The project involved a pilot study for the implementation of Data Analytics methodologies on a major aviation infrastructure program using the LaGuardia Runway Extension and Engineered Materials Arrestor System (EMAS) project as the case study. The study evaluated how text mining, sentiment analysis, and probabilistic analysis could be applied to large volumes of project documentation generated under a fast-track Design-Build delivery model to identify project risks, change trends, claims exposure, and areas of concern in real time, improving visibility into project performance and decision-making.