This chapter presents a comprehensive taxonomy for assessing data quality in the context of data monetisation, developed through a systematic literature review. Organising over one hundred metrics and Key Performance Indicators (KPIs) into four subclusters (Fundamental, Contextual, Resolution, and Specialised) within the Balanced Scorecard (BSC) framework, the taxonomy integrates both universal and domain-specific quality dimensions. By positioning data quality as a strategic connector across the BSC's Financial, Customer, Internal Processes, and Learning & Growth perspectives, it demonstrates how quality metrics underpin valuation accuracy, customer trust, operational efficiency, and innovation capacity. The framework's interconnected "metrics layer" ensures that improvements in one dimension cascade into others, maximising strategic impact. This holistic approach bridges the gap between granular technical assessment and high-level decision-making, offering practitioners, data stewards, and strategists a scalable, evidence-based reference for aligning data quality management with sustainable value creation.

翻译：本章通过系统性文献综述，提出了一套用于评估数据货币化背景下数据质量的综合分类法。该分类法将一百余项指标与关键绩效指标（KPIs）整合至平衡计分卡（BSC）框架内的四个子集群（基础性、情境性、解析性与专业性），涵盖了通用性与领域特定的质量维度。通过将数据质量定位为连接BSC财务、客户、内部流程及学习与成长四大战略视角的纽带，本框架揭示了质量指标如何支撑估值准确性、客户信任、运营效率与创新能力。其相互关联的“指标层”确保单一维度的改进能联动影响其他维度，从而最大化战略效益。这一整体性方法弥合了细粒度技术评估与高层决策之间的鸿沟，为从业者、数据管理者及战略制定者提供了可扩展的、基于证据的参考框架，以推动数据质量管理与可持续价值创造的协同发展。