From industrial operations to military and intel sensors at the edge, organizations today are drowning in data — but starved for insight.
In 2010 the world generated 2 zettabytes, and in 2025 that number will top 181 zettabytes, with 90% of the world’s data created in just the last two years. While some of that exponential growth comes from the rise of the internet and social media, much is due to accelerating Internet of Things (IoT) deployments in military and industrial settings, where there’s been an explosion in both the volume and diversity of data, particularly unstructured and semi-structured data.
Real-time data streams are growing faster than ever, with sensors, RF systems, and video feeds leading the pack. For example, Special Operations Command (SOCOM) is now generating more data inside the tactical bubble than outside the bubble. In industry, real-time data drives everything from logistics to fraud detection to pricing decisions.
This raises problems. The growing speed and complexity of data ingestion has created a widening “analysis gap,” where critical insights are lost because data can’t be processed or stored fast enough.
Understanding the Analysis Gap
Data volumes and speeds today are outpacing organizations’ analytic abilities. Right now, data is being generated faster than it can realistically be processed, stored, or analyzed. Even high-end tools struggle to handle simultaneous ingestion, filtering, and analysis at scale.
This gap threatens mission execution. When real-time data isn’t analyzed effectively, organizations risk missing key operational insights. They may fail to note emerging threats, introduce inefficiencies, and possibly encounter mission failures.
Too often, organizations are forced to discard valuable data simply to keep systems running. Network operators, for example, must sometimes jettison packet data due to limited storage, and high-speed RF or video streams may be lost before retrospective analysis is possible.
This has real-world implications. Battlefield sensor networks deliver continuous streams of situational data. With limited transmission bandwidth and compute capacity constrained at the tactical edge, the analysis gap can delay threat detection and impede situational awareness.
In defensive cyber operations, both military and industrial, system limitations mean that analysts can’t keep pace with the data flow, forcing teams to discard logs or to rely on sampling. That leaves gaps that adversaries can exploit.
In Signals and Electronic Intelligence (SIGINT/ELINT), high-speed data flows produce petabytes of information daily. Only a fraction can be captured, transmitted, or processed in real time, meaning critical information may be missed. That puts missions in jeopardy.
Data at the Edge: The New Refinery
Traditional approaches fall short here. Users may encounter appliance-based bottlenecks - current hardware systems can’t scale cost-effectively to keep pace with the ever-increasing volume of data, and raw data volume can overload existing analytic systems and sensors.
There are financial impacts, in the form of licensing inefficiencies, as organizations find themselves paying for peak throughput capacity and burning through their budgets. There are also real-world mission impacts due to siloed architectures, where network, RF, and video data often live in disconnected systems. This limits full-spectrum insight, a problem compounded by lack of storage and processing capacity.
To close the analysis gap, organizations need to process data at the edge, an approach that can be likened to a data refinery. A refinery processes raw materials into usable outputs, filtering and transforming crude inputs into refined products. In much the same way, edge computing processes raw data close to where it is generated, empowering operators to filter, analyze, and act on it locally, and to extract insights in real time.
In an edge data refinery, data is filtered and ranked — “refined” — before it reaches centralized tools. By processing data closer to where it’s created, organizations can “pull out the bad and keep the good.”
This ensures that only the most valuable, or “refined,” information is sent upstream for deeper analysis.
What Would Smarter Data Management Look Like?
Organizations face challenges as they attempt to implement edge processing. They may have limited compute and storage capacity at the edge. They may struggle to implement the high-speed ingestion needed to handle the data inputs. And current solutions may be neither adaptable nor hardware-agnostic, making it difficult to achieve edge processing.
Smarter data management can help bring the edge refinery to life. Given the accelerating pace of data generation at the edge, and the mission-critical nature of that data, organizations need key capabilities to support effective and timely analysis.
- High-speed ingestion: Systems that can handle high-speed data from multiple sources concurrently.
- Effective data filtering and deduplication: The ability to remove noise before analysis.
- Flexible storage and replay: The equivalent of a DVR for time-series data.
- Cross-domain support: Integration of network packets, RF signals, and video to break down silos.
These capabilities help an organization meet the mission-critical need to turn raw data streams into actionable intelligence — faster and more efficiently.
How the Axellio Xpress Platform helps
The Axellio Xpress Platform is a software-based, hardware-agnostic solution built to manage, refine, and distribute high-speed, time-series data. It simultaneously reads, writes, and stores data at blazing speeds (well over 200 Gbps), supporting any time-series data. By acting as a real-time data refinery, Xpress enables data reduction capabilities before it reaches analytics tools, ensuring operators see the most valuable information first.
The Platform helps prevent tool and sensor overload by throttling or buffering data streams, reducing network congestion, and minimizing data loss. This capability also lowers licensing and infrastructure costs by optimizing what is sent to analytics systems.
Built on a flexible, software-based architecture, the Xpress Platform adapts to new data sources, protocols, and emerging mission requirements. It can scale from small IoT deployments to large enterprise or government networks, ensuring organizations can ingest, store, manipulate, and distribute data efficiently, now and in the future.
Conclusion
The world’s data challenge isn’t just about volume — it’s about velocity and visibility. Organizations need tools that support effective, real-time analysis to drive mission success.
Through real-time refinement and delivery, the Xpress Platform helps transform data chaos into clarity.
