We study the dynamics underlying this process in a European postal-service organization. We develop a model that shows that the process of creating data-driven value propositions is emergent, consisting of iterative resourcing cycles. We find that creating data-driven value propositions involves the performance of two types of resourcing actions: data reconstructing and data repurposing. The process is shaped by two types of data qualities: apparent qualities, i.e., qualities perceived ex-ante as potentially significant for creating value propositions; and latent qualities, which raise unforeseen consequences en route. We discuss the implications of these findings for the literature on creating data-driven value propositions, for our understanding of data as a strategic resource, and for the literature on resourcing.
Resourcing with data: Unpacking the process of creating data-driven value propositions. WA Günther, MHR Mehrizi, M Huysman, F Deken.The Journal of Strategic Information Systems, 2022