Modeling data value in enterprise architectures: a systematic literature review

Abstract

Enterprises increasingly recognize data as a pivotal asset, yet understanding the complex and interdisciplinary nature of data valuation remains a challenge. This paper conducts a systematic literature review, linking data value drivers and approaches to enterprise architecture layers (business, data, application, technology) using The Open Group Architecture Framework (TOGAF) standard. Out of 102 papers, this study identifies seven core data valuation approaches and seven central data value drivers, emphasizing business utility and use case, costs, data security and privacy, and data quality. The findings reveal the pervasive impact of data value across all enterprise architecture layers. The paper consolidates these insights into a conceptual model, establishing a foundation for cross-domain research and practical data valuation solutions for professionals in real-world and academic settings.