What are the most significant barriers data scientists face in integrating and reconciling various types and sources of data, including issues of standards, unique identity, ownership, and semantic translation? How should users efficiently and effectively integrate different data? What organizational behavior, incentive, and design issues or techno-cultural problems arise in assembling, analyzing, and deploying data? Moreover, how should data scientists represent information in ways that maximize actionability? This panel will examine approaches for enhancing the functionality and usability of diverse financial data sets.
