On a modern battlefield, GPS is enormously useful. It is also something the warfighter cannot assume will always be available.
Buildings, terrain, tunnels, and subterranean spaces can block or degrade satellite signals. In a contested environment, an adversary may deliberately jam or spoof them. The Department of Defense has publicly described GPS and unmanned systems as targets of electromagnetic interference, while the Army has documented the operational consequences of losing GPS during unmanned ISR missions.

As ransomware attacks continue to grow in frequency and sophistication, organizations are reevaluating how they protect critical data. Traditional backup strategies were designed primarily to recover from hardware failures or accidental data loss. However, modern cyberattacks often target backup systems themselves, attempting to delete or encrypt recovery data before launching a ransomware attack.

Organizations today generate and store more data than ever before. Operational systems, analytics platforms, security tools, IoT devices, and digital services all contribute to rapidly growing data volumes. While expanding storage capacity is an important part of managing this growth, simply storing data is not enough. Organizations must also ensure that data is organized, accessible, secure, and compliant with regulatory requirements.

Artificial intelligence and advanced analytics are transforming how organizations collect, process, and use data. From predictive modeling and fraud detection to mission analytics and operational intelligence, modern workloads increasingly rely on machine learning models and large-scale data analysis. These systems process enormous datasets and require powerful compute environments such as GPU clusters and distributed processing frameworks.

Modern organizations increasingly operate in environments where data is generated far from centralized data centers. Sensors, remote operations, mobile platforms, industrial systems, and field deployments all produce valuable data that must be processed and stored close to where it originates. These environments are often referred to as edge environments, and they present unique challenges for storage architecture.

Federal agencies rely on storage infrastructure to support mission systems, analytics platforms, cybersecurity operations, and long-term data retention requirements. As data volumes continue to grow and storage architectures become more complex, agencies must carefully plan how they acquire storage systems, software, and associated services.

Organizations rely heavily on digital systems to support operations, analytics, and mission-critical workloads. When systems fail or data becomes unavailable, the consequences can be severe—ranging from operational disruptions to regulatory violations and financial losses. To protect against these risks, organizations implement data protection strategies such as backups, disaster recovery systems, and cyber recovery environments.

Organizations rely heavily on digital systems to support operations, analytics, and mission-critical workloads. When systems fail or data becomes unavailable, the consequences can be severe—ranging from operational disruptions to regulatory violations and financial losses. To protect against these risks, organizations implement data protection strategies such as backups, disaster recovery systems, and cyber recovery environments.

Organizations rely heavily on digital systems to support operations, analytics, and mission-critical workloads. When systems fail or data becomes unavailable, the consequences can be severe—ranging from operational disruptions to regulatory violations and financial losses. To protect against these risks, organizations implement data protection strategies such as backups, disaster recovery systems, and cyber recovery environments.

Storage performance is one of the most important factors in designing modern IT infrastructure, yet it is also one of the most commonly misunderstood. When organizations evaluate storage systems, they often focus on metrics such as IOPS, throughput, or latency without fully understanding how these measurements relate to real-world workloads.