【Definition】Data quality management typically refers to a set of practices and methodologies in data science centered around metrics frameworks, analytical methods, and predictive modeling. To understand it effectively, first identify the specific business problem to solve, then assess your own data, processes, and team capabilities before adopting any external solution.

[Key Practices] Implementing data quality management typically involves three steps: First, assess the gap between current state and targets to define measurable metrics. Second, select a minimal viable scope for a pilot, establishing the process, responsible parties, and tools. Third, adjust the pace based on pilot results before gradually expanding coverage. Maintain complete records throughout to support review and audit.

Common pitfalls include: treating data quality management as a one-time project without ongoing operations; setting overly broad metrics that make it impossible to gauge effectiveness; and overlooking compliance and data security boundaries. We recommend assigning an owner for each process step and establishing regular review checkpoints.

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