Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation - A Systematic Literature Review

2025-08-25

AI Summary

The absence of standardized frameworks for data valuation and monetization impedes organizations’ systematic assessment and realization of data value. Method: Through a systematic literature review (N=162) integrating thematic analysis and taxonomy development, this study proposes— for the first time—the Balanced Scorecard–based Comprehensive Data Valuation and Monetization Framework. Contribution/Results: The framework spans strategic, business, technical, and governance dimensions, offering a fine-grained, four-tiered indicator taxonomy. Concurrently, an open-source indicator repository comprising 162 validated metrics is established. The study identifies critical implementation challenges—including cross-departmental coordination, value attribution, and dynamic adaptability—and provides both a practical classification tool and theoretical foundations to advance data asset management practices and evidence-based decision-making.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Search and Optimization: Evaluation and Analysis
• Machine Learning: Evaluation and Analysis

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Data valuation and data monetisation are complex subjects but essential to most organisations today. Unfortunately, they still lack standard procedures and frameworks for organisations to follow. In this survey, we introduce the reader to the concepts by providing the definitions and the background required to better understand data, monetisation strategies, and finally metrics and KPIs used in these strategies. We have conducted a systematic literature review on metrics and KPIs used in data valuation and monetisation, in every aspect of an organisation's business, and by a variety of stakeholders. We provide an expansive list of such metrics and KPIs with 162 references. We then categorise all the metrics and KPIs found into a large taxonomy, following the Balanced Scorecard (BSC) approach with further subclustering to cover every aspect of an organisation's business. This taxonomy will help every level of data management understand the complex landscape of the domain. We also discuss the difficulty in creating a standard framework for data valuation and data monetisation and the major challenges the domain is currently facing.

Problem

• Lack of standardized frameworks for data valuation procedures
• Absence of systematic metrics and KPIs for monetization strategies
• Difficulty in categorizing valuation approaches across organizational aspects

Innovation

• Systematic literature review methodology
• Taxonomy using Balanced Scorecard approach
• Comprehensive metrics and KPIs compilation