Artificial intelligence depends on its ability to draw insights from massive datasets, but size alone isn’t the goal.  

This week on Feds At the Edge, our panel of experts explore how smarter data management can make AI faster, more efficient, and more reliable by focusing on clean, well-labeled, and right-sized information.  

Years ago, scientists dreamed of capturing data from sources as vast as satellites and as precise as atomic sensors. That dream is now reality, and today’s challenge is ensuring that this flood of data is organized and manageable.  

Thane Price of Idaho National Laboratory shares how reducing duplicative data and streamlining metadata helps AI learn more effectively. Gulan Shakir from the National Archives and Records Administration discusses how modern cloud infrastructure accelerates data transfer compared to legacy systems. And Cloudera’s Kevin Talbert explains how proper data cataloging, synthetic datasets, and metadata management enhance testing and training for AI models.