HR Analytics and Workforce Insights Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends HR Business Partners, People Analytics Specialists and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-TPM-042

Before you join: Participants should have practical experience in HR, talent management, performance management, workforce planning or HR reporting.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

HR teams hold large volumes of workforce data, yet many reports stop at headcount totals, turnover percentages and engagement scores. Decision makers need evidence that explains what is happening, where performance or retention risks sit, which behavioural patterns matter, and what action is justified. Poor metric definitions, weak data quality, untested assumptions and unmanaged bias can lead to costly hiring, performance and workforce planning decisions.

This course develops practical HR analytics capability for talent and performance management. Participants build workforce measures from HRIS, applicant tracking, learning, survey and performance data; analyse behavioural evidence using ABC analysis and behaviourally anchored rating scales; test data reliability and validity; and interpret patterns using segmentation, correlation, regression and classification metrics. The course also covers performance distribution analysis, nine-box matrices, attrition risk assessment, workforce scenarios, adverse impact analysis and GDPR requirements for people data.

Teaching combines short instructor briefings with worked datasets, group analysis, scenario-based decision simulations and structured application planning. Participants practise turning workforce questions into measurable analyses, testing findings before presenting them, and framing recommendations for senior stakeholders. Each participant leaves with a reusable HR analytics toolkit including a metric dictionary, data-quality checklist, behavioural evidence framework, fairness review checklist, dashboard specification and 90-day application plan. The five-day programme is available in classroom settings in 50+ cities or live online, with a certificate of completion awarded to participants who complete the course.

The course is suited to HR professionals who already work with workforce data or performance processes and need stronger analytical discipline. It is particularly relevant where HR teams are expected to explain workforce outcomes, improve manager decision-making and demonstrate the value of talent interventions.

What you will be able to do

  • Construct data dictionaries and quality-control rules for workforce datasets
  • Calculate and interpret workforce metrics for turnover, absence, mobility and span of control
  • Apply ABC analysis and behavioural rating methods to performance evidence
  • Assess survey and assessment measures for reliability, validity and response bias
  • Analyse performance distributions, calibration patterns and nine-box talent data
  • Test workforce decisions for adverse impact, demographic differences and ethical risk
  • Interpret correlation, regression and classification results without unsupported causal claims
  • Produce decision briefs, dashboard specifications and action plans from workforce findings

Benefits of attending

For you

  • Produce defensible workforce analyses for senior HR and business stakeholders
  • Recognise weak metrics, biased comparisons and unsupported causal claims
  • Use behavioural evidence more consistently in performance and talent decisions
  • Assess attrition, performance and capability patterns using repeatable methods
  • Build a practical 90-day plan for applying HR analytics at work

For your organisation

  • Improves consistency of workforce metrics used in management reporting
  • Reduces bias risk in performance, selection and talent review decisions
  • Identifies retention and capability issues before they become operational problems
  • Strengthens evidence behind workforce planning and talent investment choices
  • Creates clearer accountability for HR data ownership, quality and access

Course modules

10 modules over 5 days

Participants can define governed workforce datasets that support reliable HR analysis.

  • People-data operating model: decisions, stakeholders, measures, cadence and ownership
  • HRIS, ATS, LMS and survey sources mapped to data fields
  • Data dictionary definitions for headcount, FTE, turnover and internal mobility
  • Unique employee identifiers, effective dates and job architecture reconciliation
  • Data quality checks for completeness, validity, consistency, timeliness and duplicates
  • GDPR purpose limitation, access control and retention requirements for people data

Participants can calculate workforce measures and segment them for management decisions.

  • Workforce segmentation by business unit, job family, location and tenure
  • Headcount bridge analysis separating hires, exits, transfers and structural changes
  • Turnover rates calculated with average headcount and population definitions
  • Absence metrics distinguished by frequency, duration, lost-time rate and reason
  • Span-of-control and layer analysis using manager-to-employee reporting ratios
  • Dashboard metric hierarchy linking workforce measures to management decisions

Participants can convert behavioural evidence into structured performance and talent data.

  • ABC analysis identifies antecedents, observable behaviours and consequences
  • Behavioural event coding converts observations into consistent analytic categories
  • Behaviourally anchored rating scales define performance evidence at each level
  • Competency models distinguish capability, behaviour, results and potential
  • Structured interview scorecards use behavioural indicators and anchored response criteria
  • Rater calibration detects halo effects, recency bias and leniency patterns

Participants can assess the quality and limitations of workforce survey and assessment data.

  • Reliability assessed through inter-rater agreement, internal consistency and test-retest evidence
  • Validity evidence examined: content, construct, criterion and consequential validity
  • Likert-scale survey items written without double-barrelled or leading wording
  • Survey response rates, missing data and non-response bias assessed
  • eNPS interpreted alongside engagement drivers, comments and participation patterns
  • Measurement invariance considered before comparing groups across demographic categories

Participants can analyse performance processes, ratings and talent decisions using evidence.

  • Performance management logic model links inputs, behaviours, outputs and outcomes
  • Goal quality reviewed using SMART criteria and line-of-sight tests
  • OKR alignment analysis distinguishes committed objectives from aspirational outcomes
  • Performance distribution analysis tests rating concentration, drift and manager effects
  • Nine-box matrices separate performance evidence from future potential indicators
  • Calibration meeting protocols document evidence, challenge ratings and record decisions

Participants can identify fairness, privacy and ethical risks in workforce analysis.

  • Adverse impact analysis applies the four-fifths rule to selection outcomes
  • Disparate impact ratios compared across protected groups and decision stages
  • Confounding variables separated from demographic effects using stratified comparisons
  • Data minimisation limits collection to stated workforce decision purposes
  • Explainability requirements documented for automated screening and performance recommendations
  • Ethics review considers surveillance, consent, dignity and foreseeable harms

Participants can interpret common statistical outputs and state their decision limits.

  • Correlation coefficients interpreted without claiming causation or individual prediction
  • Cross-tabulations test workforce patterns by population, period and business unit
  • Confidence intervals communicate uncertainty around survey and turnover estimates
  • Regression models estimate relationships while controlling specified explanatory variables
  • Classification metrics compare precision, recall, false positives and false negatives
  • Model validation separates training, testing, drift monitoring and recalibration

Participants can frame and evaluate attrition and retention analyses for action.

  • Attrition risk question framed around decision, population, horizon and intervention
  • Survival analysis tracks time-to-exit across cohorts and employment milestones
  • Driver analysis combines quantitative patterns with employee listening evidence
  • Risk scores prioritised by actionability, fairness, confidence and intervention capacity
  • Retention interventions evaluated through comparison groups and pre-post measures
  • Flight-risk dashboards avoid individual labelling and unsupported causal claims

Participants can translate workforce data into capability, capacity and supply-demand scenarios.

  • Strategic workforce planning translates demand scenarios into capability and capacity gaps
  • Supply analysis models internal mobility, retirement, attrition and hiring pipelines
  • Scenario planning compares base, constrained-growth and transformation workforce assumptions
  • Build-buy-borrow-bot decisions assessed against cost, speed, risk and capability
  • Skills taxonomies connect role requirements, proficiency levels and learning records
  • Workforce planning dashboards show gaps, actions, owners and review dates

Participants can communicate workforce findings and prepare a focused workplace application plan.

  • Decision briefs frame workforce findings, options, trade-offs and recommended action
  • Executive charts select comparisons, annotations and scales that prevent misinterpretation
  • Data storytelling separates evidence, inference, assumption and decision request
  • Action plans assign interventions, owners, milestones, measures and review cadence
  • Stakeholder maps identify decision rights, data users and escalation routes
  • Personal application plan prioritises one workforce question and 90-day actions

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Workforce metric dictionary template
  2. People-data quality control checklist
  3. Behavioural evidence coding framework
  4. Performance calibration analysis model
  5. Fairness and privacy review checklist
  6. 90-day HR analytics application plan

Who should attend

  • HR Business Partners — need evidence for talent, performance and workforce decisions
  • People Analytics Specialists — need disciplined methods for workforce data analysis
  • Talent Management Managers — need better measures for potential, mobility and succession
  • Performance Management Leads — need defensible rating, calibration and goal-setting analysis
  • Learning and Development Managers — need to connect capability data with workforce outcomes
  • HR Directors — need clearer workforce insight for executive and board discussions

Skills and tools covered

Workforce data governanceBehavioural performance analysisTalent metric designStatistical interpretationFairness risk assessmentWorkforce decision storytelling

Tools and standards

ABC analysis, Behaviourally Anchored Rating Scales, four-fifths rule, GDPR

How the course runs

Training methodology

The course uses short instructor briefings followed by case work based on workforce reporting, performance ratings, employee surveys, attrition patterns and talent decisions. Participants analyse sample datasets, compare interpretations in groups and work through decision simulations involving rating calibration, retention risk and workforce scenarios. Facilitated discussion tests assumptions, ethical implications and communication choices. Application planning helps participants select priority workforce questions, define measures and identify stakeholders, actions and review points for use after the course.

Requirements and prerequisites

Participants should have practical experience in HR, talent management, performance management, workforce planning or HR reporting. They should be comfortable reading tables, percentages, averages and basic charts, and should understand common HR terms such as headcount, turnover, absence, performance ratings and employee surveys. Prior use of Excel or a reporting dashboard is helpful. Statistical modelling, programming, data science qualifications and specialist HR analytics software are not required. The course explains analytical concepts before applying them to workforce decisions.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should have experience in an HR, talent, performance, learning or workforce planning role, or regularly work with HR reports. You do not need programming, advanced statistics or a data science background.

Bring a laptop for reviewing datasets, templates and case materials. You may also bring examples of non-confidential workforce reports or questions from your organisation for use during application planning.

The course is designed for HR business partners, people analytics specialists, talent managers, performance management leads, learning leaders and HR directors. It is most useful for professionals who need to turn workforce data into management action.

This course applies analytical methods specifically to employee behaviour, talent, performance, attrition, fairness and workforce planning. It addresses HR data definitions, behavioural evidence, privacy obligations and the decision limits of people analytics.

You will use reusable templates to define a workforce question, select appropriate measures, test data quality and assess fairness risks. The final application plan identifies a priority analysis, stakeholders, actions and review points for the following 90 days.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The programme progresses from data foundations and metrics through behavioural analysis, statistical interpretation, workforce planning and decision communication.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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