TRANSFORMING DATA INTO ADDED-VALUE INFORMATION: THE DESIGN OF SCIENTIFIC MEASUREMENT MODELS THROUGH THE LENS OF DESIGN THEORY

Raphaëlle Barbier (), Pascal Le Masson () and Benoit Weil ()
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Raphaëlle Barbier: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Pascal Le Masson: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Benoit Weil: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique

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Abstract: Transforming data into added-value information is a recurrent issue in the context of "big data" phenomenon, as new sources of data become increasingly available. This paper proposes to offer a fresh look on how data and added-value information are linked through the design of specific models. This investigation is based on design theory, used as an analysis framework, and on a historical example in the Earth science field. It aims at unveiling the reasoning logic behind the design process of models combining data science and domain knowledge in specific ways, especially involving not only knowledge about the physical phenomena but also on the measuring instrument itself. More specifically, this paper shows how specific efforts on exploring the originality of the new instrument compared to existing ones can result in designing performant models to transform new sources of data into information. This also suggests several important competencies to be involved in the model-design process: (1) a detailed understanding of the limitations of existing models (2) the ability to explore both the originality of the new source of data compared to existing ones (3) the ability of leveraging independent data sources.

Date: 2021-08
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Published in Proceedings of the Design Society: International Conference on Engineering Design, 2021, 1, pp.3239-3248. ⟨10.1017/pds.2021.585⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03356306

DOI: 10.1017/pds.2021.585

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