Behavioral big data (BBD) captures human and social actions and interactions at a new level of detail. BBD studies and applications are quickly growing in popularity in industry as well as in academic research. “Behavioral” in BBD highlights its focus on human and social behavior, and “big” – the novelty of its scale.
The combination of ‘‘behavioral’’ and ‘‘big’’ creates challenges and opportunities for statisticians, data miners, and other data scientists since the great majority of researchers in these communities are not trained in the behavioral sciences and are therefore unfamiliar with study design, ethical conduct, and research methods for studies with human subjects. It also creates challenges for social and behavioral scientists who have classic statistical modeling training and experience.
Academic researchers are now using BBD in fields that earlier had small behavioral data—such as psychology, management, marketing, information systems, sociology, political science, education—as well as in fields that earlier dealt with big inanimate data—production, biology, and engineering. Based on BBD studies presented at conferences and published in journals, it appears that these two types of researchers are encountering new dilemmas and challenges. My article Research Dilemmas with Behavioral Big Data (Big Data, 2017, Vol 5 Issue 2) examines the new challenges and dilemmas behavioral researchers face in the new BBD era.
Scientific research aims at discovering scientifically valid regularities that generalize from the data sample to a population of interest. To achieve this, behavioral studies include four major elements: the researcher, the human subjects, the research question, and the data. In BBD studies, challenges arise due to the changes in relationships between these four elements. For example, the distance between the researcher and the human subject is typically much larger in BBD studies compared to “small behavioral data” studies. The figure highlights some of the main issues.
It is worth noting that the availability of behavioral big data is bringing closer different disciplines: behavioral sciences, medical and life sciences, data sciences, management, law, and even engineering. The challenges to each of these communities is slightly different (see, for example, my slides on BBD in Healthcare or on BBD and Quality Engineering), which means interdisciplinary collaborations and research are extremely important and useful.
Galit Shmueli, Ph.D., is the Tsing Hua Distinguished Professor at the Institute of Service Science, and Director of the Center for Service Innovation & Analytics at the College of Technology Management, National Tsing Hua University, Taiwan. Before joining NTHU, she was the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business, and tenured Associate Professor at University of Maryland’s Smith School of Business.