Structured logging in Python applications can transform logs from a cryptic data dump to a rich, searchable, and actionable source of insight, enabling faster and more accurate automated processing and analysis. By structuring logs consistently, developers can extract specific pieces of information without needing to parse arbitrary text strings, making troubleshooting easier and more efficient. The `structlog` library provides a robust and flexible way to implement structured logging in Python applications, with built-in processors that add timestamps, format log entries, filter logs, or redirect logs to different targets. By combining structured logging with a powerful query language like NRQL, developers can analyze their application's behavior and usage patterns more effectively, leading to more informed decision-making and efficient problem-solving. With the right implementation of structured logging, teams can maximize their ROI by gaining deeper insights into user behavior, identifying errors more easily, analyzing performance metrics, and optimizing log management.