| Summary: | This master’s project leverages process improvement methodologies to build a case for developing standardized and streamlined data systems for analyzing the environmental impacts of the U.S. Antarctic Program (USAP). As one of fifty-four signatories to the Antarctic Treaty, the United States has a responsibility to monitor environmental impacts of all activities associated with conducting research on the coldest and harshest continent in the world. Scientific and support activities affect the environment due to the presence of people in Antarctica and their use of machinery, vehicles and infrastructure. Over the last three decades, the amount of data required for environmental reporting has increased, but the associated tools and resources needed to process and manage data have not evolved at the same rate and are now outdated. As a result, most of the data collected over the years are not accessible or saved in useful formats. The purpose of my master’s project is to implement process improvements to better organize and streamline data management. In October 2020, I started by investigating USAP environmental reporting data lifecycles, and then identified process improvements with objectives to: 1) produce higher quality and accessible data for analysis; and 2) to reduce resources required to manage data by streamlining procedures. This paper discusses data improvement projects, which are currently ongoing. These projects included three main phases—data standardization, improving data management, and improving data reporting. The time and resources needed to evaluate data management processes and data lifecycles are not insignificant, but the outcomes of these projects are already resulting in reduced processing time and touchpoints, and improved data quality and reporting. Standardizing environmental monitoring data collection and data management will lead to accessible and accurate data for analysis in the future. Currently, however, these data are not accessible, organized or clearly understood. With clean, ...
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