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 |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
article_81756.pdf | Versão Aceite | 194,53 kB | Adobe PDF | Ver/Abrir |
Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.