Resourcing with data: Unpacking the process of creating data-driven value propositions

•Data-driven value proposition creation is an emergent, iterative resourcing process.•Creating data-driven value propositions involves reconstruction and repurposing.•Latent data qualities surface en route that pose unforeseen consequences.•Latent qualities are created, contextual, and historically situated.•Actors uncover the boundaries of data in use. This paper examines how organizations create data-driven value propositions. Data-driven value propositions define what customer value is created based on data. 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.