Data in telecom
telecom data: network and usage data, customer analytics and churn prediction, and privacy/regulatory constraints on rich data.
Telecom generates some of the densest, most continuous data in any industry: network signaling, CDRs, geolocation, device telemetry, and billing events at massive scale. This block builds fluency in how telecom operators structure, measure, and govern this data. You will learn the core data concepts as applied to networks and customers, then map the actual data landscape: OSS/BSS sources, network performance datasets, churn and usage metrics, and the quality benchmarks operators track. Finally, you will cover the regulatory and privacy regime specific to telecom (location data, lawful intercept, metadata retention) and the governance controls and audits needed to keep data trustworthy, compliant, and usable for analytics and AI use cases.
What you'll master
- Map the core OSS/BSS and network data sources an operator relies on and explain what each captures
- Evaluate telecom-specific data quality metrics such as CDR completeness, latency accuracy, and churn-label consistency
- Identify privacy and regulatory obligations around location, metadata, and lawful intercept data in telecom
- Design a practical data audit checklist for network and customer datasets before they feed analytics or AI models
Key terms
Modules
Applique les fondamentaux de la donnée aux cas d'usage concrets du secteur télécom.
Cartographie le paysage des données télécom, sa qualité et les KPI suivis par la direction.
Met en place gouvernance, gestion du consentement et audits pour la conformité télécom.
Latest articles
Recent articles from the blog that apply to Telecom.
- How did Telefónica build a churn model that actually moved retention numbers?Telefónica's data teams spent years accumulating subscriber signals before their churn models started producing revenue-grade predictions. The mechanics of what they built, and where other telcos consistently fall short, carry direct lessons for any CDO running a retention program in 2026.
- Clean rooms in practice: a CDO playbook for data collaboration that actually worksData clean rooms offer a principled path to collaborative analytics without exposing raw customer data, but most implementations stall on governance gaps and misaligned incentives. This playbook gives CDOs a concrete sequence to stand up a clean room partnership, avoid the common failures, and extract value quickly.