A Framework for Data Valuation and Monetisation

2025-12-08

AI Summary

Organizations struggle to quantify the commercial value of data assets due to fragmented, siloed valuation approaches—divided across economic, governance, and strategic perspectives—and the absence of actionable mechanisms. This paper proposes an integrated data valuation framework that unifies these three perspectives using a Balanced Scorecard–inspired hybrid model. The framework combines qualitative scoring, cost-utility estimation, data quality indexing, and Analytic Network Process (ANP)-based multi-criteria weighting to enhance transparency and strategic alignment. Adopting a design science research methodology, it is iteratively refined through embedded industrial case studies. Empirical evaluation demonstrates that the framework significantly reduces subjectivity in valuation, improves the precision of mapping data assets to organizational strategic objectives, and supports diverse monetization pathways—including Data-as-a-Service (DaaS). It exhibits cross-industry applicability and robustness.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Humans and AI: Learning Human Values and Preferences
• Search and Optimization: Evaluation and Analysis

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

As organisations increasingly recognise data as a strategic resource, they face the challenge of translating informational assets into measurable business value. Existing valuation approaches remain fragmented, often separating economic, governance, and strategic perspectives and lacking operational mechanisms suitable for real settings. This paper introduces a unified valuation framework that integrates these perspectives into a coherent decision-support model. Building on two artefacts from the Horizon Europe DATAMITE project, a taxonomy of data-quality and performance metrics, and an Analytic Network Process (ANP) tool for deriving relative importance, we develop a hybrid valuation model. The model combines qualitative scoring, cost- and utility-based estimation, relevance/quality indexing, and multi-criteria weighting to define data value transparently and systematically. Anchored in the Balanced Scorecard (BSC), the framework aligns indicators and valuation outcomes with organisational strategy, enabling firms to assess monetisation potential across Data-as-a-Service, Information-as-a-Service, and Answers-as-a-Service pathways. Methodologically, the study follows a Design Science approach complemented by embedded case studies with industrial partners, which informed continual refinement of the model. Because the evaluation is connected to a high-level taxonomy, the approach also reveals how valuation considerations map to BSC perspectives. Across the analysed use cases, the framework demonstrated flexibility, transparency, and reduced arbitrariness in valuation, offering organisations a structured basis for linking data assets to strategic and economic outcomes.

Problem

• Integrates economic, governance, and strategic perspectives into a unified data valuation framework.
• Develops a hybrid model combining qualitative scoring, cost-utility estimation, and multi-criteria weighting.
• Aligns data valuation with organizational strategy to assess monetization potential across service pathways.

Innovation

• Unified framework integrating economic, governance, and strategic perspectives
• Hybrid model combining qualitative scoring, cost-utility estimation, and multi-criteria weighting
• Anchored in Balanced Scorecard to align valuation with organizational strategy