Beyond technology: Management challenges in the Big Data era

Detalhes bibliográficos
Autor(a) principal: Francisco, Eduardo de Rezende
Data de Publicação: 2019
Outros Autores: Kugler, José Luiz, Kang, Soong Moon, Silva, Ricardo, Whigham, Peter Alexander
Tipo de documento: Artigo
Idioma: eng
por
Título da fonte: Revista de Administração de Empresas
Texto Completo: https://periodicos.fgv.br/rae/article/view/80770
Resumo: The ability of organizations to produce, collect, manage, analyze, and transform data has increased rapidly over the past decade (Delen & Zolbanin, 2018). This has resulted in significant new challenges regarding how data can be leveraged for improving business decisions and how this new scenario changes business processes and operations (Vidgen, Shaw, & Grant, 2017). The widespread adoption of advanced analytical methods (e.g., machine learning) has attracted significant interest (Gupta, Deokar, Iyer, Sharda, & Schrader, 2018; Vassakis, Petrakis, & Kopanakis, 2018) particularly because the required data storage and methods can be accessed remotely through web-based interfaces such as cloud services. This has resulted in an increased belief that businesses must actively engage with this technology to remain competitive. However, this Red Queen scenario comes at a cost as collecting, curating, and managing large datasets requires expertise and dedicated staff, often consuming resources that do not contribute to core business activities. Consider the fact that there is an increasing role for data scientists and data engineers, among others, within organizations (Davenport & Patil, 2012). Roles such as Chief Data Officer (CDO) and Chief Analytics Officer (CAO)are now commonplace within most organizations.
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spelling Beyond technology: Management challenges in the Big Data eraAlém da tecnologia: Desafios gerenciais na era do Big DataThe ability of organizations to produce, collect, manage, analyze, and transform data has increased rapidly over the past decade (Delen & Zolbanin, 2018). This has resulted in significant new challenges regarding how data can be leveraged for improving business decisions and how this new scenario changes business processes and operations (Vidgen, Shaw, & Grant, 2017). The widespread adoption of advanced analytical methods (e.g., machine learning) has attracted significant interest (Gupta, Deokar, Iyer, Sharda, & Schrader, 2018; Vassakis, Petrakis, & Kopanakis, 2018) particularly because the required data storage and methods can be accessed remotely through web-based interfaces such as cloud services. This has resulted in an increased belief that businesses must actively engage with this technology to remain competitive. However, this Red Queen scenario comes at a cost as collecting, curating, and managing large datasets requires expertise and dedicated staff, often consuming resources that do not contribute to core business activities. Consider the fact that there is an increasing role for data scientists and data engineers, among others, within organizations (Davenport & Patil, 2012). Roles such as Chief Data Officer (CDO) and Chief Analytics Officer (CAO)are now commonplace within most organizations.A capacidade das organizações de produzir, coletar, gerenciar, analisar e transformar dados aumentou rapidamente na última década (Delen & Zolbanin, 2018). Isso gerou novos desafiossignificativos em relação a como os dados podem ser aproveitados para melhorar as decisões de negócios e como esse novo cenário altera os processos e as operações de negócios (Vidgen, Shaw, & Grant, 2017). A adoção generalizada de métodos analíticos avançados (por exemplo, aprendizado de máquina) tem atraído bastante interesse (Gupta, Deokar, Iyer, Sharda, & Schrader, 2018; Vassakis, Petrakis, & Kopanakis, 2018), principalmente porque o armazenamento de dados e os métodos necessários podem ser acessados remotamente por meio de interfaces baseadas na web, como serviços em nuvem. Isso gerou uma crença crescente de que as empresas devem envolver-se ativamente com essa tecnologia para se manterem competitivas. No entanto, esse cenário de corrida da Rainha Vermelha (que pressupõe um desenvolvimento contínuo por parte das empresas) tem um custo, pois a coleta, a curadoria e o gerenciamento de grandes conjuntos de dados requerem experiência e uma equipe dedicada, o que, muitas vezes, consome recursos que não contribuem para as principais atividades do negócio. É preciso considerar também que cientistas de dados e engenheiros de dados, entre outros, cada vez mais exercem um papel relevante dentro das organizações (Davenport & Patil, 2012). Cargos como Chief Data Officer (CDO) e Chief Analytics Officer (CAO) agora são comuns na maioria das organizações.RAE - Revista de Administracao de Empresas RAE - Revista de Administração de EmpresasRAE-Revista de Administração de Empresas2019-12-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAvaliado por Paresapplication/pdfapplication/pdfhttps://periodicos.fgv.br/rae/article/view/8077010.1590/S0034-759020190603RAE - Revista de Administracao de Empresas ; Vol. 59 No. 6 (2019): november-december; 375-378RAE - Revista de Administração de Empresas; Vol. 59 Núm. 6 (2019): november-december; 375-378RAE-Revista de Administração de Empresas; v. 59 n. 6 (2019): novembro-dezembro; 375-3782178-938X0034-7590reponame:Revista de Administração de Empresasinstname:Fundação Getulio Vargas (FGV)instacron:FGVengporhttps://periodicos.fgv.br/rae/article/view/80770/77144https://periodicos.fgv.br/rae/article/view/80770/77145Copyright (c) 2019 RAE - Revista de Administração de Empresasinfo:eu-repo/semantics/openAccessFrancisco, Eduardo de RezendeKugler, José LuizKang, Soong MoonSilva, RicardoWhigham, Peter Alexander2020-01-09T19:29:47Zoai:ojs.periodicos.fgv.br:article/80770Revistahttps://rae.fgv.br/raeONGhttps://old.scielo.br/oai/scielo-oai.phprae@fgv.br||ilda.fontes@fgv.br||raeredacao@fgv.br2178-938X0034-7590opendoar:2024-03-06T13:06:04.113724Revista de Administração de Empresas - Fundação Getulio Vargas (FGV)true
dc.title.none.fl_str_mv Beyond technology: Management challenges in the Big Data era
Além da tecnologia: Desafios gerenciais na era do Big Data
title Beyond technology: Management challenges in the Big Data era
spellingShingle Beyond technology: Management challenges in the Big Data era
Francisco, Eduardo de Rezende
title_short Beyond technology: Management challenges in the Big Data era
title_full Beyond technology: Management challenges in the Big Data era
title_fullStr Beyond technology: Management challenges in the Big Data era
title_full_unstemmed Beyond technology: Management challenges in the Big Data era
title_sort Beyond technology: Management challenges in the Big Data era
author Francisco, Eduardo de Rezende
author_facet Francisco, Eduardo de Rezende
Kugler, José Luiz
Kang, Soong Moon
Silva, Ricardo
Whigham, Peter Alexander
author_role author
author2 Kugler, José Luiz
Kang, Soong Moon
Silva, Ricardo
Whigham, Peter Alexander
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Francisco, Eduardo de Rezende
Kugler, José Luiz
Kang, Soong Moon
Silva, Ricardo
Whigham, Peter Alexander
description The ability of organizations to produce, collect, manage, analyze, and transform data has increased rapidly over the past decade (Delen & Zolbanin, 2018). This has resulted in significant new challenges regarding how data can be leveraged for improving business decisions and how this new scenario changes business processes and operations (Vidgen, Shaw, & Grant, 2017). The widespread adoption of advanced analytical methods (e.g., machine learning) has attracted significant interest (Gupta, Deokar, Iyer, Sharda, & Schrader, 2018; Vassakis, Petrakis, & Kopanakis, 2018) particularly because the required data storage and methods can be accessed remotely through web-based interfaces such as cloud services. This has resulted in an increased belief that businesses must actively engage with this technology to remain competitive. However, this Red Queen scenario comes at a cost as collecting, curating, and managing large datasets requires expertise and dedicated staff, often consuming resources that do not contribute to core business activities. Consider the fact that there is an increasing role for data scientists and data engineers, among others, within organizations (Davenport & Patil, 2012). Roles such as Chief Data Officer (CDO) and Chief Analytics Officer (CAO)are now commonplace within most organizations.
publishDate 2019
dc.date.none.fl_str_mv 2019-12-05
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Avaliado por Pares
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dc.identifier.uri.fl_str_mv https://periodicos.fgv.br/rae/article/view/80770
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url https://periodicos.fgv.br/rae/article/view/80770
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dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv https://periodicos.fgv.br/rae/article/view/80770/77144
https://periodicos.fgv.br/rae/article/view/80770/77145
dc.rights.driver.fl_str_mv Copyright (c) 2019 RAE - Revista de Administração de Empresas
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2019 RAE - Revista de Administração de Empresas
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv RAE - Revista de Administracao de Empresas
RAE - Revista de Administração de Empresas
RAE-Revista de Administração de Empresas
publisher.none.fl_str_mv RAE - Revista de Administracao de Empresas
RAE - Revista de Administração de Empresas
RAE-Revista de Administração de Empresas
dc.source.none.fl_str_mv RAE - Revista de Administracao de Empresas ; Vol. 59 No. 6 (2019): november-december; 375-378
RAE - Revista de Administração de Empresas; Vol. 59 Núm. 6 (2019): november-december; 375-378
RAE-Revista de Administração de Empresas; v. 59 n. 6 (2019): novembro-dezembro; 375-378
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