Renaming data files involves systematically changing filenames to follow a consistent, meaningful structure. This improves dataset organization, simplifies data loading, aids reproducibility, and ensures clarity about file contents. Unlike random or unclear names (like image1.jpg or data_old.csv), good renaming uses descriptive elements such as data type, source, date, or labels within the filename itself following a pre-defined pattern, separating these elements consistently with underscores or hyphens.

Common practices include naming medical images like patientID_scanDate_anomalyPresent.jpg in healthcare AI, or timestamped sensor data like vehicleID_20240615T143000_frontCamera.avi for autonomous driving projects. Scripting tools like Python's os and pathlib libraries automate bulk renaming. Platforms like TensorFlow or PyTorch datasets also benefit from logically named files during the data loading stage.

Effective renaming prevents errors (like loading wrong data splits), enables automation (e.g., parsing labels from filenames), and boosts collaboration. However, establishing the naming convention takes initial effort and requires team-wide adoption. While not a replacement for proper metadata management, it’s a fundamental step in building reliable data pipelines, directly supporting FAIR (Findable, Accessible, Interoperable, Reusable) principles for machine learning data.