Significant terms aggregation is a powerful data analysis tool that identifies terms unusually common within a specific dataset compared to a broader background, enabling the discovery of unique insights. This technique is likened to the Predator's thermal vision, highlighting anomalies by observing term frequency differentials rather than heat. Various applications include detecting geographic anomalies in crime data, root cause analysis in fault reports, training classifiers for document categorization, identifying mis-categorized content, detecting credit card fraud, and generating product recommendations. The significant terms aggregation focuses on the "uncommonly common" rather than the most prevalent terms, thus providing more insightful results, such as identifying unusual crime patterns near airports or recurring themes in movie recommendations based on shared audience preferences. This method allows users to filter out noise and hone in on meaningful connections, making it a valuable tool for data-driven decision-making and enhancing the analytic capabilities of data systems.