Making the Case: Data Wonks
Businesses are drowning in data, but McDonough students are helping them harness it to build stronger organizations.
Data is ruling the world — and smart businesses are harnessing it to become more efficient, nimble, and profitable. But data collection is one thing — determining where it’s leading an organization is quite another. That’s why McDonough’s Master of Science in Business Analytics (MSBA) prepares the next generation of business leaders to create, share, and sustain value using data. For their capstone projects, the Class of 2024 worked with organizations to wrangle data into actionable information.
Amina Abacon, James Bamberger, Youngkwang (Wilson) Kim, Caroline King, and Michael Kong
The Client: U.S. Census Bureau, Center for Enterprise Dissemination, Disclosure Avoidance (CEDDA) Team
The Problem: To protect personal information while disseminating data to researchers, CEDDA hoped to implement data synthesis, a process to bring data together to analyze patterns, specifically in their microdata sets that cover relatively small geographic regions. CEDDA developed a Python package, CenSyn, to perform decision-tree data synthesis. They found, however, that CenSyn produced biased data in particular cases. The MSBA team sought to further define and mitigage the bias issues.
The Pitch: The MSBA team identified the strong underlying patterns within the Census data contributing to the biased output. They proposed CEDDA instead use a random forest model to introduce more variation into the synthesis process and ultimately reduce bias. The team also wrote code and built a framework for converting the current CenSyn model to fit their recommendations.
Brian Barragan, Paul Maiellano, Mathew Roth, Josh Ney, Taylor Schneider, Cynthia Xu
The Client: Deloitte Consulting
The Problem: Deloitte aims to balance data utility and privacy in the healthcare industry to ensure responsible handling of sensitive information. They saw an opportunity to develop robust systems to navigate data privacy regulations while maximizing dataset capabilities. Deloitte asked MSBA students for recommendations on how best to do that.
The Solution/Pitch: The MSBA team proposed a solution that anonymized personal medical data with advanced techniques to prioritize patient privacy while enabling accurate and efficient analysis of tools. The team recommended a digital software package that could successfully anonymize sensitive data. Integrating this package into their work allowed Deloitte to rigorously test various machine-learning models to validate their effectiveness while maintaining patient confidentiality.
Parker Shelton, Keith Howard, Heidi Nguyen, Anita Zeng, Gaurav Patanker
The Client: Georgetown’s lacrosse team
The Problem: The Georgetown lacrosse team faced issues with the reporting, storage, and processing of athlete data from practices and weight room sessions. The data lived in two different databases, and staff faced a time-consuming process to manually download the data from both sites, input it into Excel, and create the necessary charts to analyze and provide reporting for each player.
The Solution/Pitch: The MSBA team recommended a centralized Athlete Management System (AMS) that can pull data from both systems and automate data retrieval. By connecting the AMS to Tableau, the coaches and trainers could leverage visual dashboards capable of offering full reports on individual players and the team as a whole.
— Maureen Harmon
This story was originally featured in the Georgetown Business Spring 2024 Magazine. Download the Georgetown Business Audio app to listen to the stories and other bonus content.