Infrastructure organizations invest heavily in data management, yet a gap persists between the data generated and its actual utilization, particularly in the construction sector. As digitalization progresses, concerns arise regarding the effective transformation of raw data into actionable knowledge. This study aims to develop a structured framework that evaluates current data utilization practices and enhances decision-making through improved integration strategies.
The study uses social network analysis and graph theory to examine the interconnections between data, information, knowledge and decisions. The framework is applied to preconstruction phase data from a National Highway Authority of India project to assess its practical viability.
The framework highlights inefficiencies in data management by mapping relationships within infrastructure projects. A key outcome is the Composite Integration Score (CIS), a metric that quantifies data utilization effectiveness, enabling organizations to optimize database management and improve decision-making.
Although the framework is validated using a specific case study, its applicability across different infrastructure projects requires further exploration. Future research can extend its use to diverse sectors and larger data sets.
The study provides organizations with a tool to evaluate data investments, refine knowledge management strategies and enhance information accessibility for decision-makers.
This research introduces a novel performance metric, CIS, providing a systematic method to assess and enhance data-driven decision-making. By offering a structured approach, it contributes to bridging the gap between data collection and its practical application in infrastructure projects.
