Telco Data Challenges Are Hampering AI-Native Network Progress

Why It Matters

If telcos cannot standardize and govern their data, they risk failing AI initiatives, losing operational efficiency and missing revenue from AI-driven services, while ceding competitive ground to cloud providers. Addressing these data barriers is essential for reliable automation, faster innovation and realizing the business case for AI-native networks.

Summary

Speakers at Telecom TV’s DSP Leaders World Forum warned that decades-old, heterogeneous and siloed telco data is a major obstacle to building AI-native and cloud-native networks. Data lives in inconsistent formats across wireless, wireline and OSS/BSS domains, with weak cross-business governance and entrenched ownership protecting stovepipes. That fragmentation — compounded by privacy, sovereignty and peering requirements — means many AI projects either fail or never get off the ground because the necessary, high-quality data does not exist. Panelists argued fixing data hygiene, standards and governance is a prerequisite before AI can reliably deliver network automation or new monetization.

Original Description

In this episode of the Extra Shot, industry experts from Everpure, Luth Computer, Telefónica and examine the persistent challenges facing telcos in terms of data quality and fragmentation. They discuss how legacy systems, inconsistent data formats across different network technologies and organisational silos create barriers to AI implementation and cloud-native operations. The panel explores why decades of data standardisation efforts have failed to resolve these issues and looks at the realistic prospects for operators to complete their cloud-native transformation journey beyond the network core.

Featuring:

- Beth Cohen, Telco Industry Analyst, Luth Computer

- Diego R Lopez, Senior Technology Expert, Telefónica and ETSI Fellow

- Patrick Smith, EMEA CTO, Everpure

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