HR analytics for evidence-based people management: A structured synthesis of methods, decision interfaces, and governance principles for sustainable organizations

Abstract

Purpose. This study synthesizes empirical and methodological evidence on human resource data analytics to provide a practical, decision-oriented blueprint for people management aligned with the United Nations Sustainable Development Goals (SDGs).

Methodology. An integrative, PRISMA-compliant systematic review of 20 highly relevant peer-reviewed sources published between 2021 and 2026 was conducted. The synthesis evaluates three core analytical workstreams – Employee Net Promoter Score (eNPS) monitoring, 360-degree feedback, and prescriptive recruitment optimization – mapping extracted evidence onto specific decision interfaces. Due to the methodological heterogeneity of the corpus, a structured narrative synthesis was utilized to evaluate the alignment of data-driven interventions with sustainability targets.

Results. Descriptive metrics such as eNPS function most effectively as continuous early-warning signals for retention intention rather than isolated diagnostic tools. Multi-rater 360-degree feedback yields reliable developmental signals exclusively when embedded in structured programs and structurally decoupled from compensation decisions. Furthermore, prescriptive optimization in recruitment generates measurable operational gains, particularly when algorithmic fairness mandates, budget constraints, and organizational capacity limits actively constrain predictive algorithms.

Theoretical contribution. The study advances a structured analytical pipeline that bridges the operational gap between technical predictive modeling and corporate social responsibility. It establishes ethical data governance and epistemic alignment as mandatory preconditions for deploying advanced people analytics, integrating previously fragmented literature on digital human resource transformation.

Practical implications. Organizations can implement the proposed five-stage blueprint - defining business objectives, specifying decision points, selecting minimal viable data, validating on decision horizons, and embedding governance rails – to convert raw workforce data into traceable, repeatable interventions. This framework directly supports the promotion of decent work (SDG 8), the reduction of algorithmic inequalities (SDG 10), and the establishment of transparent, accountable institutional data governance (SDG 16).

SDG 8: Decent Work and Economic Growth; SDG 10: Reduced Inequalities; SDG 16: Peace, Justice and Strong Institutions