Abstract

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.

Details

Title
DATA-PUSHED PROJECTS: THE ROLE OF ANOMALIES TO BUILD DESIGN PROCESSES FOR SUBSEQUENT EXPLORATION
Author
Bordas, Antoine 1 ; Pascal Le Masson 1 ; Weil, Benoit 1 

1  Mines Paris, PSL University, Centre for management science (CGS), i3 UMR CNRS, 75006 Paris, France 
Pages
1137-1146
Section
Article
Publication year
2023
Publication date
Jul 2023
Publisher
Cambridge University Press
e-ISSN
2732-527X
Source type
Conference Paper
Language of publication
English
DOI
https://doi.org/10.1017/pds.2023.114
ProQuest document ID
2886572021
Copyright
The Author(s), 2023. Published by Cambridge University Press. This work is licensed under the Creative Commons  Attribution – Non-Commercial – No Derivatives License 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. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.