Data Quality Taxonomy for Data Monetization

2025-09-30

AI Summary

Data quality assessment in data monetization remains fragmented and misaligned with value creation. Method: This study proposes an integrative data quality taxonomy grounded in the Balanced Scorecard (BSC), mapping over one hundred generic and domain-specific metrics—via systematic literature review and multidimensional KPI clustering—to the BSC’s four perspectives (financial, customer, internal processes, learning & growth), yielding a hierarchical framework comprising foundational, contextual, resolution, and specialized quality sub-dimensions. Contribution/Results: It innovatively positions data quality as a strategic connector within the BSC, enabling cross-dimensional alignment between technical evaluation and executive decision-making. Empirically grounded and extensible, the framework significantly improves data valuation accuracy, customer trust, operational efficiency, and innovation enablement—advancing data quality management toward sustainable value creation.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Search and Optimization: Evaluation and Analysis

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

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.

Problem

• Developing a taxonomy for data quality assessment in monetization contexts
• Organizing metrics into Balanced Scorecard framework for strategic alignment
• Bridging technical data assessment with high-level business decision-making

Innovation

• Develops a comprehensive data quality taxonomy
• Organizes metrics into four Balanced Scorecard subclusters
• Bridges technical assessment with strategic decision-making