How to Data in Datathons
It addresses a practical problem for datathon organizers by offering incremental guidelines to improve data handling.
The paper tackles the problem of organizations struggling with data-related issues in datathons due to a lack of clear guidelines, and it provides a framework based on experiences from over 80 datathon challenges and 10 case studies.
The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate in a short timeframe. Despite their significant potential benefits, organizations often struggle to effectively work with data due to a lack of clear guidelines and best practices for potential issues that might arise. Drawing on our own experiences and insights from organizing >80 datathon challenges with >60 partnership organizations since 2016, we provide guidelines and recommendations that serve as a resource for organizers to navigate the data-related complexities of datathons. We apply our proposed framework to 10 case studies.