Groups Conversations All groups and messages Sign in     Fueling the autonomous network: Google Cloud and DigitalRoute simplify data readiness for AI 0 views Eyal Estrin unread, 12:17 PM (2 minutes ago)    to https://cloud.google.com/blog/topics/telecommunications/partnering-with-digitalroute-on-reusable-data-pipelines Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

To achieve Level 4 and Level 5 autonomous network operations (ANO), the telecommunications sector is increasingly turning to AI to build self-healing, self-optimizing networks capable of predictive...

A recent study reveals that organizations struggle to grasp the costs of AI features, creating obstacles in monetization strategies amid a rapidly evolving landscape.

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At MWC 2026, we launched reusable data pipelines with DigitalRoute that transform raw, chaotic network noise into high-quality, “AI-ready” data.

Google Cloud and DigitalRoute Team Up to Tackle Telco Data Challenges for Autonomous Networks

The telecommunications industry is striving for Level 4 and 5 autonomous networks using AI, but data quality is a major obstacle. AI models are ineffective without clean, reliable data, which is often fragmented and messy. A new collaboration with DigitalRoute focuses on data readiness by transforming raw network data into "AI-ready" formats. This collaboration aims to provide reusable data pipelines to help solve the data swamp challenge, where data is hard to use. Network data presents challenges due to disparate formats, high volume and velocity, schema evolution, and correlation complexity. The solution utilizes a cloud-native architecture that bridges legacy protocols with cloud-native intelligence. This architecture normalizes data at the edge and core, creating a "network digital twin" with real-time data in Spanner and historical data in BigQuery for analytics. The partnership will enable production-grade AI applications for rapid anomaly detection, automated root cause analysis, and predictive maintenance. This data-focused approach lays the groundwork for AI-driven networks. The collaboration offers a crucial solution to the industry's data challenges. By improving the data, the path to autonomous network operations will become more attainable.