Global analytics leader SAS and MAG Aerospace are teaming up to pursue opportunities from the recently awarded US Space Force Space Systems Command $900 million indefinite delivery, indefinite quantity contract. SAS and MAG Aerospace will bring expertise in AI, analytics and Joint All-Domain Command and Control to the space realm, enabling the Space Force to analyze and use all the data coming in from its vast network of radars and sensors.
The Space Systems Command IDIQ contract is in support of Space Command and Control. Space Force tracking radars and other sensors produce incredible amounts of data from thousands of locations. Last year, the Space Command tracked more than 47,000 objects in space. There is a critical need to convert this proliferation of diverse sensor data accurately and rapidly into usable, real-time intelligence for mission success. SAS’ deep analytical capabilities enable commanders to enhance and accelerate decision making while promoting full network and data integration.
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“For nearly 50 years, SAS has been an industry leader in applying AI and analytics to the most complex mission challenges,” said Caroline Baldwin, Director for SAS Federal. “As Space Force embarks on its burgeoning mission to conduct global space operations, we believe our alliance with MAG Aerospace is uniquely positioned to deliver on Space Systems Command’s IDIQ task order opportunities by enabling data and network interoperability across all operating environments.”
MAG Aerospace has successfully hosted the SAS Viya platform in a secure cloud environment for processing Internet of Things data and performing advanced analytics at the tactical edge. SAS Viya an open, cloud-native AI, analytics and data management platform supports MAG’s intelligence, surveillance and reconnaissance requirements across the full analytics life cycle, from the edge to analysis of data sets in complex operational environments. Powered by Viya, SAS Analytics for IoT puts the power of insight into users’ hands to provide value that is realized from fast-moving, voluminous data in hours and days, not months.
SOURCE : PRNewswire