We live in an era of unprecedented explosion in data. Over the past 70 years, the doubling time of medical knowledge decreased from 50 years to just 73 days. Use of big data is becoming increasingly prevalent in the otolaryngology literature. Between 2005 and 2016, there was a tenfold increase in database publications and only 18% made clinical recommendations. Conclusions drawn based on analyses of big datasets are frequently difficult to interpret because of unaccounted confounding factors, bias and lack of actionable recommendations. With initiatives such as RegENT, big data will continue to play an important role in otolaryngology research and quality improvement efforts. New technologies, including artificial intelligence, will bring about new tools to process and incorporate vast quantities of data into otolaryngology practice. This panel will review the major datasets commonly used in otolaryngology research, including the National Surgical Quality Improvement Program (NSQIP). Data collection methods, quality assurance measures and limitations of these datasets will be discussed. Our panel will present common pitfalls of database research from the perspective of a clinician reader to address the fundamental question: “Does this study change my practice?” The panel will address the future of big data in otolaryngology, including the use of natural language processing and the machine learning to enable otolaryngologists to care for their patients using the best available evidence.