Midi de la recherche SIO avec Aleksi Aaltonen

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Le Département de systèmes d’information organisationnels vous invite à une présentation du professeur Aleksi Aaltonen sur sa recherche Data Design in Practice. Une boîte à lunch sera offerte gratuitement aux personnes présentes.

La présentation se déroulera en anglais.

Résumé

Data Design in Practice

Management research typically assumes that data are ready-made resources that represent relevant facts about an organization or its environment. Yet, valuable data do not come into existence by themselves. Data are human-made material that could have been otherwise, or not existed at all. Information systems research has traditionally addressed the creation of data as a matter of conceptual modeling, while recent sociotechnical studies have focused on the production, sourcing, use, and reuse of actual data points. However, little attention has been paid to how data-producing arrangements and, by implication, data are designed in practice. We address this blind spot by conceptualizing data design as the process through which organizations configure data-producing arrangements that are used to produce data about events, entities, and people in the organization or its environment. Data design can be studied in terms of constitutive rules that stand for the ways in which a data-producing arrangement recognizes and allows to render a certain aspect of an organization or its environment as data. Building on a case vignette of a gender identity data project at a large U.S. university, we show how the reconfiguration of a data-producing arrangement to better support inclusion, compliance, and strategic goals of the organization could be analyzed in terms of six new constitutive rules. We further argue that data design can amount to data innovation if the new or improved data-producing arrangement uniquely enhances organizational value creation or enables new forms of interventions. The paper contributes a vocabulary for analyzing data design in terms of constitutive rules, and discusses the implications of data design for digital innovation, information quality, classification systems, and data justice.