Naomi Boyer

NB NAOMI BOYER POLICY ANALYST Case Study: Automating Data Reporting for UF/IFAS Extension PSP Packets The Challenge UF/IFAS Extension faced significant inefficiencies in managing data from over 2,100 Permanent Status and Promotion Packets (PSP). The manual data extraction and reporting process required extensive staff hours, was prone to human error, and caused delays in decision-making. With staff resources stretched thin, there was a need for a streamlined, automated solution to improve accuracy and save time while maintaining data integrity. Key Pain Points: Time-Consuming Process: Staff dedicated hundreds of hours each quarter to manually extract and input data. Increased Errors: The manual approach led to inconsistent reporting and occasional errors in sensitive documents. Scalability Concerns: As the volume of PSP packets increased, the current process became unsustainable. My Role To address these challenges, I took a leading role in designing and implementing an automated data reporting system. My responsibilities included: 1. Process Analysis and Optimization Conducted a comprehensive review of the PSP packets to identify repetitive patterns and high-priority sections (14–20, 24, 27– 28, and 35) for automation. Collaborated with subject matter experts to understand the data fields critical for reporting and ensure their accurate extraction. 2. Integration of AI-Based Tools Recommended and implemented AI-driven solutions to extract structured and unstructured data from PSP packets. Ensured the tools could handle various document formats and extract data with high accuracy. 3. Workflow Design and Automation Designed end-to-end workflows that automated data extraction and integration into Snowflake/OneLake for centralized storage and reporting. Developed a robust system for verifying data integrity and flagging anomalies during the automation process. 4. Team Collaboration and Training Worked closely with cross-functional teams, including IT, data analysts, and administrative staff, to implement the new system. Provided training and documentation to ensure smooth adoption and scalability of the automated process. The Results The efforts led to transformative improvements in the data reporting process, including: 1. Significant Time Savings Automated workflows reduced manual effort by approximately 150 hours per quarter, freeing up staff to focus on higher-value tasks. 2. Enhanced Accuracy The AI-based system minimized reporting errors by 30%, ensuring more reliable data for decision-making. 3. Improved Efficiency Enabled near-real-time data reporting, significantly improving the timeliness and effectiveness of administrative reviews and decisions. 4. Scalability and Sustainability The new system was designed to scale with future increases in PSP packet volumes, ensuring long-term operational efficiency. Key Takeaways This project demonstrated my ability to: Analyze complex workflows and identify opportunities for automation. Leverage AI and data integration tools to solve operational challenges. Collaborate effectively with diverse teams to implement and sustain innovative solutions. Deliver measurable results that align with organizational goals.