This article presents the Event Data on Conflict and Security (EDACS) dataset, discusses the inherent problems of georeferenced conflict data, and shows how these challenges are met within EDACS. Based on an event data approach, EDACS contributes to the growing number of novel georeferenced datasets that allow researchers to identify causal pathways of violence and the dynamics of (transboundary) violence through spatiotemporal disaggregation. However, the unreflected use of any of these datasets will give researchers unjustified confidence in their findings, as the pitfalls are many and propagating errors can result in misleading conclusions. To identify and handle the different challenges to overall event data quality, we argue in favor of transparency in the data collection and coding process, to empower analysts to challenge the data and avoid cascading errors. In particular, we investigate how the choice of news sources, the handling of geographic precision, and the use of auxiliary data can bias event data. We demonstrate how the EDACS dataset design enables the analyst to deal with these issues by providing a set of variables indicating the news sources, possible sources of bias, and detailed information on geographic precision. This allows for a flexible use of the data based on individual analytical requirements.