Big Data Science projects fail at an alarming rate despite significant organizational investments, with 85% failing to progress beyond the experimental stage. While existing research has extensively examined technical aspects such as algorithm development and data engineering, there is limited empirical evidence on institutional factors that influence project outcomes. The purpose of this quantitative, correlational study was to investigate how cultural differences, technological changes, and resource allocation predict success in Big Data Science projects. The research questions focus on understanding how technological changes, cultural differences, and resource allocation influence project success. Grounded in Attribution Theory and the CRISP-DM framework, this study employed a correlational research design using validated survey instruments. The researchers collected data from 102 Big Data Science professionals via SurveyMonkey, assessing cultural differences, technological readiness, resource allocation, and project success. The analysis results showed that all three institutional factors significantly predict project success. These findings underscore the importance of organizational and institutional factors beyond technical capabilities in achieving project success, emphasizing the need for integrated management approaches that address resource, technological, and cultural dimensions. This research provides evidence-based recommendations for practitioners seeking to improve data science project outcomes and maximize return on Big Data Science investments.
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