Research on faultlines—hypothetical dividing lines splitting a team into homogeneous subgroups based on team members’ attributes—has produced several meta-analyses, reviews, and algorithms for faultline and subgroup detection. To help navigate this complexity, we summarize the current theories underlying faultline and subgroup research. We also compare the two most recent algorithms for computational faultline/subgroup detection, offer a guideline for choosing adequate algorithms, and recommend measure combinations for future research. We further review empirical faultlines and subgroup research and show that different contextual factors exhibit a strong influence on the effects of faultlines and subgroups. We discuss the need for further theorization on faultlines that does not rely on attribute salience, which considers the number of aligning attributes and the consequences of faultlines at the subgroup level. We conclude considering new potential applications of the faultline construct.