DaVe: Data Value Evaluation Framework for Seamless Data Integration

Abstract

Effective data management is critical for organizations, yet integrating diverse data sources remains challenging. Data governance plays a key role in ensuring data quality, security, and consistency. Without governance, integration becomes complex, costly, and time-consuming. A crucial aspect of governance is the evaluation of data value, often done through market-based, economic, and dimensional models. However, existing dimensional frameworks lack formal validation, and tools for comprehensive evaluation of datasets and ontologies prior to integration are scarce. This paper introduces DaVe (Data Value Evaluation framework), a formal framework designed to assess the value of datasets and ontologies before integration, focusing on metrics like data quality, coherence, and consistency, while accounting for project-specific constraints. Built on an algebraic foundation, DaVe estimates the potential value of integrated datasets, reducing risks and speeding up the integration process. Its effectiveness is demonstrated through a meteorological case study, highlighting its ability to assist integrators in evaluating and comparing ontologies, with promising implications for future data-driven projects.

Keywords

• Data management
• data value assessment
• data value evaluation
• dimensional model
• ontology integration