Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/22795
Autoria: Sousa, M.
Melé, P. M.
Pesqueira, A. M.
Rocha, Á.
Sousa, M.
Salma Noor
Data: 2021
Título próprio: Data science strategies leading to the development of data scientists’ skills in organizations
Volume: 33
Paginação: 14523 - 14531
ISSN: 0941-0643
DOI (Digital Object Identifier): 10.1007/s00521-021-06095-3
Palavras-chave: Data science
Pharma
Health sector
Big data
Skills
Data technostructure
Data management structure
Resumo: The purpose of this paper is to compare the strategies of companies with data science practices and methodologies and the data specificities/variables that can influence the definition of a data science strategy in pharma companies. The current paper is an empirical study, and the research approach consists of verifying against a set of statistical tests the differences between companies with a data science strategy and companies without a data science strategy. We have designed a specific questionnaire and applied it to a sample of 280 pharma companies. The main findings are based on the analysis of these variables: overwhelming volume, managing unstructured data, data quality, availability of data, access rights to data, data ownership issues, cost of data, lack of pre-processing facilities, lack of technology, shortage of talent/skills, privacy concerns and regulatory risks, security, and difficulties of data portability regarding companies with a data science strategy and companies without a data science strategy. The paper offers an in-depth comparative analysis between companies with or without a data science strategy, and the key limitation is regarding the literature review as a consequence of the novelty of the theme; there is a lack of scientific studies regarding this specific aspect of data science. In terms of the practical business implications, an organization with a data science strategy will have better direction and management practices as the decision-making process is based on accurate and valuable data, but it needs data scientists skills to fulfil those goals.
Arbitragem científica: yes
Acesso: Acesso Aberto
Aparece nas coleções:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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