This case study presents an account of using big data discovery technology on over 6 million unique patient charts to investigate the association between migraine headaches and dry eye disease. Through a review of the current literature, we determined the existence of a viable pathogenic relationship between migraine headaches and dry eye disease. Among a cohort of other studies who investigated a similar research question, the choice of undergoing the proposed retrospective review of aggregated and de-identified information using big data tools provided a larger and more generalizable sample as well as was easier to perform, safer for the patients, more flexible for the researcher, and provided statistically significant evidence behind the presence of an association. International Classification of Diseases-9 (ICD-9) and ICD-10 diagnosis codes were used due to being able to account for subjective measures, objective measures, and medical decision making over time. The presence of such a large data set allowed for usage of big data discovery technologies. The Informatics for Integrating Biology and Bedside interface was used to safely obtain aggregated and de-identified data from the Carolina Data Warehouse for Health which was then data cleansed, prepared, and mined with multivariate logistic regression to determine that migraine headaches are more likely to have comorbid dry eye disease compared with the general population.