Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation - Internal Processes Perspective

2025-12-11

AI Summary

In data-driven economies, organizations lack systematic frameworks for evaluating and managing data value within internal business processes. To address this gap, this study develops a comprehensive data value assessment framework grounded in the Balanced Scorecard’s internal process perspective, integrating three interrelated dimensions: data quality, governance compliance, and operational efficiency. It introduces a novel, multi-layered taxonomy of data value—spanning technological, organizational, and regulatory dependencies—that resolves metric redundancy and establishes cross-dimensional conceptual linkages. Through systematic literature review, theoretical modeling, indicator clustering, and taxonomy design, the research produces a scalable, reusable data value metrics system. This system underpins standardized data valuation models and decision-support systems, offering both a methodological foundation and actionable implementation pathways for cross-sectoral data assetization. (149 words)

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Humans and AI: Learning Human Values and Preferences
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Data valuation and monetisation are emerging as central challenges in data-driven economies, yet no unified framework exists to measure or manage data value across organisational contexts. This paper presents a systematic literature review of metrics and key performance indicators (KPIs) relevant to data valuation and monetisation, focusing on the Internal Processes Perspective of the Balanced Scorecard (BSC). As part of a broader effort to explore all four BSC perspectives, we identify, categorise, and interrelate hundreds of metrics within a comprehensive taxonomy structured around three core clusters: Data Quality, Governance & Compliance, and Operational Efficiency. The taxonomy consolidates overlapping definitions, clarifies conceptual dependencies, and links technical, organisational, and regulatory indicators that underpin data value creation. By integrating these dimensions, it provides a foundation for the development of standardised and evidence-based valuation frameworks. Beyond its theoretical contribution, the taxonomy supports ongoing practical applications in decision-support systems and data valuation models, advancing the broader goal of establishing a coherent, dynamic approach to assessing and monetising data across industries.

Problem

• Lacks unified framework for measuring data value across organizations
• Identifies metrics for data valuation from internal processes perspective
• Develops taxonomy linking technical, organizational and regulatory indicators

Innovation

• Systematic literature review of data valuation metrics
• Taxonomy structured around three core clusters
• Integrates technical, organizational, and regulatory indicators