Data-Push Projects and their Unique Feature: Managing with Anomalies

Data-push projects are defined as projects whose objective is to derive valuable insights from an initial database, through model design. They have become increasingly common with the ever-rising availability of data and encompass several other terms found in the literature. Even though there is a growing body of research on how to manage such projects, it is noted by both scholars and practitioners that this remains a main challenge. This paper therefore proposes to address this issue with brand new lenses, after gathering insights from the philosophy of mathematics. This literature is particularly relevant because mathematics is at the heart of data-push projects. Thanks to a longitudinal case study we have been able to demonstrate the role of anomalies, especially putting forward three dimensions in which they act as a resource for the management of data-push projects. As such this paper complements the literature in management of data-push projects, in the brand-new direction of anomalies, therefore opening the path for more investigations in this promising direction.