METHODOLOGY FOR BUILDING SKILL MATRICES IN MODERN IT

Abstract

Drawing on fifteen rigorously vetted studies published between 2021 and 2025, this conceptual paper knits those silos together and distils a replicable, five-stage workflow for designing, auditing, and refreshing skill matrices in software-centric contexts. We traced evidence across agile scaling, cybersecurity, DevOps, and AI adoption, coded methodological commonalities, and abductively mapped them into sequential steps: strategic alignment, skill-universe definition, multimodal assessment, gap prioritisation, and continuous update. Unlike earlier reviews that merely catalogue tools, our approach explains when and why each design choice matters, linking matrix architecture to observed outcomes such as deployment velocity and retention lift. The argument balances theory and practice: it grounds design principles in capability theory, yet translates them into everyday HR actions—think heat-map visualisations or telemetry-assisted self-ratings—that busy engineers will actually tolerate. Limitations include reliance on secondary data and possible publication bias; nonetheless, the synthesis offers a sharper lens through which organisations may future-proof their workforce planning while opening avenues for fresh, primary research in subsequent field studies.