Organic data (i.e., data that is generated or collected absent a systematic research design or plan) presents unique challenges and opportunity to organizational scientists. On the one hand, the processes by which data is generated and coded can be opaque and these data can show a low signal-to-noise ratio. On the other hand, the large volume of data, which is often collected unobtrusively, makes it possible to study complex systems that pose significant challenges to traditional data collection designs. The use of computational algorithms in the collection and coding of organic data and the challenges to drawing causal inferences from organic data is outlined. It is noted that coding data filtering and parameter tuning can introduce threats to the validity of inferences drawn from organic data. Potential solutions to these threats are discussed, and practical advice for using organic data and for structuring research programs to make better use of these data (e.g., by building multidisciplinary teams) is offered.
