Data Science as a New Frontier for Design
This work aims to enhance data science methodologies for researchers and practitioners facing data deluge, but it is incremental as it applies existing design theories to a new domain.
The paper tackles the challenge of integrating design research theories and methods into data science to address data-related societal issues, using the Higgs boson detection challenge as a case study to demonstrate how design can improve innovation dynamics.
The purpose of this paper is to contribute to the challenge of transferring know-how, theories and methods from design research to the design processes in information science and technologies. More specifically, we shall consider a domain, namely data-science, that is becoming rapidly a globally invested research and development axis with strong imperatives for innovation given the data deluge we are currently facing. We argue that, in order to rise to the data-related challenges that the society is facing, data-science initiatives should ensure a renewal of traditional research methodologies that are still largely based on trial-error processes depending on the talent and insights of a single (or a restricted group of) researchers. It is our claim that design theories and methods can provide, at least to some extent, the much-needed framework. We will use a worldwide data-science challenge organized to study a technical problem in physics, namely the detection of Higgs boson, as a use case to demonstrate some of the ways in which design theory and methods can help in analyzing and shaping the innovation dynamics in such projects.