Case Studies

Challenge: Life sciences companies require access to large-scale, de-identified patient data to accelerate research and development. However, stringent privacy regulations (like HIPAA) and the technical complexity of de-identifying sensitive health information (FHIR data) at scale posed a significant barrier. The challenge was to unlock the value of this data for research while guaranteeing patient privacy and ensuring regulatory compliance.

Solution: As the Data Product Leader, I led a cross-functional initiative to build a suite of differential privacy tools. These tools were designed to de-identify sensitive FHIR data programmatically and at scale. I also developed a comprehensive metadata strategy to govern sensitive data management, enabling attribute-based access control that respected business agreements, patient consents, and data tenancy. This required aligning over 15 senior leaders from engineering, product, privacy, legal, and compliance on a unified strategy and an iterative delivery plan that met the needs of the sales pipeline.

Results: This initiative successfully enabled the creation of high-value, privacy-preserving research data products for life sciences customers. By navigating complex compliance and regulatory risks, we established a clear, secure, and scalable pathway for leveraging sensitive data in research, unlocking new opportunities for medical innovation.

Challenge: League faced significant inefficiencies in its data operations. The process of sharing data with customers was slow, taking 5-6 months to generate value. Data integration from various healthcare sources was a complex, manual, and time-consuming process. Furthermore, the platform lacked the deep personalization needed to drive user engagement effectively.

Solution: As Principal Product Manager, I spearheaded a multi-faceted data product strategy.

• Standardized Data Sharing: I led the creation of "Analytics Objects," a standardized product that harmonized internal, vendor, and customer data into ready-to-use formats.

• Scalable Integration: I designed and implemented productized, configuration-driven integration workflows and FHIR data conversion tools. This automated the ingestion and mapping of diverse healthcare standards (like FHIR and HL7), eliminating manual processes.

• AI-Powered Personalization: I pioneered the company's Generative AI strategy, launching a Personalization Data Store and leveraging Google Gemini to deliver "Next Best Action" services and personalized content.

Results: The impact of this data product overhaul was transformative across the organization and for our customers.

• Time-to-Value Reduced by 90%: The standardized "Analytics Objects" cut the time it took for customers to derive value from shared data from over 5 months to less than 1 month.

• Integration Timelines Slashed by 80-95%: The new automated workflows reduced the time for complex healthcare data integrations from months to mere days.

• Personalization Efficiency Boosted by 75%: The new personalization data store reduced the time to deploy targeted content from 4 weeks to just 1 week.

• User Engagement Increased by 20%: The Gemini-powered personalization services led to a significant lift in user engagement, earning recognition in Gartner’s Hype Cycle for Digital Care Delivery.

Challenge: As a Data Strategy Consulting Manager, I worked with large enterprise clients struggling to modernize their data infrastructure and unlock the value of their data assets. A Fortune 100 company faced high infrastructure costs and slow compliance responses. A major international bank needed to accelerate its machine learning development lifecycle and modernize HR analytics. A healthcare CRM provider sought to improve agent efficiency and reduce call times.

Solution: I led teams to deliver tailored, high-impact data solutions for each client.

• Modern Data Platform: For the Fortune 100 company, I defined the strategy, secured a $10M budget, and delivered a modern data platform that enabled self-service analytics and streamlined data governance.

• Data Science Acceleration: For the bank, I owned the end-to-end delivery of a Data Science Platform that made curated internal and third-party data available on demand, dramatically speeding up the ML model development process.

• AI-Powered CRM: For the healthcare client, I led a team of data scientists to build and deliver a "smart suggestions" feature that provided real-time guidance to customer service agents.

Results: The solutions delivered significant and measurable business value for each client.

• $2M Annual Savings & 90% Faster Compliance: The modern data platform saved the Fortune 100 client $2M per year in upfront infrastructure costs and reduced the time to respond to privacy and regulatory requests by 90%.

• 50% Faster ML Deployment: The Data Science Platform cut the production deployment time for machine learning models in half.

• 20% Reduction in Call Time: The "smart suggestions" feature reduced customer call time by 20% and increased the daily case closure rate by 50%, improving both customer satisfaction and operational efficiency.

Challenge: Google's strategic staffing process relied on planning models that often diverged from the actual time and resources used, leading to inefficiencies in resource allocation. There was a need for a data-driven tool to provide better insights and improve the accuracy of staffing plans.

Solution: During my internship as a Technical Account Manager, I was tasked with addressing this challenge. I defined the strategy, vision, and business case for a new strategic staffing Business Intelligence (BI) tool. I then managed the development of this tool, ensuring it met the needs of the stakeholders. After the initial launch, I conducted a tool adoption survey to gather user feedback and incorporated the findings to improve functionality and user experience.

Results: The new BI tool had a significant impact on the efficiency and effectiveness of the staffing process.

• Improved Planning Accuracy by 27%: The tool directly improved the metric tracking actual time used against planned time by 27%.

• Increased User Engagement by 70%: By actively incorporating user feedback, we successfully increased engagement with the tool by 70%, ensuring its widespread adoption and long-term value.