How to Build a Solid Foundation for Network Data Quality

By Karthik Jeevanige

The fast-paced evolution of telecoms technology has brought communication service providers (CSPs) to a critical juncture. With the rapid expansion of 5G, cloud-native network functions and software-defined infrastructure, CSPs benefit from greater agility and artificial intelligence (AI)-powered automation, opening the door to telecommunications AIOps and intelligent networks. Yet, real-world execution lags behind.

In fact, only 6 percent of operators currently rely on AIOps to resolve network issues end-to-end. Moreover, despite CSPs having invested millions into AI, automation and autonomous network initiatives, most AIOps projects struggle to deliver the expected value. Where is the disconnect?

While it may be logical to assume that a lack of technology capability is preventing AIOps implementation, the problem does not stem from insufficient computing power or inadequate AI algorithms. Ultimately, the underlying issue is data readiness. Time and again, compromised network data quality is the primary stumbling block derailing telecommunications AIOps deployments.

The Hidden Prerequisite – Data Readiness

As legacy networks grow and evolve, network data becomes fragmented in isolated silos within Operations Support Systems (OSS) and Business Support Systems (BSS) platforms. Although, in theory, the combination of OSS data with BSS data can produce a richer feature set for better root cause analysis, in reality this integration commonly leads to data corruption due to translation errors, event duplication, stale context and inaccurate network inventory.

This situation is further complicated by conflicting vendor performance metrics, data governance gaps and inconsistent naming conventions. All of these errors propagate throughout the pipeline and across domain boundaries, and even advanced AI architectures are unable to succeed under these conditions. The truth is that most production AIOps environments are operating with at least partially corrupted data carrying systematic biases.

In traditional network monitoring, human engineers typically compensate for minor data anomalies. Yet, AI actually amplifies network data quality problems, fundamentally changing the AIOps dynamic. Consider, for example, a 5 percent error rate that a network operations center (NOC) engineer might filter out manually. When scaled across automated decision-making pipelines, this error becomes a systematic flaw.

To overcome these data issues and enable AI to work correctly, the advancement of intelligent networks will require a data readiness foundation built on clean, contextualized network telemetry.

The True Cost of Poor Network Data Quality

Ensuring data readiness is essential for autonomous network operations in the following domains: 

• Anomaly Detection: Requires clean historical baselines to distinguish actual service degradation from background collection noise across the radio access network (RAN), core and transport domains.
• Event Correlation: Demands semantic consistency; if equipment from different vendors reports identical metrics under varied labels, root cause analysis breaks down.
• Predictive Analytics: Relies on accurate historical patterns; inconsistent records generate unreliable forecasts that diminish operational trust.
• Automated Remediation: This function demands high-confidence signals; noisy data risks triggering automated actions that cause worse outages than the initial fault.

Operating on an inadequate data foundation places a severe financial and operational toll on network operators, which all too often is overlooked. In fact, for the typical Tier 2 and Tier 3 CSP, the combined annual loss from inadequate network data quality ranges between $5 million and $12 million.

These costs are due to a combination of several factors. 1) Alert fatigue resulting from false positives can waste an average of more than 150 hours of NOC engineering time per month. 2) Unidentified service degradation leads to customer outages, costing up to $500,000 or more per major incident in service level agreement (SLA) credits and customer churn. 3) OSS/BSS fragmentation causes integration delays of roughly four to six months when undocumented data sources emerge late in project lifecycles.

Five Dimensions of Data Readiness 

To determine AIOps readiness, network managers need to evaluate if their network data foundation is ready for AI-driven service assurance. Inconsistent reporting formats alone can account for a 2 to 5 percent variance in identical metrics between multivendor elements, sufficient to disrupt automated correlation engines.

Evaluating readiness across five core dimensions provides a clear diagnostic path:

Readiness Dimension	Core Focus
1. Network Data Completeness	Multivendor coverage across RAN, core and transport, including 5G container metrics and network slice KPIs.
2. Data Quality & Consistency	Validating network telemetry accuracy and consistency, including precise timestamps and alarm deduplication across vendors.
3. Data Accessibility & Integration	Breaking down OSS/BSS silos to provide a foundation for reliable AI correlation with consistency across alarms, topology and customer data.
4. Data Governance & Compliance	Documented policies governing data handling, retention and security, including GDPR/CCPA compliance for multi-region data sovereignty.
5. Service Assurance Architecture	A modern service assurance framework should correlate technical network KPIs with actual customer quality of experience (QoE) outcomes.

 A Strategic Framework for Telecom AIOps

While it’s important to establish a lasting network data quality framework, this goal does not require years of effort. An AI-ready data baseline can be established using a structured eight-week phased roadmap, working closely with AIOps experts at 1Finity.

Important steps along this roadmap include Assessment and Planning (Weeks 1–2), Quick Wins and Momentum (Weeks 3–4), Data Quality Deep Dive (Weeks 5–6) and Integration & Readiness Validation (Weeks 7–8). 

Once a validated data foundation is mapped out, network telemetry deployment follows three distinct phases:

1. Proof of Concept: By deploying the AIOps platform in read-only mode, network engineers can monitor a single network domain to validate correlation accuracy against NOC expert assessments. 
2. Production Pilot: The pilot phase involves enabling AIOps for one or two critical services in production using closed-loop automation for low-risk remediation. Service assurance protocols include human oversight for customer-impacting decisions. 
3. Enterprise Rollout: Once the pilot has proven successful, deployment is expanded to all critical services and network domains, enabling predictive analytics, capacity forecasting and customer experience correlation. At this point, the NOC role shifts from reactive troubleshooting to proactive service assurance oversight.

Operators that complete this eight-week structured data preparation program and phased deployment consistently achieve significant performance upgrades.

Building for Tomorrow

When selecting a solution for telecommunications AIOps, the main discussion often revolves around key feature comparisons. However, the fact of the matter is straightforward: the most advanced AIOps platform will fail if deployment is based on unreliable, fragmented data.

Building a robust network data quality foundation guarantees higher success rates, accelerates time-to-value and unlocks meaningful returns on network automation investments, thus paving the way to tomorrow’s AI-powered intelligent networks.

To learn more about how to optimize your network data for a solid AIOps foundation – as well as helpful tips for avoiding common pitfalls – be sure to read the white paper: “How to Build a Lasting Network Data Quality Foundation for Telecommunications AIOps.”