Introduction
Data are recognized as a strategic asset for organizations. However, valuing data remains one of the central challenges in data management, due to its inherently multidimensional nature (encompassing financial, content-based, and functional aspects) and its strong context-dependence across industries.
This study focuses on information services (including data analytics, information consulting, and digital health) and aims to conduct a systematic analysis of the existing literature to identify data valuation frameworks within this domain and to explain their relationship with cost of information services.
Research Questions
1. What criteria determine the value of data in information services?
2. How can the value of data in these services be quantitatively measured?
3. How is the relationship between data value and the costs of information services described in the scientific literature, and what factors influence this relationship?
Literature Review
The existing body of literature on data valuation demonstrates that diverse approaches have been developed to assess the value of data in information services, which can be broadly categorized into three dimensions: financial, content-based, and functional. Financial models focus on production, storage, and transaction costs; content-based models emphasize data quality, freshness, rarity, and entropy; and functional models address operational utility and the role of data in decision-making and innovation processes. Several studies (e.g., Mendizabal-Arrieta et al., 2023; Lim et al., 2018) underscore the necessity of integrating these dimensions and employing hybrid models to achieve greater flexibility.
A systematic review of 24 selected studies, conducted using the PRISMA methodology, revealed that there is no single universal model for data valuation. Instead, the choice of an optimal model should be contingent on industry characteristics, data type, organizational technological maturity, and legal requirements. Market-based, income-based, and AI-driven approaches have emerged as complementary methods capable of addressing the shortcomings of traditional cost-based models.
Key challenges identified include conceptual heterogeneity (context-dependent definitions), computational limitations (complexity of measuring quality and entropy), and weaknesses in comprehensive modeling (lack of simultaneous coverage of financial, qualitative, and functional dimensions). The reviewed research further reports a positive and significant relationship between data value and the cost of information services, influenced by factors such as data update frequency, secure sharing capabilities, and the impact on customer decision-making. These findings highlight the necessity of developing hybrid, context-specific, and multidisciplinary frameworks for data valuation within information service chains.
Methodology
This study adopted a theoretical-analytical approach, utilizing library-documentary research and systematic literature review methods. Following the PRISMA methodology, we identified 138 relevant studies from reputable academic databases (Scopus, ScienceDirect, IEEE) published between 2008 and 2024. After rigorous screening, 24 articles met the inclusion criteria for systematic data extraction and analysis.
Studies were selected based on the following criteria: Relevance to data valuation in information services; inclusion of a pricing model or cost-value analysis; and publication in peer-reviewed journals.
Results
The findings of this study revealed three key dimensions of data valuation: (1) the financial dimension (production and exchange costs), (2) the content-based dimension (data quality and entropy), and (3) the functional dimension (operational utility). These dimensions have been incorporated in various ways across different valuation models, including cost-based models, market-based models, income-based models, and others.
Synthesis of the selected studies (n=24) indicated a reported positive relationship between intrinsic data value and information service costs in 75% of the studies (18 out of 24). A structured and methodological integration of findings from various studies showed that cost-based models were used in 9 studies, while market-based models were employed in 7 studies. The financial dimension was present in nearly all models, while the functional dimension was considered in 17 studies. Hybrid models demonstrated the best performance in 60% of cases, typically combining factors such as data quality and production costs.
Sectoral analysis revealed distinct patterns: in the healthcare industry, data value was primarily dependent on quality and privacy considerations, while in the financial sector, market-based models proved challenging due to price volatility. Consequently, hybrid models emerged as the most effective approach due to their superior flexibility.
Conclusion
This paper introduced a novel framework characterized by three key features: (1) a three-dimensional structure encompassing financial, content-based, and functional aspects; (2) parametric flexibility for industry-specific customization; and (3) compatibility with both structured and unstructured data types. Developed through a PRISMA-compliant systematic literature review, this framework established a comprehensive foundation for data valuation models in information services. Analysis of the 24 selected studies revealed that the healthcare sector demonstrated the highest valuation impact (85%).
The study proposed three strategic recommendations for enhancing data valuation:
• AI-driven real-time valuation for time-sensitive domains like cloud services requiring dynamic processing.
• Industry-specific standardization (e.g., GDPR-inspired frameworks in healthcare) to unify metrics and ensure regulatory compliance.
• Specialized training programs for data managers, analysts, and policymakers to improve understanding of data quality-cost interdependencies.
These integrated solutions can foster transparent, efficient, and scalable data valuation ecosystems.
Two primary limitations emerged: (1) predominant focus on structured data (≈80% of studies), resulting in insufficient attention to unstructured data, and (2) inadequate research on emerging markets.
Future studies should: (1) develop nonlinear models for multidimensional data complexity; and (2) explore blockchain applications to enhance valuation transparency. These approaches could address critical gaps in current literature.
The research established that effective information service data valuation requires multidimensional, industry-adapted approaches. Hybrid models (particularly AI-enhanced variants) showed significant potential in bridging theory-practice gaps. Findings offered actionable insights for data managers, service designers, and digital policymakers.