In the digital economy, data serve as strategic assets that play a vital role in creating economic value. This study aims to systematically analyze and classify the existing literature to identify frameworks and models of data valuation in information services and to explain their relationship with the cost of information services across different industries using the PRISMA guideline. The research addresses three key questions: (1) What are the fundamental criteria for data valuation? (2) What are the quantitative methods for measuring data value in information services? (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?

A systematic literature review (PRISMA) of 138 studies (2008-2025) identified 24 articles for analysis. Findings reveal three data valuation dimensions: financial (production/transaction costs), content-based (quality/entropy), and functional (operational utility). More than 75% of the reviewed studies confirm a positive relationship between data value and service cost. Despite the diversity of measurement methods—particularly in entropy-based calculations—data quality has a significant impact on its value. Among the reviewed models, hybrid and AI-driven approaches show superior performance in multidimensional analysis of data value. The proposed framework, encompassing these three dimensions, provides a foundation for developing data valuation models in information services. The heterogeneity of existing approaches highlights the need for nonlinear models and future empirical studies. The identified patterns form a basis for designing data valuation models applicable to specific industries as well as broader economic contexts.