Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling

• Main Author: Daum, Thomas
• Publication Date: 2019
• Format: Master thesis
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10362/69618
• topic: IEFP; Long-term unemployment; Data-driven decision-making; Profiling; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

Data-driven decision making and well-developed analytical capabilities are generally perceived as fundamental for being a competitive organization nowadays. Nevertheless, especially publicly-led organizations show little agility towards technical advancement and face difficulties in developing necessary capabilities. The following case demonstrates how the Portuguese national body for employment and professional training, IEFP, engaged in a data-driven “profiling” model to combat long-term unemployment (LTU). The case walks the reader through the whole project-lifecycle, starting with IEFP´s previous touchpoints with data science over modeling and implementation of profiling, data curation, until managerial challenges which occurred along the way. The study reveals difficulties of a public organization linked to the usage of data-science and encourages students to look for ways on how to overcome those problems and push the progress forward.