A Delphi-Driven Ontology for Integrating Big Data in Monitoring and Evaluation

The integration of M&E systems presents transformative opportunities for enhancing evidence-based decision-making. However, the absence of a standardized framework for managing and interpreting Big Data in M&E contexts hampers its full potential. This study addresses this critical gap by developing and validating a Big Data ontology tailored for M&E applications. Utilizing the Delphi method, the research engaged a panel of 14 experts from diverse fields, including M&E, Big Data analytics, and ontology development, to iteratively refine and validate the framework. The study's methodology follows a three-phase process: (1) constructing an initial ontology through an extensive literature review and conceptual analysis, (2) validating and refining the framework through multiple rounds of expert consensus, and (3) finalizing the ontology based on systematic feedback integration. Key components of the ontology include core classes such as Projects, Indicators, Measurements, Outcomes, and Big Data, structured to enhance data interoperability, scalability, and analytical precision within M&E practices. Validation results, measured using Kendall's coefficient of concordance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{W}=0.82)$</tex>, indicate strong expert consensus on the ontology's theoretical soundness, practical utility, and technical accuracy. This study makes significant contributions by providing a structured framework that standardizes Big Data integration in M&E, facilitating improved decision-making, transparency, and scalability. The findings offer valuable insights for policymakers, researchers, and practitioners, with implications for cross-sectorial applications and digital transformation in evaluation practices. Future research should explore empirical validation through case studies and adaptation of the ontology for broader domains beyond M&E.