Data-pushed projects: the role of anomalies to build design processes for subsequent exploration
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author: Bordas, Antoine; Le Masson, Pascal; Weil, Benoit
Institution: Mines Paris, PSL University, Centre for management science (CGS), i3 UMR CNRS, 75006 Paris, France
Section: Design Methods
DOI number: https://doi.org/10.1017/pds.2023.114
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.