In this paper, we developed a novel data collection and analysis approach that uses manual and automated processing of large datasets and combines them to offset the limitations of past research and practice when it comes to identifying actors in complex environmental governance systems. Our empirical context is wildfire governance in Colorado. Wildfire governance represents a pressing challenge for interested actors in the western United States. We characterize Colorado’s wildfire governance system by using big data triangulation techniques to identify key actors and their topical domains to reveal meaningful subgroups. Our results highlight the importance of understanding the pitfalls of each source of data and the potential benefits of combining them to gain a more complete understanding of the system. The true potential of multi-source analysis becomes apparent when one pays special attention to the degree of overlap that exists between sources. Each source reveals a unique makeup of actors (both in terms of individuals and organizational types) and different forms of engagement in the system. In addition to providing different sets of actors, we found evidence of different emergent topics in each data source.