In the age of digital transformation, enterprises are increasingly recognizing the value of external data, which originates beyond their four walls. Despite the growing number of datasets and their potential value, external data is sourced in an ad-hoc manner without clear guidelines. This leads to inconsistent sourcing decisions, characterized by a lack of clarity on the object of sourcing and the underlying data sourcing practices. Furthermore, the field of data sourcing lacks extensive research, necessitating urgent action within the Information Systems (IS) research community to bridge this gap. Considering the abovementioned research opportunities, this thesis – through three interrelated research streams – provides foundations for, analyzes, and improves data sourcing practices in the enterprise context. The contributions of the first research stream are an external data sourcing taxonomy (Essay 1), which informs sourcing decisions in an enterprise context, and a reference process to source and manage external data, which is accompanied by explicit prescriptions in the form of design principles (Essay 2). The second research stream proposes a use case-driven assessment of open corporate registers (Essay 3) and, building on the subsequent findings, a method to screen, assess, and prepare open data for use in support of companies’ open data activities (Essay 4). Finally, the third research stream reveals and elaborates on three data sourcing practices developed by companies in response to institutional pressures in the sustainability context (Essay 5). Thus, the outcomes of this thesis enable the transition from ad-hoc acquisition to well-informed, professional data sourcing approaches in the enterprise context.