09-19, 15:35–16:10 (Europe/Amsterdam), Escher
Data scientists often encounter challenges related to sparse, privatized, or low-quality data. This talk will explore how synthetic data is changing problem-solving in markets with data scarcity, drawing from my experience in the African market, particularly in Kenya.
I will demonstrate how synthetic data has been utilized to develop models for proactive healthcare solutions.
The methodologies and lessons learned from these applications provide valuable insights that could inspire approaches in other markets, including Europe.
Data scientists frequently face significant challenges when dealing with sparse, privatized, or low-quality data. These challenges are particularly acute in regions with less developed data infrastructure, where collecting comprehensive and high-quality datasets is often difficult. This talk will delve into how synthetic data is transforming problem-solving in such data-scarce markets.
Drawing on my experience working within the African market, and specifically in Kenya, I will illustrate how synthetic data has been a crucial tool in overcoming data limitations. We will explore how we used synthetic data to simulate local health trends accurately, providing a richer and more comprehensive dataset than what is typically available. By simulating various health scenarios, we can predict medical risks more effectively and create targeted interventions tailored to the specific needs of the population all while minimizing the bias and privacy issues.
The methodologies and insights gained from these applications in Kenya offer valuable lessons that can inspire and inform data science practices in other markets facing similar challenges, including those in Europe. By sharing these strategies, we can explore how synthetic data can be adapted and implemented across different regions to solve local problems, improve outcomes, and advance the field of data solutions globally.
This session aims to provide attendees with an understanding of the potential of synthetic data, not only as a solution to data scarcity but also as a means to enhance privacy and reduce bias in data-driven models. Through detailed case studies and practical demonstrations, attendees will gain actionable insights that can be applied to their own work, fostering innovation and effectiveness in their data science endeavors.
She writes backend logic and ML models for a living. She is also passionate about health informatics,open source source and tech communities.