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DATA-PUSHED PROJECTS: THE ROLE OF ANOMALIES TO BUILD DESIGN PROCESSES FOR SUBSEQUENT EXPLORATION

Published online by Cambridge University Press:  19 June 2023

Antoine Bordas ,
Pascal Le Masson  and
Benoit Weil
Antoine Bordas*
Affiliation:
Mines Paris, PSL University, Centre for management science (CGS), i3 UMR CNRS, 75006 Paris, France
Pascal Le Masson
Affiliation:
Mines Paris, PSL University, Centre for management science (CGS), i3 UMR CNRS, 75006 Paris, France
Benoit Weil
Affiliation:
Mines Paris, PSL University, Centre for management science (CGS), i3 UMR CNRS, 75006 Paris, France
*
Bordas, Antoine, Mines Paris, France, [email protected]

Abstract

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Data-pushed projects are common in companies and consist in the design of a model in order to deliver a desirable output. The design of data science models appears at the intersection of optimisation and creativity logic, with in both cases the presence of anomalies to a various extent but no clear design process.

This paper therefore proposes to study the possible design processes in data-pushed projects, highlighting distinct knowledge exploration logics and the role of anomalies in each. This research introduces a theoretical framework to study data-pushed projects and is based on design theory. Three case studies complete this theoretical work to examine each of the processes and test our hypothesis.

As a result, this paper derives three design processes adapted to data-pushed projects and put forward for each of them: 1) the various knowledge leveraged and generated and 2) the specific role of anomalies.

Keywords

Design theoryDesign processBig dataData-pushed projectsAnomaly management
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1137 - 1146
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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