TALENT ANALYTICS AND WORKFORCE GOVERNANCE: BALANCING DECISION VALUE, FAIRNESS, PRIVACY, AND ACCOUNTABILITY

Abstract

Background: Organizations increasingly depend on talent analytics, yet visible activity does not by itself demonstrate reliable capability or sustainable value. Fragmented responsibilities, weak evidence, and locally optimized metrics can cause decisions about workforce governance to create unmanaged exposure elsewhere. Aim: This article develops a governance framework that connects talent analytics, workforce governance, and role clarity through five mutually reinforcing capabilities: inclusive participation, capability development, role clarity, fair accountability, and institutional learning. Method: The paper uses an integrative conceptual review. Established management research, professional standards, and institutional guidance are synthesized through construct clarification, mechanism mapping, risk-control analysis, and proposition development. It does not report respondents, sample statistics, or causal estimates. Results: The synthesis indicates that performance becomes more resilient when decision rights, data definitions, controls, escalation paths, and learning routines are designed as one management system. The proposed model links each capability to observable evidence and balanced indicators. Contribution: The article offers an auditable implementation sequence and propositions that can be tested in later empirical research.