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Analytics in Human Resource Management The OpenSKIMR Approach
Author(s) -
Peter Mirski,
Reinhard Bernsteiner,
Dania Radi
Publication year - 2017
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2017.11.430
Subject(s) - computer science , matching (statistics) , order (exchange) , plan (archaeology) , analytics , resource (disambiguation) , visualization , knowledge management , imperfect , data science , artificial intelligence , computer network , linguistics , statistics , philosophy , mathematics , archaeology , finance , economics , history
Matching skill sets of individuals with highly demanded skill sets of jobs or occupations in the IT area is a great challenge – adding necessary learning items and visualizing the result is a very promising end to end approach. With the “Open Skill Match Maker” (OpenSKIMR) young people shall be able to plan and simulate their individual learning and career routes towards their desired career destination, like with classical route planning software. Using the ESCO, a multilingual classification of occupations, skills, competences and qualifications, will ensure a consistent understanding of the skills and qualification of the talents. This paper aims at showcasing the possibility of matching data about skills, learning items and job offers. It opens up the opportunity to simulate career paths through visualization in order to support decision making in a world of imperfect data and information and overall this project shall also support the Europe 2020 target for inclusive growth as it aims to motivate people in acquiring new digital skills.

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