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Data science as knowledge creation a framework for synergies between data analysts and domain professionals

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  • van der Voort, Haiko
  • van Bulderen, Sabine
  • Cunningham, Scott
  • Janssen, Marijn

Abstract

The road from data generation to data use is commonly approached as a data-driven, functional process in which domain expertise is integrated as an afterthought. In this contribution we complement this functional view with an institutional view, that takes data analysis and domain professionalism as complementary (yet fallible) knowledge sources. We developed a framework that identifies and amplifies synergies between data analysts and domain professionals instead of taking one of them (i.e. data analytics) at the centre of the analytical process. The framework combines the often-cited CRISP-DM framework with a knowledge creation framework. The resulting framework is used in a data science project at a Dutch inspectorate that seeks to use data for risk-based inspection. The findings show first support of our framework. They also show that whereas more complex models have a higher predictive power, simpler models are sometimes preferred as they have the potential to create more synergies between inspectors and data analyst. Another issue driven by the integrated framework is about who of the involved actors should own the predictive model: data analysts or inspectors.

Suggested Citation

  • van der Voort, Haiko & van Bulderen, Sabine & Cunningham, Scott & Janssen, Marijn, 2021. "Data science as knowledge creation a framework for synergies between data analysts and domain professionals," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s004016252100593x
    DOI: 10.1016/j.techfore.2021.121160
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    References listed on IDEAS

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    1. Janssen, Marijn & van der Voort, Haiko & Wahyudi, Agung, 2017. "Factors influencing big data decision-making quality," Journal of Business Research, Elsevier, vol. 70(C), pages 338-345.
    2. Martin Hilbert, 2016. "Big Data for Development: A Review of Promises and Challenges," Development Policy Review, Overseas Development Institute, vol. 34(1), pages 135-174, January.
    3. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    4. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    5. Georg Von Krogh & Johan Roos & Ken Slocum, 1994. "An essay on corporate epistemology," Strategic Management Journal, Wiley Blackwell, vol. 15(S2), pages 53-71, June.
    6. Ikujiro Nonaka & Georg von Krogh, 2009. "Perspective---Tacit Knowledge and Knowledge Conversion: Controversy and Advancement in Organizational Knowledge Creation Theory," Organization Science, INFORMS, vol. 20(3), pages 635-652, June.
    7. Jeffrey Saltz & Ivan Shamshurin & Colin Connors, 2017. "Predicting data science sociotechnical execution challenges by categorizing data science projects," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2720-2728, December.
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