In the race to harness artificial intelligence, telecommunications companies face a critical challenge that goes beyond finding the best AI engineers or models. According to Ericsson Americas CTSO Joe Constantine, data fidelity – the accuracy and completeness of network data – has emerged as the determining factor for which telcos will dominate the AI era.
Why Data Fidelity Trumps AI Models
“The companies that will win and prevail are not the companies that have the best AI engineers or AI models. It’s the companies that have the best access to data and trusted data,” Constantine explained in a recent interview with Fierce Network. “That’s the holy grail.”
Telecom networks generate massive amounts of data every microsecond from multiple sources in various formats. However, this data often lacks integrity, with pieces missing, stuck in silos, or lost in translation. The challenge isn’t applying AI – Constantine notes that’s “the easy part” – but rather effectively capturing, curating, and authenticating all that data to make it usable.
The Path to Horizontal Data Architecture
Constantine advocates for creating a horizontal data layer that spans all facets of an operator’s network, including RAN, transport, and compute infrastructure. While this is technically possible today, it typically requires manual system integration – a complex and costly approach.
“Every CEO and CTO across the board wants to create exactly the kind of system described,” Constantine noted. “The top 10, 20 carriers on the planet, all of them are looking into this because data is monetizable.”
This horizontal approach won’t just benefit AI initiatives but will also enable the kind of automation operators need to dynamically handle AI traffic. While some operators have demonstrated Level 4 automation, they’ve done so in vertical silos rather than the horizontal integration required for comprehensive network intelligence.
Enterprise Network Equipment and Data Management
For enterprises managing their own network infrastructure, similar data fidelity principles apply. Modern enterprise networking equipment from leading manufacturers like Peplink, Cradlepoint, Teltonika, Semtech, Inseego, Digi, and Katalyst increasingly incorporates AI-driven features that rely on accurate data collection and analysis. Edge AI capabilities in cellular networks are transforming how businesses manage their connectivity and IoT deployments.
Industry Challenges and Constraints
Despite the clear benefits, telcos face several constraints in implementing comprehensive data management systems:
- Critical Infrastructure Responsibilities: Telecom companies must balance innovation with the reliability requirements of essential services
- Regulatory Compliance: Heavy regulation creates additional complexity compared to other industries
- Real-time Operations: Unlike many tech companies, telcos operate true real-time systems that can’t afford disruption
- Financial Constraints: Short-term monetary pressures must be balanced against long-term technology upgrade roadmaps
The Bigger Picture: From QoS to Intent-Based Networks
The push for better data management aligns with the industry’s broader shift from quality of service (QoS) networks to intent-based, quality of experience connectivity. A recent Cloudera survey found that while telcos are ahead in data visibility and access, they lag in data governance – the policies around how data can be used and by whom.
This evolution represents a fundamental change in how networks operate, moving from reactive service delivery to proactive, AI-driven optimization based on user intent and experience requirements.
5Gstore Take
The emphasis on data fidelity over AI sophistication represents a crucial insight for both service providers and enterprises. While the telecommunications industry often focuses on the latest AI breakthroughs and machine learning models, Constantine’s perspective highlights that success depends more on data quality and accessibility than algorithmic complexity.
For enterprises investing in networking equipment, this translates to prioritizing solutions that provide comprehensive data visibility and management capabilities. Whether deploying cellular failover systems, SD-WAN solutions, or IoT connectivity, the ability to capture, analyze, and act on high-quality network data will increasingly differentiate successful implementations from those that merely function.
The challenge for vendors and operators alike is creating systems that make this horizontal data integration easier and more cost-effective, moving beyond today’s manual approaches to automated, intelligent data management platforms.
FAQ
What is data fidelity in telecommunications?
Data fidelity refers to the accuracy and completeness of data collected from telecom networks. It ensures that information captured from various network components is reliable, properly formatted, and accessible for analysis and AI applications.
Why is data fidelity more important than AI models for telcos?
According to Ericsson’s Joe Constantine, having access to trusted, accurate data is more critical than having the best AI engineers or models. High-quality data enables effective AI implementation, while poor data quality makes even the most sophisticated AI models ineffective.
What is a horizontal data layer in telecommunications?
A horizontal data layer is an integrated system that captures and manages data across all aspects of a network – including RAN, transport, and compute infrastructure – rather than keeping data isolated in vertical silos. This approach enables comprehensive network intelligence and automation.
What challenges do telcos face in implementing better data management?
Telcos must balance innovation with critical infrastructure responsibilities, comply with heavy regulations, maintain real-time operations, and manage short-term financial constraints while investing in long-term technology upgrades.
How does data fidelity relate to intent-based networking?
High-quality data enables the shift from traditional quality of service (QoS) networks to intent-based, quality of experience connectivity. Accurate data allows networks to understand and respond to user intent and optimize for actual experience rather than just technical metrics.
