Putting Big Data to Work in Government: The Case of the United States Border Patrol

• 成果类型: Article
• 署名作者: Coulthart, Stephen; Riccucci, Ryan
• 署名单位: State University of New York (SUNY) System; University at Albany, SUNY; University of Arizona
• 刊物名称: PUBLIC ADMINISTRATION REVIEW
• ISSN/ISSBN: 0033-3352
• DOI: 10.1111/puar.13431
• 发表日期: 2022
• 页码: 280-289
• 关键词: PUBLIC-SECTOR; INNOVATION; POLICY

摘要

Investigating how the public sector adopts technologies to process and analyze very large datasets is crucial for understanding governance in the digital age. The authors of this article examine a large government agency, the United States Border Patrol (USBP), an organization that is in the early phases of building big data capabilities. They argue the wide-scale adoption of big data analytics will require trial-and-error processes coordinated by organizational leadership in partnership with front-line employees who make the technology relevant to their needs in the field. Absent engagement from both levels, organizations like USBP that face significant barriers to adoption (e.g., limited data science expertise) will struggle to leverage data at scale. The authors also extend the literature on big data in the public sector and provide a rich description of how factors, such as organizational leadership and resources, impact the innovation process.