Enterprise-Architecture-Data-Science Modeling Framework for Data Asset Valuation

Abstract

Organizations struggle to realize value from artificial intelligence investments, with 74-76% of companies failing to achieve significant returns. This research addresses two critical questions: how to integrate data science initiatives into enterprise architecture (EA) modeling to connect value realization, and how to assess data asset value in a structured manner. The paper discusses a comprehensive framework that combines EA principles with data science methodologies, incorporating TOGAF’s architectural layers with performance indicator ontologies. Using predictive maintenance in smart manufacturing as a demonstration case, the framework links Key Performance Indicators (KPIs) with data science model evaluation metrics through contingency table analysis. The approach enables organizations to quantify data asset value by comparing model performance against baseline scenarios, translating technical metrics like accuracy into business-relevant indicators such as maintenance costs. This integration provides a systematic methodology for valuing data assets and demonstrating the business impact of data science initiatives