How do telecom companies build a customer 360 across BSS OSS and CRM data silos?

Summary

• Telecom data is trapped in three silos: billing systems (BSS), network operations (OSS), and customer service (CRM), each with disconnected customer records.
• A customer 360 unifies this fragmented data into one governed source, enabling real-time insights, churn prediction, and personalized offers.
• Profile Agents with agentic identity resolution accurately deduplicate and match customer records across systems without manual rules.
• Real-time operational databases embedded in the lakehouse let frontline teams (service, network, marketing) access the same data with millisecond latency.
• Unity Catalog governance ensures compliance with telecom regulations while enabling secure data sharing across teams.

How do telecom companies build a customer 360 across BSS OSS and CRM data silos?

Telecom companies hold the richest operational data: billing history, network usage, and service interactions. Yet this data lives in isolated systems, each with partial, fragmented views of the same customer. Building a unified customer 360 requires consolidating BSS (billing system of record), OSS (network operations), and CRM (customer service) data without duplicating sensitive information or creating new governance risks.
The answer lies in embedding customer intelligence natively in the governed data platform, then using autonomous agents to unify and activate customer records at scale.

What is a customer 360?

A customer 360 is a unified, real-time view of a customer that combines all known facts: who they are, what they've bought, how they use the network, their service history, and their lifecycle stage. A customer 360 built in the lakehouse keeps data where it already lives, governed by the same controls, and accessible to every team that needs it.

Key capabilities to evaluate

Capability	Why it matters
Data unification without movement	Consolidate data where it lives to maintain governance, reduce security risk, and avoid duplication.
Identity resolution accuracy	Billing has one customer ID, OSS has another, CRM has a third. Unifying them correctly means accurate segmentation and real-time decisioning.
Real-time operational access	If your 360 is batch-based, it's stale. Customer service reps, network engineers, and marketers need current data in milliseconds.
Governed activation	A 360 is only useful if teams can act on it (personalize, target, predict churn). Activation must stay within compliance boundaries.
Autonomous profile management	Manual rules don't scale. Agents should learn from data quality patterns and improve identity resolution over time.

How Databricks approaches building a customer 360

Databricks CustomerLake is an agentic customer data platform embedded natively in the lakehouse. It's designed for companies that already store massive data volumes, but need to unify fragmented customer contexts into lived insights.
Data ingestion from BSS, OSS, and CRM. CustomerLake ingests structured billing records from your BSS, streaming network telemetry from OSS infrastructure, and unstructured call center transcripts and support data from your CRM. Data stays in Unity Catalog, governed at rest, and accessible to authorized teams with fine-grained access control.
Profile Agents and agentic identity resolution. Profile Agents transform raw customer records into business-ready profiles using Agentic Identity Resolution, a hybrid approach combining deterministic rules, probabilistic models, and agentic reasoning. They ingest BSS, OSS, and CRM data; identify data quality issues; apply matching logic; and continuously improve as they learn from feedback and data patterns.
Real-time customer context via Lakebase. For telecom, real-time means milliseconds. Lakebase is a serverless Postgres database built natively on the lakehouse, allowing customer service agents, network engineers, and marketers to query unified customer context in real time. A support rep sees billing history, network status, and churn risk simultaneously, without querying separate systems.
Campaign Agents for always-on engagement. Once the customer 360 is built, Campaign Agents help marketers move from static campaigns to always-on engagement. Agents recommend next-best actions (personalized retention offers, service upgrade suggestions), build dynamic audiences, and activate across channels (SMS, email, in-app, call center) while respecting business rules and compliance boundaries.
Governance through Unity Catalog. All data access is tracked and controlled through Unity Catalog. Fine-grained access control ensures only authorized teams access sensitive customer data. Lineage is transparent, enabling compliance with telecom regulations (GDPR, CCPA, Telecom Data Protection rules).

Telecom use cases

Churn prediction and retention. Predict churn risk from tenure, usage trends, and support interactions. Activate retention campaigns with personalized offers before customers switch carriers.
Real-time customer service. Support representatives see billing, network status, and service context instantly, resolving issues faster. Transcripts feed into training loops for continuous improvement.
Network optimization and personalization. Combine usage data with demographics to predict capacity strain and optimize infrastructure. Marketing teams segment by value and churn risk, recommending targeted offers.

FAQs

How does agentic identity resolution differ from static rules?

Static rules require manual maintenance and don't adapt. Agentic identity resolution uses machine learning to continuously improve as data patterns change, reducing manual work and improving accuracy over time.

How does real-time access work for regulated data?

Lakebase queries pull from Unity Catalog, which enforces governance policies before returning results. Customer service reps only see authorized records, and all access is logged for compliance audit.

How long does it take to see business value?

Identity resolution begins within weeks of data ingestion. Initial churn prediction and segmentation are visible in the first month; complex models take 2-3 months as teams iterate.