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Powering Telecom Data for Tomorrow’s AI Operations
Only a third of operators say their data is fully governed. Every new pilot builds its own pipeline, and costs keep rising.
A shared foundation, organized as data products with named owners, cuts the time and cost of each new AI use case and keeps results traceable for your board and regulators.
Your teams reuse certified data instead of building a new pipeline for each project, so use cases go live sooner.
The data platform, quality rules and access controls are shared, so every new use case costs less to build and run.
Network, IT and customer data sit in one common telecom data model, so care, assurance and network planning work from the same numbers.
Lineage, privacy and data residency controls keep sensitive data in the jurisdictions where regulation requires it, under your control.
How NTT DATA helps
We assess network, OSS/BSS and customer data sources against your priority AI use cases and put a figure on what poor data costs you today.
A data fabric connects your platforms where they already run, on a common telecom data model, so you migrate step by step with no lock-in to one vendor.
We start with network inventory, service assurance and billing, each with quality rules, service levels and an accountable owner on your side.
The platform enforces lineage, access policies, privacy and residency rules, so compliance stops depending on manual checks.
MLOps and LLMOps move priority use cases from pilot to production, and we track each one against a business outcome agreed before launch.
Consolidate network and customer data into governed data products once, then build every new AI use case on top of them.
Your business signs off on every measure before the first use case goes live, and we report results use case by use case.
Track how long a new AI use case takes to go from approved idea to live operation.
Measure the share of your priority data domains under full governance, with owners, quality rules and lineage.
Follow the full cost of each AI use case, including the run cost of the data platform underneath.
Watch service activation lead time, order fallout, cost to serve, mean time to repair and churn where the data is in use.
Clearing data debt turns AI from a series of isolated pilots into a lasting operating advantage.
