Unpacking human systems in data science innovations: Key innovator perspectives

Keyao Li and Mark A. Griffin

Technovation, 2023, vol. 128, issue C

Abstract: Despite optimistic forecasts, industry innovations in data science have extraordinarily high rates of failure. It is essential to minimise the failure of data science projects, for both businesses and data professionals. Human systems are critical to the success of data science innovations. However, the human aspects of innovation management are often neglected or omitted in most guidelines and frameworks for data science. This provides limited guidance about the necessary human conditions for successful data science innovations. In this article we address this concern by developing a systematic framework for understanding human systems that support data science innovations.

Keywords: Data science innovation; Human systems; Innovation management; Data scientist (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:techno:v:128:y:2023:i:c:s0166497223001803

DOI: 10.1016/j.technovation.2023.102869

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