Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/69618 |
Resumo: | 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. |
id |
RCAP_8a414d5190cf72aa0c7dda3c25ce4db4 |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/69618 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profilingIEFPLong-term unemploymentData-driven decision-makingProfilingDomínio/Área Científica::Ciências Sociais::Economia e GestãoData-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.Zejnilovic, LeidRUNDaum, Thomas2019-05-14T13:36:10Z2019-01-252019-01-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/69618TID:202225194enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:33:02Zoai:run.unl.pt:10362/69618Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:34:59.953570Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling |
title |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling |
spellingShingle |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling Daum, Thomas IEFP Long-term unemployment Data-driven decision-making Profiling Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling |
title_full |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling |
title_fullStr |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling |
title_full_unstemmed |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling |
title_sort |
Human-machine systems vs. the unemployment spell: how IEFP embraced data-driven decision making with profiling |
author |
Daum, Thomas |
author_facet |
Daum, Thomas |
author_role |
author |
dc.contributor.none.fl_str_mv |
Zejnilovic, Leid RUN |
dc.contributor.author.fl_str_mv |
Daum, Thomas |
dc.subject.por.fl_str_mv |
IEFP Long-term unemployment Data-driven decision-making Profiling Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
IEFP Long-term unemployment Data-driven decision-making Profiling Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-05-14T13:36:10Z 2019-01-25 2019-01-25T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/69618 TID:202225194 |
url |
http://hdl.handle.net/10362/69618 |
identifier_str_mv |
TID:202225194 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799137971233882112 |