Partner content: AI-enhanced tools are no longer exciting predictions for the future – they have become the proverbial crown jewel for many telecom operations
To manage network complexity at scale, AIOps platforms are actively being deployed, and AI-driven security tools are increasingly used to counter sophisticated threats. In doing so, these platforms grant operators faster access to larger yields of data.
There is, however, a hard truth emerging: more data doesn’t guarantee better outcomes. AI platforms are only as intelligent as the data feeding them. Fragmented, sampled, and siloed network telemetry often amplifies noise, inflates observability costs, and undermines the AI integration meant to improve operational efficiency and strengthen security.
Research shows the network telemetry market is growing at 27 percent annually, reflecting substantial industry investment in data collection. But the telecom industry’s priority must now shift from data quantity to data quality – ensuring real-time, high-fidelity, AI-ready data for the models that depend on it.
The competitive advantage of AIOps will not come from collecting data, but from panning for gold within it to achieve high-signal, low-noise telemetry ready to benefit the systems it feeds. Operators who understand this shift will be best positioned to capitalise on AI’s true potential.
Why fragmented telemetry is a liability
An abundance of data used to mean operators had more control over their networks, especially when managing static infrastructure with applications that evolved gradually. However, the rise of cloud technology has changed this. Legacy tools now face a data burden they were never designed for. Yet despite this transformation, operators’ strategies have largely remained rigid, anchored to outdated collection habits.
This rigidity is costly. The scale and complexity of data flowing unfettered through telemetry pipelines creates noisy, expensive tools that obscure critical insights. As a result, network degradation and system downtime become more frequent risks – a reality that’s especially damaging for a sector dependent on uninterrupted connectivity.
If these canaries in the coal mine are dismissed, even minor outages can cause dropped calls, stalled 5G sessions, and mass subscriber churn. The reputational, financial, and trust consequences of poor data management are simply too high to ignore.
As carrier-scale infrastructure develops to handle massive traffic loads, disconnected monitoring tools trigger an unmanageable barrage of alerts. Not all alarms are equally important, but distinguishing and prioritising the most urgent ones can be incredibly draining, if not impossible.
This combination of information overload and inadequate equipment strains operators and their productivity, while paralysing the teams responsible for service assurance and threat detection. Over time, operators become dangerously dependent on a small group of experts for emergency incident response.
These seasoned firefighters become the first and last point of call for late-night rescues and system support. Unfortunately, this accelerates burnout and disrupts team balance, as experienced engineers are drawn away from strategic modernisation and automation initiatives to prop up outdated telemetry infrastructure instead.
The lack of clarity and knowledge sharing is deeply problematic in fast-paced operational environments. When telecoms teams are stuck in a cycle of reactive troubleshooting, the forward planning and innovation AIOps platforms promise are unable to come to fruition.
The stakes are similarly high for cybersecurity teams. Next-generation threats targeting telecom infrastructure are sophisticated, fast-moving, and designed to evade detection. To combat this, AI-driven threat detection requires speed, accuracy, and deep contextual awareness. These are all compromised by fragmented telemetry.
When security models ingest incomplete data, threats go unnoticed, allowing attackers to establish long-term persistence and inflict significant damage. This, in turn, costs operators in breach remediation, reputational damage, and infrastructure maintenance.
What does it mean to have AI-ready data?
Rather than collecting more data or sourcing new tools to manage an ever-growing burden, the solution lies in ensuring telemetry data is inherently fit for the purpose. In an AI-driven model, that means a shift towards a definable standard of high-signal, low-noise data.
In practical terms, AI-ready data is complete, accurate, contextually enriched, high-fidelity, scalable, and available in real time. It minimises gaps caused by sampling, tool silos, and inconsistent collection methods while providing comprehensive visibility across the network. By combining broad coverage with deep operational context, AI-ready data enables models to distinguish meaningful patterns from background noise while delivering operational insights at speed. Without these qualities, even the most advanced AI systems can produce false positives, overlook emerging threats, and generate unreliable, inconsistent outcomes.
Just as a diamond must be polished to remove surface imperfections and enhance its brilliance, data needs to be cleaned, refined, and smoothed to its essential brilliance to realise its potential within these systems.
So how can operators achieve this? One pillar of AI-ready data is timeliness. Effective AIOps depends on telemetry that presents an up-to-date image of the network. Because faults and security threats evolve rapidly, AI models require up-to-date, streamlined data to consistently monitor and protect the environment.
Maintaining this real-time observability cannot be an afterthought; without it, investment in network automation and performance optimisation is conducted in the dark. Building on this, solutions offering packet-level visibility help operators achieve high-fidelity telemetry. AI systems need detailed, accurate information to build reliable models of normal network behaviour.
AI-ready data must also be contextually enriched and correlated across domains. Raw network telemetry may indicate that an event has occurred, but context explains what happened, where it happened, what services were affected, and why it matters. Application-aware insights achieve this by transforming telemetry into actionable intelligence, helping teams understand the origin of unusual data.
Incorporating these capabilities alongside traditional observability data drastically reduces blind spots, improves service assurance, and strengthens threat detection. Telecoms operators must keep in mind that a transition of this significance requires time and commitment. Carriers looking to modernise their observability strategies should begin by establishing a trusted foundation of AI-ready data. AI can accelerate decision-making and automation, but only when the underlying data is complete, accurate, contextualised, and delivered at the speed of network operations.
A glimpse of the road ahead
The competitive divide across the telecom industry won’t simply be defined by which operators adopt AI and which do not. The true advantage will lie with those who provide these AI technologies with the precise data quality they need to perform.
AI systems and their potential are readily apparent and available to everyone. However, operators that continue feeding their models fragmented, sampled, and siloed telemetry will pay more for infrastructure, generate more noise for their teams to manage, and fall further behind in operational efficiency and security effectiveness.
In this AI-driven operations model, long-term competitive advantage comes from delivering the right data defined by high-signal, low-noise, and immediately ready for action. With packet-level visibility and application-aware insights anchoring their AI strategies, operators can transform raw network activity into an undeniable system of truth, maximising the return on both observability and security investments.
For telecom operators, the path forwards is clear: it begins with data quality. By honestly assessing data flowing through their telemetry pipelines today, and prioritising contextual telemetry over raw data volume, operators can finally move past the noise and strike the gold of true, transformative potential in the form of AIOps and AI-driven security.
For more about NETSCOUT go to https://www.netscout.com/
About the author
As NETSCOUT’s senior vice president of Europe, Donogh O’Reilly is a leader in computer networking, with a focus on driving business development within telecommunications and mobile communications. With over five years as senior vice president and over a decade at the company, he has been instrumental in deploying 3G and LTE managed services. Top of Form
