Towards Effective Workforce Management: Hiring Algorithms, Big Data-driven Accountability Systems, and Organizational Performance Social Sciences, Sociology, Management and complex organizations

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their ‘depth’ and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote ‘blind’ big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.

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O fim da teoria: o confronto entre a pesquisa orientada por dados e a pesquisa orientada por hipóteses | The end of theory: the confrontation between data-driven research and hypothesis-driven research

Liinc em Revista ◽

10.18617/liinc.v15i1.4688 ◽

2019 ◽

Vol 15 (1) ◽

Author(s):

Luís Fernando Sayão ◽

Luana Farias Sales

Keyword(s):

Social Sciences ◽

Big Data ◽

Scientific Method ◽

Computational Analysis ◽

Data Driven ◽

Present Essay ◽

Contemporary Science ◽

The Social ◽

Analysis Strategies ◽

New Knowledge

RESUMO A ciência contemporânea e seus fundamentos metodológicos têm sido impactados pelo fenômeno do big data, que proclama que na era dos dados medidos em petabytes, de supercomputadores e sofisticados algoritmos, o método científico está obsoleto e que as hipóteses e modelos estão superados. As estratégias do big data científico confia em estratégias de análises computacionais de massivas quantidades de dados para revelar correlações, padrões e regras que vão gerar novos conhecimentos, que vão das ciências exatas até as ciências sociais, humanidade e cultura, delineando um arquétipo de ciência orientada por dados. O presente ensaio coloca em pauta as controvérsias em torno da ciência orientada por dados em contraposição à ciência orientada por hipóteses, e analisa alguns dos desdobramentos desse embate epistemológico. Para tal, tomo como metodologia os escritos de alguns autores mais proximamente envolvidos nessa questão.Palavras-chave: Big Data; Método Cientifico; Ciência Orientada por Dados; Ciência Orientada por Hipóteses.ABSTRACT Contemporary science and its methodological foundations have been impacted by the big data phenomenon that proclaims that in the age of data measured in petabytes, supercomputers and sophisticated algorithms the scientific method is obsolete and that the hypotheses and models are outdated.The strategies of the scientific big data rely on computational analysis strategies of massive amounts of data to reveal correlations, patterns and rules that will generate new knowledge, ranging from the exact sciences to the social sciences, humanity and culture, outlining an archetype of data-driven science. The present essay addresses the debates around data-driven science as opposed to hypothesis-oriented science and analyzes some of the ramifications of this epistemological confrontation. For this, the writings of some authors who are more closely involved in this question are taken as methodology.Keywords: Big Data; Scientific Method; Data-Driven Science; Hypothesis-Driven Science.

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