Oravec et al 1) rightly bring up concerns about the quality of diagnosis codes that may impact the internal validity of studies using these data. Quality improvement efforts by the institutions that manage these databases, including the involvement of neurosurgeon input in database design, are one approach to address this issue. An opensource designation of neurosurgical diagnosis codes and relevant complications can also help standardize the way the field uses these datasets. Additionally, like other types of retrospective data, analysis drawn from administrative databases can be used for descriptive and inferential analysis, but cannot establish causality. This should be appropriately mentioned as a limitation in any paper that uses this type of data. These limitations, however, do not obviate the utility of administrative databases in answering particular types of questions and as hypothesis-generating tools. Descriptive and inferential analysis aside, the volume provided by administrative databases, which is unparalleled by any prospective dataset, is especially useful for the generation of predictive models. Ultimately, our goal is to highlight the fact that administrative databases are an example of only one type of data that falls within the umbrella of “Big Data,” and that we have only begun to scratch the surface of what the data science, powered by Big Data and artificial intelligence, can offer the neurosurgical community and our patients 2).