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

Best Practices for Implementing Data Lineage: Step 1: Assess the gap between current state and goals, defining measurable metrics. Step 2: Pilot a minimum viable scope to establish processes, assign owners, and select tools. Step 3: Adjust the pace based on pilot results and gradually expand coverage. Maintain complete records throughout to support review and audit.

Common Pitfalls: 1) Treating data lineage as a one-time project without ongoing operations; 2) Setting overly broad metrics that make impact assessment difficult; 3) Overlooking compliance and data security boundaries. Recommendation: Assign an owner to each stage and establish regular review checkpoints.

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