From Knowledge Management to Data Management in Innovation: Organizing to Leverage Data Through Anomalies

Antoine Bordas (), Pascal Le Masson () and Benoit Weil ()
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Antoine Bordas: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique, Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres
Pascal Le Masson: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Benoit Weil: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique

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Abstract: ABSTRACT Owing to their wide accessibility, data are currently at the root of many opportunities and challenges. Among the opportunities is the value that data can generate for innovation and new product development. The managerial implementation of this so‐called value generation constitutes an associated challenge. This study addresses this issue with the goal of proposing a rationalisation of data management that allows organisations to leverage data for innovation. We draw upon knowledge management (KM) literature because data and knowledge have been closely linked for decades, as seen in the knowledge pyramid linking data to wisdom. Akin to KM practices, we begin by uncovering the four necessary dimensions that must be structured to leverage data (generation, relations, usage, and technologies). By recalling the singularities of data compared with knowledge, we hypothesise that anomalies play a potential role in operationalizing this framework for value generation. Evidence from several case studies support the proposed framework and lead us to introduce the notion of data heritage as a necessary condition for initiating a value generation process from data. Second, we highlight the importance of coupling between this data heritage and the knowledge heritage of organisations. Third, we emphasise that this coupling can be effectively realised through anomalies. Consequently, in response to recent calls in innovation management literature, this study provides insights into value generation from data by indicating several necessary conditions that need to be fulfilled. Moreover, it also benefits the KM literature by stressing the logic of a new KM pyramid.

Date: 2025-07-09
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Published in Creativity and Innovation Management, 2025, 36 (1), pp.1. ⟨10.1111/caim.70004⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05264557

DOI: 10.1111/caim.70004

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