From Candidate Sourcing to Successful Placement: A Data-Driven Model for Matching Software Talent with Enterprise Skill Requirements

Background: The loss of information and fit across sourcing, assessment, matching, selection, and placement limits better match quality, shorter time-to-supply, and stronger placement durability.

Purpose: This conceptual review integrates research and institutional guidance published to date to explain the problem and develop an actionable framework for enterprise software staffing and digital talent platforms.

Method: The paper uses an integrative review organized around demand definition, evidence, interaction, decisions, transition, and feedback.

Results: The synthesis identifies six recurring requirements: Clear demand signals, job-relevant evidence, accessible communication, multidimensional matching, transition support, and governed learning. It proposes a five-stage model comprising Demand specification, structured evidence capture, Multidimensional matching, Human validation, Post-placement learning. The model pairs each stage with controls and measures, including demand stability, evidence quality, conversion, time to contribution, sustained performance, and fairness indicators.

Conclusion: Data-driven software talent matching should be managed as a connected socio-technical system. Organizations should pilot the model in a bounded role family, compare outcomes with a baseline, examine unequal effects, and revise the process before scaling.