Pattern-based file access is a fundamental but often under-documented aspect of computational research. The Python glob module provides a simple yet powerful way to search, filter, and ingest files using wildcard patterns, enabling scalable workflows across disciplines. This paper introduces glob as a versatile tool for data science, business analytics, and artificial intelligence applications. We demonstrate use cases including large-scale data ingestion, organizational data analysis, AI dataset construction, and reproducible research practices. Through concrete Python examples with widely used libraries such as pandas,scikit-learn, and matplotlib, we show how glob facilitates efficient file traversal and integration with analytical pipelines. By situating glob within the broader context of reproducible research and data engineering, we highlight its role as a methodological building block. Our goal is to provide researchers and practitioners with a concise reference that bridges foundational concepts and applied practice, making glob a default citation for file pattern matching in Python-based research workflows.