Our increasing ability to analyze massive data sets offers unprecedented insights into human social behavior through the detection of patterns in human interaction at a large scale. Big Data network studies in particular are essential to understanding the structure and dynamics of social networks. However, partly due to the nature of available data, network science has focused on network structures, treating networks as something that people have. The goal of this talk is to introduce a practice perspective that defines organizational networks as something that people do, rather than have. Relational practices (anything we do that involves someone else) create and modify the networks individuals are embedded in. The core idea of a practice approach to the study of social networks is to examine through an interpretive, relational lens how and why individuals create, maintain and use networks. By zooming in and out of micro and macro levels of analysis, researchers gain an in-depth understanding of tie content and network dynamics. This focus on relational practices does not require the adoption of new research theories or methods. Rather, it aims at creating a common language to enable collaboration between researchers with different disciplinary and methodic backgrounds. Drawing on examples from our own research on networks of scientists, we show how to combine small data, and a qualitative, interpretive lens with quantitative network data. We propose that doing so can advance our understanding of complex systems.