Daniel G. Hernandez

National security customers are focused on transforming their ISR strategies. Our team did some digging and we realised that there was a global trend across countries who were all looking to change the way they leveraged space based GEOINT. There were several common themes that emerged; - Organisations are moving toward procuring data from multiple commercial sources, - Challenges persist around effectively co-ordinating sovereign assets with commercial capture capabilities, - Harmonizing satellite datasets from multiple sources at scale is an issue, - Securely sharing large quantities of spatial intelligence data across teams and with allies was a pain point. The global intelligence community is starting to invest in Earth Observation Data Infrastructure (EODI) to scale their operations in order to produce better intelligence products for people that need them most. #EODI #geoint #nationalsecurity #space

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Go Slingshot! 👏   NOAA’s COLA Gap Pathfinder effort marks a key step forward in providing satellite operators with faster, more responsive space traffic coordination. As space becomes rapidly congested, efforts like this are essential for ensuring the safety and sustainability of our orbital environment.   We’re proud to be a key partner of NOAA and the Office of Space Commerce, and we look forward to continuing the mission of a secure and resilient space domain.

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This is a pivotal moment!! We are deploying our most sophisticated, battle-proven AI capabilities across the Force. The USMC is moving out decisively with our advanced Integrated Readiness Optimization (IRO) AI utility suite to solve the complex sustainment equation for the high-demand MV-22 fleet. The mission is clear: Maximize aircraft availability and get birds in the air, faster. We are proud to stand shoulder-to-shoulder with the USMC, translating cutting-edge AI into a lethal, ready, decision advantage. 🇺🇲 #MissionAI #ReadinessAI #TeamVirtualitics

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Somewhere between seventy and eighty-seven percent of AI projects never make it to production. If your data is not clean, your organization is not ready for AI. In aerospace engagements, I have seen teams map data lineage across legacy systems where the same field names carry completely different meanings depending on which decades-old platform generated them. AI models could confidently make decisions based on fundamentally incompatible information. You build a decision-making engine on quicksand, and the ground gives way. Before AI, analytics teams would generate reports where a person in the loop could bridge gaps and apply contextual judgment. That human filter masked inconsistencies that organizations could afford to live with. When AI makes autonomous decisions (without human-in-the-loop pulling from multiple sources, those previously tolerable inconsistencies could become catastrophic. The good old master data management (MDM) creates the standardized framework that makes enterprise AI more reliable and capable of delivering ROI. Data lineage requires tracing where information originates, what control gates exist at entry points, how data transforms through ETL processes, and what validation happens before it lands in your data lake. I know this sounds like fundamental plumbing work when everyone wants to talk about AI breakthroughs, but it determines whether your AI delivers business value or becomes another expensive pilot that never scales.

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🗓 Weekly Recap on SpaceX • Partners with JAXA: Japan Aerospace Exploration Agency on satellite launch, deepening Japan–US cooperation. • Weighs South Texas land exchange to expand Starbase and Starship operations. • Caps record 2025 launch cadence with COSMO-SkyMed mission. 🚀🚀🚀

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