Title: Data Quality Taxonomy for Data Monetization Show Chinese title

Title: 数据质量分类法用于数据货币化

Abstract: 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. Show Chinese abstract

Abstract: 本章提出了一种全面的分类法，用于评估数据货币化背景下的数据质量，该分类法通过系统的文献综述开发而成。 将一百多个指标和关键绩效指标（KPIs）组织到平衡计分卡（BSC）框架内的四个子集群（基础、上下文、解析和专业）中，该分类法整合了通用和领域特定的质量维度。 通过将数据质量定位为平衡计分卡的财务、客户、内部流程和学习与成长视角之间的战略连接点，它展示了质量指标如何支撑估值准确性、客户信任、运营效率和创新能力。 该框架的相互关联的“指标层”确保一个维度的改进会传递到其他维度，从而最大化战略影响。 这种整体方法弥合了细粒度的技术评估与高层决策之间的差距，为从业者、数据负责人和战略制定者提供了一个可扩展的、基于证据的参考，以将数据质量管理与可持续价值创造相一致。