.
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Workforce planning professionals connecting employee data with capacity, staffing, skills, and organizational requirements.
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HR technology and HRIS professionals supporting people-data structures, reporting, analytics, and data quality.
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Senior HR managers and business leaders requiring practical talent intelligence for strategic people decisions.
Course Objectives
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Develop a comprehensive understanding of HR data analytics and apply structured analytical frameworks to employee, organizational, talent, and workforce information.
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Design meaningful HR KPI frameworks covering headcount, hiring, turnover, retention, tenure, mobility, workforce composition, performance, engagement, and talent indicators.
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Prepare and transform HR datasets into reliable analytical structures suitable for reporting, workforce analysis, employee lifecycle analysis, and strategic talent intelligence.
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Analyze employee populations across departments, locations, job families, organizational levels, employment types, tenure groups, and other relevant workforce dimensions.
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Evaluate recruitment, hiring, onboarding, internal movement, retention, and separation patterns to identify important employee lifecycle trends and areas for investigation.
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Apply analytical techniques to talent data covering skills, capabilities, performance, development, succession, mobility, critical roles, and organizational talent requirements.
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Develop employee engagement and experience analytics that connect survey information, workforce characteristics, and relevant behavioral indicators while respecting privacy and confidentiality requirements.
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Build interactive HR analytics dashboards that communicate workforce KPIs, employee lifecycle patterns, talent insights, organizational trends, and areas of potential workforce risk.
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Establish responsible HR analytics practices covering data quality, privacy, confidentiality, access control, appropriate aggregation, governance, interpretation, and documentation.
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Complete an applied talent intelligence project integrating HR data preparation, KPI analysis, employee lifecycle analytics, talent segmentation, visualization, interpretation, and strategic recommendations.
Comprehensive Course Outline
Module 1: Foundations of HR Data Analytics and Talent Intelligence
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Understanding HR analytics, people analytics, talent intelligence, workforce intelligence, employee data, and their role in evidence-based organizational decision-making.
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Connecting HR analytics with organizational strategy, business objectives, workforce requirements, talent priorities, employee experience, productivity, and organizational capability.
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Distinguishing descriptive, diagnostic, predictive, and decision-support analytics and understanding how each approach contributes to different HR questions.
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Establishing analytical questions, stakeholder requirements, business definitions, data requirements, and reporting principles for effective HR intelligence programs.
Module 2: HR Data Architecture, Preparation, and Quality
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Understanding employee master data, organizational structures, positions, jobs, departments, locations, recruitment records, performance data, learning information, and employee lifecycle records.
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Preparing HR datasets by addressing duplicates, missing values, inconsistent classifications, invalid dates, changing organizational structures, and incomplete employee histories.
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Designing analytical dimensions and measures for employees, departments, positions, job families, locations, employment types, tenure, hiring, separation, and talent indicators.
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Establishing HR data-quality controls covering validation, documentation, ownership, definitions, data lineage, access, and repeatable preparation processes.
Module 3: HR KPIs, Workforce Composition, and Employee Intelligence
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Developing HR KPI frameworks covering headcount, FTE, hiring, turnover, retention, tenure, vacancies, workforce distribution, internal movement, and employee lifecycle measures.
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Analyzing workforce composition by department, business unit, location, job family, organizational level, employment type, tenure, and other relevant dimensions.
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Measuring workforce changes over time using period comparisons, growth rates, rolling measures, cohort analysis, and organizational movement indicators.
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Building analytical frameworks that distinguish workforce facts, trends, relationships, anomalies, and areas requiring additional investigation or qualitative context.
Module 4: Talent Acquisition and Recruitment Analytics
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Analyzing recruitment pipelines, vacancies, applications, interviews, offers, hires, recruitment sources, time-to-fill, candidate progression, and hiring outcomes.
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Evaluating hiring performance by department, role, location, employment type, recruitment source, candidate segment, and reporting period.
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Measuring recruitment conversion rates, vacancy duration, hiring activity, candidate movement, and other indicators that support talent acquisition intelligence.
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Connecting recruitment analytics with organizational requirements to identify hiring priorities, potential capability gaps, workforce constraints, and emerging talent needs.
Module 5: Employee Lifecycle, Retention, and Attrition Analytics
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Measuring employee turnover, voluntary and involuntary separations, retention, tenure, employee movement, and lifecycle patterns across organizational populations.
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Analyzing attrition and retention patterns by department, role, location, tenure, employment type, employee segment, and other relevant workforce dimensions.
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Investigating employee movement through promotions, transfers, role changes, organizational restructuring, and internal mobility across the employee lifecycle.
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Developing analytical approaches for identifying workforce patterns while carefully distinguishing statistical association from evidence of causal relationships in observational HR data.
Module 6: Talent Segmentation, Skills, Performance, and Mobility
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Developing talent segments using skills, roles, performance indicators, career stage, experience, potential, mobility, development activity, and organizational requirements.
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Analyzing workforce skills and capabilities to identify critical competencies, emerging requirements, capability gaps, and areas for targeted talent development.
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Evaluating internal mobility, promotions, transfers, career progression, and movement patterns to understand how talent flows through the organization.
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Connecting performance and development information with talent intelligence while applying appropriate governance, privacy controls, interpretation standards, and organizational context.
Module 7: Employee Engagement, Experience, and Sentiment Analytics
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Understanding employee engagement measurement, survey data, sentiment indicators, participation, favorability, employee experience dimensions, and organizational feedback.
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Analyzing employee experience patterns across departments, roles, locations, tenure groups, organizational levels, and other appropriately sized workforce populations.
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Exploring how employee sentiment data can be combined with workplace-pattern information to develop broader organizational insights; Microsoft documents Viva Glint and Viva Insights integration for this purpose.
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Applying confidentiality, aggregation, suppression, and responsible interpretation practices when analyzing employee feedback and sensitive workforce information.
Module 8: Predictive Talent Intelligence and Workforce Risk Analysis
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Introducing predictive HR analytics concepts for retention, recruitment, workforce demand, skills, employee lifecycle outcomes, and other organizational talent questions.
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Developing analytical features from employee history, tenure, role, performance, engagement, mobility, recruitment, and other workforce information for structured predictive analysis.
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Evaluating predictive models using appropriate analytical measures while considering data quality, bias, interpretability, uncertainty, fairness, and the practical implications of model outputs.
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Developing responsible talent-risk frameworks that support human decision-making without treating analytical predictions as automatic decisions about individual employees.
Module 9: HR Analytics Dashboards and Talent Intelligence Reporting
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Designing HR dashboards covering headcount, workforce composition, hiring, turnover, retention, employee lifecycle, talent, engagement, and organizational performance indicators.
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Building interactive reports with filters, drill-downs, trend analysis, segmentation, comparative views, and management-oriented summaries for HR and business stakeholders.
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Applying visualization principles to communicate workforce trends, talent patterns, anomalies, risks, and opportunities clearly without exposing inappropriate individual-level information.
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Developing Power BI-based HR intelligence solutions using dashboards, semantic models, visualizations, and interactive reporting; Microsoft's HR sample includes reporting on new hires, active employees, former employees, and separation analysis.
Module 10: Talent Intelligence Strategy and Applied Capstone
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Designing an end-to-end HR analytics framework connecting workforce data, HR KPIs, employee lifecycle analysis, talent segmentation, employee experience, predictive insights, dashboards, and strategic decisions.
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Establishing governance practices for HR data quality, privacy, confidentiality, access controls, analytical definitions, documentation, reporting consistency, and responsible talent intelligence.
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Completing a practical capstone project using employee, recruitment, organizational, performance, engagement, skills, and lifecycle data to identify meaningful talent insights.
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Presenting the final HR analytics solution through dashboards, analytical findings, talent patterns, workforce risks, data limitations, strategic recommendations, and measurable implementation 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.
