[Definition] Metadata management in data science typically refers to a set of practices and methodologies centered on metrics, analytical methods, and predictive modeling. To understand it effectively, first clarify the business problem you aim to solve, then evaluate your own data, processes, and team capabilities before adopting any external solutions.

[Key Implementation Steps] Metadata management is typically implemented in three stages: First, assess the gap between current status and goals to define measurable KPIs. Second, launch a pilot within a minimal viable scope to establish processes, assign responsibilities, and configure tools. Third, adjust the pace based on pilot data before gradually expanding coverage. Maintain complete documentation throughout for review and audit purposes.

Common Pitfalls: Treating metadata management as a one-time project without ongoing operations; setting overly broad metrics that make it impossible to gauge effectiveness; and neglecting compliance and data security boundaries. We recommend assigning an owner for each phase and establishing regular review checkpoints.

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