HRM & Industrial Relations cheat sheet

Resource-Based View (RBV): a resource yields sustained competitive advantage only if it is valuable, rare, inimitable, and non-substitutable (VRIN); skilled, committed human capital can meet all four conditions.

Horizontal fit = internal alignment among HR practices (e.g., pay-for-performance paired with individual performance appraisal); vertical fit = alignment of HR practices with business strategy and external environment.

Miles & Snow typology: Prospector (innovation, flexible staffing, market pay), Defender (efficiency, internal development, fixed pay), Analyzer (mixed), Reactor (inconsistent, no coherent HR pattern).

Porter's generic strategies: cost leadership pairs with narrow jobs, efficiency-based pay and minimal training; differentiation pairs with broad jobs, developmental appraisal and market-leading pay.

Human capital theory: firms invest in training when expected marginal benefit exceeds marginal cost; general skills are portable and under-invested by employers, firm-specific skills are shared-cost.

AMO framework: performance = f(Ability, Motivation, Opportunity); HR systems must bundle practices affecting all three, since single practices show weak effects.

HRM is a line-manager responsibility with an HR-enabling function; devolution succeeds only when managers receive training, time, and clear accountability.

Sustainable HRM and the 'mutual gains' premise: high-involvement work systems can raise both firm performance and employee outcomes, but gains are not automatic and depend on voice mechanisms.

Demand forecasting techniques: trend analysis (extrapolate historical headcount), ratio analysis (sales per employee), scatter plots, managerial judgement, and Delphi method for expert consensus.

Supply gap formula: Net requirement = Forecast demand − Current headcount + Expected attrition (turnover, retirements, promotions out). Positive = shortage; negative = surplus.

Markov/transition-matrix planning: multiply current headcount by historical move probabilities (stay, promote, exit) to project future availability by level.

Yield ratios (applications→interviews→offers→hires) and time-to-fill (days from requisition approval to offer acceptance) are core recruitment efficiency metrics.

Utility analysis of selection: expected gain ≈ (validity × SD of job performance × selection ratio effect) × number hired × tenure − selection cost; validity coefficients above 0.5 are strong for structured methods.

Structured interviews and work samples generally show higher predictive validity than unstructured interviews; cognitive ability tests show high validity but adverse impact risk requires justification.

Realistic job previews reduce early turnover by lowering inflated expectations and enabling self-selection, without lowering acceptance rates substantially.

Employer branding and candidate experience affect offer-acceptance rates; recruiting source effectiveness should be tracked by post-hire performance and retention, not headcount alone.

Training needs analysis has three levels: organisational (strategy, resources), task/operational (KSA requirements of the job), and person (who needs what).

Kirkpatrick's four levels: reaction, learning, behaviour, results; level 3 transfer requires supervisor support, opportunity to apply, and accountability.

70-20-10 model: roughly 70% experiential, 20% social/coaching, 10% formal coursework; treat as a heuristic, not a strict empirical law.

Goal-setting theory (Locke & Latham): specific, difficult goals with feedback outperform vague 'do your best' goals; goal commitment and self-efficacy moderate the effect.

SMART goals: Specific, Measurable, Achievable, Relevant, Time-bound.

360-degree feedback is developmental when anonymous and multi-rater; using it for pay or promotion decisions reduces rater honesty and increases leniency/retaliation risk.

Forced ranking/distribution can raise legal and engagement risk; a calibration approach (cross-manager review of ratings) achieves consistency without rigid quotas.

Evaluation designs: randomised control vs. pre-post with control group vs. single-group pre-post; the single-group design cannot rule out maturation and history effects.

Job evaluation methods: ranking, classification/grading, factor comparison, and point-factor - point-factor is the most defensible because it scores compensable factors (skill, effort, responsibility, conditions) with weights.

Pay policy line: regress pay on job points; compa-ratio = actual pay ÷ midpoint of grade; range spread = (max − min) ÷ min; midpoint progression between grades typically 10–20%.

Equity theory (Adams): employees compare outcome/input ratios to referents; perceived inequity motivates reducing effort, demanding more pay, or leaving.

Expectancy theory: Motivation = Expectancy × Instrumentality × Valence; if any term is zero, motivation is zero.

Pay-for-performance design: merit pay, bonuses, gainsharing, profit-sharing, and stock options differ in line of sight, risk sharing, and retention power.

Total rewards model: compensation, benefits, work-life effectiveness, performance recognition, development, and career opportunity; cash alone rarely drives retention.

Benefits compliance anchors: pension/retirement plan rules, statutory leave minimums, and mandated social insurance contributions vary by jurisdiction and must be verified locally.

Pay transparency and pay-equity audits: unexplained pay gaps by protected characteristic create legal exposure; regression controlling for grade, tenure, and performance is the standard audit method.

Employment status tests: control test (direction and supervision), integration/organisational test (part of the business), and multiple/economic reality test (tools, risk, exclusivity, pay method).

Wrongful dismissal vs. unfair dismissal: the former concerns breach of contract; the latter concerns whether a statutory fair procedure and substantive reason existed.

Procedural fairness for misconduct dismissal generally requires: investigation, written allegation, opportunity to respond with representation, impartial hearing, reasoned decision, and right of appeal.

Protected characteristics typically include sex, race, age, disability, religion, pregnancy, and sexual orientation; discrimination may be direct, indirect, harassment, or victimisation.

Reasonable accommodation: an employer must adjust the workplace or role for a disabled employee unless doing so causes unjustifiable hardship.

Redundancy/retrenchment selection must use objective, verifiable, and consistently applied criteria; consultation obligations and notice/severance minima are statutory in most jurisdictions.

Record-keeping: keep recruitment, pay, and dismissal records for the statutory limitation period; documentation is the primary defence in tribunal claims.

Verify jurisdiction-specific thresholds (employer size for unfair dismissal rights, statutory notice weeks, minimum wage rates) against current legislation; these change frequently.

Unitarist, pluralist, and radical frames of reference: unitarism sees one source of authority and conflict as deviance; pluralism sees legitimate competing interests managed through bargaining.

Collective bargaining structures: single-employer, multi-employer/sectoral, and pattern bargaining; scope items are typically pay, hours, leave, and grievance procedure.

Industrial action typology: strike, work-to-rule, go-slow, overtime ban, lockout; legality depends on ballot requirements, notice periods, and essential-service rules.

Grievance procedure stages usually escalate: line manager → senior manager → HR/panel → final appeal; time limits at each stage prevent drift.

Discipline: progressive sanctions (verbal warning, written warning, final warning, dismissal) with warnings time-limited and recorded.

Employee voice mechanisms: works councils, joint consultative committees, union recognition, engagement surveys, and suggestion schemes; voice reduces exit (EVLN model: exit, voice, loyalty, neglect).

Psychological contract: unwritten reciprocal expectations; breach correlates with reduced commitment and citizenship behaviour, even where no legal contract is broken.

Collective agreement terms are typically incorporated into individual contracts and are enforceable; check whether the agreement is legally binding or a voluntary code.

Hierarchy of controls, most to least effective: elimination, substitution, engineering controls, administrative controls, personal protective equipment (PPE).

Risk assessment formula: Risk = Likelihood × Severity; residual risk is assessed after controls are applied.

Incident causation models: Heinrich's domino theory, Swiss cheese model (latent failures align with active failures), and Reason's model of organisational accidents.

Lagging indicators: lost-time injury frequency rate (LTIFR = lost-time injuries × 1,000,000 ÷ hours worked); leading indicators: near-miss reports, training completion, inspection closure rates.

Psychosocial hazards include excessive workload, low control, role ambiguity, bullying, and harassment; ISO 45003 provides guidance on managing psychological health at work.

Stress models: Karasek's demand-control model (high demand + low control = strain) and effort-reward imbalance.

Burnout dimensions (WHO/ Maslach): emotional exhaustion, depersonalisation/cynicism, and reduced personal accomplishment.

Return-to-work and reasonable accommodation: graduated hours, modified duties, and ergonomic adjustment reduce recurrence and support legal compliance.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Lewin's model: unfreeze → change → refreeze; force-field analysis maps driving vs. restraining forces to target the restraining forces.

Kotter's eight steps: urgency, coalition, vision, communicate, empower/remove barriers, short-term wins, consolidate, anchor in culture.

Bridges' transition model: ending, neutral zone, new beginning - change is situational, transition is psychological and slower.

Change curve (Kübler-Ross-derived): denial, resistance, exploration, commitment; communication and involvement shorten the resistance phase.

Resistance is diagnostic information, not automatically deviance; participation and perceived fairness increase acceptance (procedural justice effect).

Ethical frameworks: utilitarian (greatest good), deontological (duty/rights), virtue (character), and justice-based (distributive, procedural, interactional).

HR ethics decision rule: test a decision against legality, fairness to affected parties, transparency (would you disclose it publicly?), and reversibility.

Change failure correlates with poor communication, unclear vision, and insufficient resourcing; measure adoption, not just go-live.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Lewin's model: unfreeze → change → refreeze; force-field analysis maps driving vs. restraining forces to target the restraining forces.

Kotter's eight steps: urgency, coalition, vision, communicate, empower/remove barriers, short-term wins, consolidate, anchor in culture.

Bridges' transition model: ending, neutral zone, new beginning - change is situational, transition is psychological and slower.

Change curve (Kübler-Ross-derived): denial, resistance, exploration, commitment; communication and involvement shorten the resistance phase.

Resistance is diagnostic information, not automatically deviance; participation and perceived fairness increase acceptance (procedural justice effect).

Ethical frameworks: utilitarian (greatest good), deontological (duty/rights), virtue (character), and justice-based (distributive, procedural, interactional).

HR ethics decision rule: test a decision against legality, fairness to affected parties, transparency (would you disclose it publicly?), and reversibility.

Change failure correlates with poor communication, unclear vision, and insufficient resourcing; measure adoption, not just go-live.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.

Turnover rate = leavers during period ÷ average headcount × 100; separate voluntary from involuntary and regrettable from non-regrettable.

Cost per hire = (internal recruiting costs + external agency/advertising costs) ÷ number of hires in the period.

Absence rate = total absent days ÷ (average headcount × working days in period) × 100; Bradford factor = S² × D (S = spells, D = days).

Human capital ROI = (Revenue − (Operating expense − Total compensation)) ÷ Total compensation; revenue per FTE = Revenue ÷ FTE count.

Descriptive → diagnostic → predictive → prescriptive analytics maturity; correlation does not establish causation, so controlled or quasi-experimental designs are needed for causal claims.

Data governance essentials: lawful basis for processing employee data, purpose limitation, retention limits, access controls, and anonymisation/pseudonymisation for analytics.

HRIS/HRMS architecture: core record (HRIS), payroll, time and attendance, talent modules, and analytics layer; integration quality determines data reliability.

Selection-ratio and validity generalisation logic underpin predictive hiring analytics; algorithmic tools require bias testing and human review to satisfy fairness obligations.