Data Digitization to Improve NSW Training Outcomes
Abstract
Reporting and clerical functions at Naval Special Warfare (NSW) Echelon IV commands are ripe for digitization, automation and optimization. While cost is a concern for prioritizing a more automated data system, there are potentially large benefits including the use of these data for prediction modelling. This study utilizes two unique digitalized NSW datasets to showcase how to use "big data" in the context of SEAL training and how it can be used to predict medical and performance fails during BUDS and passing various types of training evolutions (e.g., two mile swim, four mile run, etc.). Our main findings indicate higher probabilities for a medical fail to occur amongst males, Whites, and SEALS (both enlisted and officers). For performance fails, the results show higher probabilities for fails to occur amongst females, Blacks, Hispanics, and enlisted SEALs. As for passing evolutions, we find that individuals who are taller, older, lighter (in terms of weight), males, married, White, officers, and college educated are more likely to pass their evolutions. This study is just one example of how long-term efficiencies could be gained from greater automation of data through the use of simple software. Some data (such as those shown in this study) could provide long-term benefit if captured in a more persistent manner. We highly advocate the implementation of a more automated data/software collection system and the use of "big data for" NSW studies going forward in the near future.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 22, 2022
- Accession Number
- AD1189481
Entities
People
- Ryan J Sullivan
- Sawyer Rogers
- Simon Véronneau
- William Weldin
Organizations
- Naval Postgraduate School