The past decade has witnessed a significant increase in emphasis on what was initially called “Big Data”, but has subsequently matured into the field of Data Sciences. The National Consortium for Data Science describes Data Science as “the systematic study of the organization and use of digital data in order to accelerate discovery, improve critical decision-making processes, and enable a data-driven economy”. A data science perspective has permeated nearly all of the STEM disciplines, although the social and behavioral sciences have been somewhat slower in adopting these new methodologies. Stereotypical sources of big data in the social sciences include Twitter feeds, Facebook posts, text messages, and Mechanical Turk. Other more biologically-based sources of complex data structures arise from genetically-informed measures, functional magnetic resonance imaging, electrophysiological responsivity, and extensive data obtained from wearable devices. Regardless of data source, the old adage “be careful for what you wish” well applies here: although we have long sought high dimensional data, the optimal measurement and modeling of these same data are vexing. To help explore these important issues, the latest issue of the APA journal Psychological Methods is entirely dedicated to the topic of big data. There are 11 excellent articles that address a myriad of topics in the data sciences ranging from theory-driven web scraping to structural equation modeling forests to finding unobserved structures in large data files. This is an exciting time to be conducting research in the social and behavioral sciences, and big data is an example of how we can continue to propel our science forward.