During peacetime and in conflict, the U.S. military still relies on decades-old enterprise ground systems (EGS) in their attempt to achieve true situational awareness. As data collection increases, information continues to be loaded and trapped in stovepipes – despite the fact that these siloed architectures are expensive and time consuming.
Analysts have been spending more time searching and correlating information from various data sources rather than analyzing data because it is housed across a landscape of systems and networks. This siloed data prevents collaboration on important military tactics such as threat detection, decision support and furthering research. Each satellite-to-ground-station stack needs its own costly front-end interfaces and data management tools to allow the flow between satellites, operators, and other systems. As new technology emerges, sensors and data sources (such as surface weather, space weather, and ISR data sources) are becoming readily available. While this may be a good thing, the data needs to be ingested and put in context order to enable better decision-making.
With today’s infrastructure and architecture, such insights are extremely challenging to attain without slow and manual data aggregation efforts. In order to focus on mission success, user collaboration, and innovation, space-based data should be modernized to work well alongside legacy ground systems. These legacy architectures contain traditional relational database models and relational-technology stacks which cannot easily adapt to new data structures and formats, therefore more silos of data and systems are created. Because of the increase of data silos, information cannot be easily shared across satellite systems to find common signatures.
For example, it can become very difficult to answer the question ‘were variances in on-board system voltages coinciding with a space weather event, or do they indicate a cyber threat to the satellite or larger constellation?’ Without access to multiple satellites and space weather data being integrated on one platform to allow searching across all data sources, it is very difficult to answer similar questions. Diving deeper into this example, a single satellite reporting a variance in voltage on an on-board sensor may not be a major concern. But, the variance being spotted across a collection of different satellites managed by different ground-stations and vendors would bring significantly more valuable context to the question being asked. This variance being correlated to an intelligence source indicating cyber-threat activity would be even more valuable.
Having one platform to promote sharing of intelligence enables leaders to make quicker and more accurate decisions.
In order to gain true situational awareness across space related data, an innovative approach using a multi-model data repository for cross-functional discovery and operations is needed. You need a database to be able to harmonize various data feeds into canonical forms on an as-needed basis. This repository can serve as a real time data-centric interchange supporting enterprise operations, analysis and discovery throughout the data lifecycle.
A newer technology that can provide a wide spectrum of data-oriented capabilities - from enterprise storage, search, and analytics to synchronized information exchange would allow for better situational awareness.
Enhanced discoverability of data and information across all feeds provides a number of business benefits:
Bringing together all EGS stovepipes means data should be integrated, enriched and delivered in geospatial, temporal and semantic context allows the U.S. military to obtain a holistic view of information. Having one platform to promote sharing of intelligence enables leaders to make quicker and more accurate decisions. Learn how government agencies revamp legacy relational databases to deliver new, mission-critical applications better, faster, and with less cost.