Global Telecom Turns Network Telemetry into Real-Time Intelligence with CrowdStrike Falcon Onum
For a global telecommunications provider serving millions of subscribers across multiple generations of infrastructure, operating at network speed is mission critical. Small issues can cascade quickly, often before teams know something is wrong.
Visibility is foundational to that mission. Telemetry flows continuously from customer premises equipment, multi-vendor network infrastructure, deep packet inspection systems, call detail records, and service quality platforms supporting media and streaming services. That telemetry powers everything from performance monitoring to incident response.
Over time, however, the sheer volume of telemetry began to undermine the insight it was meant to deliver. To stay ahead of the curve, the telecom shifted from the mindset of “store first, analyze later” to creating intelligence in-motion with CrowdStrike Falcon® Onum (formerly Onum).
When Visibility Becomes a Bottleneck
As the telecom’s network expanded, tens of terabytes of telemetry poured in each day. Analytics systems downstream were tasked with normalizing fragmented data, shaped by vendor-specific schemas and years of incremental infrastructure growth. What should have been a source of operational advantage increasingly slowed teams down.
Processing costs rose alongside data volume, driven by storage requirements and volume-based licensing. Latency was also problematic. Traditional analytics workflows relied on batch processing windows measured in minutes. In a telecommunications environment, those minutes mattered. Network issues could escalate quickly, turning minor anomalies into customer-facing incidents before alerts surfaced.
Every adjustment added friction. Refining KPI definitions or alerting logic required downstream reprocessing, manual configuration changes, and long validation cycles. Teams faced a persistent trade-off: reduce telemetry volume and risk blind spots, or preserve visibility and accept delay as the cost of doing business.
Neither option aligned with how the network operated. A bold change was necessary.
Shifting Intelligence Upstream
Rather than pushing ever-growing volumes of data into downstream platforms and hoping analytics could keep up, the organization took a different approach. Intelligence would no longer be something extracted after the fact. It would be created in motion.
That shift began with Falcon Onum.
Falcon Onum was deployed as a real-time data pipeline that processed network telemetry as it moved through the environment, before it reached centralized analytics and storage systems. Filtering, enrichment, transformation, and contextualization happened upstream, while the data was still fresh.
Instead of collecting everything first and deciding what mattered later, Falcon Onum allowed the organization to make real-time decisions about what data mattered, how it should be shaped, and where it should go. Processing moved closer to the source, where action could be taken immediately.
For network teams, telemetry stopped behaving like a storage problem and started functioning as a live intelligence stream.
Building Pipelines That Match Network Reality
The implementation centered on multiple purpose-built pipelines, each aligned to specific operational needs across the network.
High-volume telemetry from heterogeneous sources was normalized and filtered in motion. Redundant events were eliminated without sacrificing analytical value, allowing downstream platforms to focus on signal rather than noise.
KPIs such as throughput, latency, packet loss, and service quality metrics were calculated as telemetry flowed through the pipeline. These KPIs became available immediately, rather than after batch analytics completed. Network teams gained sub-second visibility into performance trends, enabling faster detection of emerging issues.
Anomaly detection logic also moved upstream. Potential problems were identified in motion and routed directly to operational systems, reducing reliance on delayed downstream analytics. In some workflows, alerting latency dropped to as low as 230 milliseconds, giving teams valuable time to respond before issues cascaded.
Context was equally critical. Telemetry alone rarely tells the full story. By combining live network data with contextual information such as topology and subscriber attributes, Falcon Onum ensured that each downstream destination received only the data it needed, structured and enriched for its specific purpose.
From Terabytes to Timely Insight
The impact of processing telemetry in motion was immediate and measurable.
For select KPI-driven pipelines, data reduction reached as high as 99.8%. Massive daily telemetry volumes were transformed into focused, high-value datasets without degrading analytical capability. Across multiple data streams, consistent volume reduction lowered storage and compute demands, easing infrastructure strain and controlling costs tied to volume-based licensing.
Operational visibility improved dramatically. Instead of waiting minutes for batch processing windows to close, supported workflows gained sub-second access to performance indicators. Alerting latency dropped to hundreds of milliseconds, enabling faster detection and earlier response across network operations. Multi-vendor and multi-generation infrastructure could be monitored consistently, even as the network continued to evolve.
By decoupling telemetry onboarding from downstream analytics constraints, the organization achieved up to 70% faster response compared to legacy batch-based approaches, without sacrificing visibility or trust in analytics.
Operating at Network Speed
Beyond performance improvements, the shift to real-time data processing changed how teams worked day to day.
Network engineers gained faster access to the indicators they relied on most. Adjustments to KPIs or alerting logic no longer required weeks of development and validation. Telemetry arrived structured and contextualized, allowing operational workflows to move with greater speed and flexibility.
Vendor diversity, once a source of complexity, became easier to manage. Falcon Onum’s vendor-agnostic approach allowed telemetry from legacy and modern infrastructure to be processed consistently, even as new technologies and services were introduced.
A Foundation for Modern Network Operations
By adopting Falcon Onum as a real-time data pipeline, this telecom provider transformed network telemetry from an operational bottleneck into a strategic asset. Processing data in motion enabled faster onboarding, lower downstream overhead, and immediate access to actionable insight, without compromising visibility or confidence in analytics.
As telecommunications networks expand and customer expectations rise, the ability to operate at network speed becomes a competitive necessity. This story demonstrates how shifting intelligence upstream, filtering, enriching, and routing telemetry in real time, creates the foundation for resilient, responsive network operations in an always-on world.
Reported data reductions and performance improvements reflect internal measurements and customer-provided estimates during pre-deployment and early operational use that occurred prior to CrowdStrike’s acquisition of Onum. Actual results vary based on deployment scope, data types, and downstream system requirements.
