Do algorithms always portray geospatial data correctly? How do you keep bias out of maps? Are there systematic blind spots in the geospatial analytics workflow? Are there viable mitigations that analysts can take? This discussion examines issues pertaining to the use of AI with geospatial data, and the challenges to ensure analytic integrity.

The most important element in the battlefield of the future won’t be rockets, bullets, or robots, but data, the ability to collect it at any point and send it precisely to where it needs to be. The shareability of data is at the heart of the U.S. military’s next-generation Multi-Domain Operations concept, a vision of the future in which every tool in the U.S. arsenal, in the land, air, sea, space and cyberspace, can communicate ins...

The new administration’s directive to “do more with less” calls on federal agencies to both increase efficiencies and improve results with existing resources. To meet this challenge agencies are harnessing the power of data and analytics to implement effective, evidence-based programs. This culture of results-driven decision-making enables more productive employees, fewer wasted resources, better service for citizens, and hig...

The era of big data is here. And with it, agencies have the opportunity to make faster, better decisions to improve mission performance. But there’s a problem. The volume of data is overwhelming and traditional databases and methodologies aren’t up to the task to manage, analyze and share more disparate data sets quickly and securely while accelerating agencies’ mission. The good news, however, is that every agency and organi...