Telecom Data Analytics Built on the Databases and Streams Behind Subscribers, Sessions, and the Network
MinervaDB engineers the data platforms communications service providers run on: call, data, and signalling records mediated at carrier scale, network counters and probe telemetry joined to the subscriber and cell they describe, billing and CRM reconciled to usage for revenue assurance, and the residency and retention controls a regulated operator has to prove. Delivered by the team that operates the Cassandra, PostgreSQL, Oracle, Kafka, and ClickHouse platforms beneath it.
Telecom Data Analytics Built on Database-Grade Engineering
An operator generates more data per subscriber per day than most enterprises generate per customer per year, from systems that were never designed to agree with each other. Making the network, the customer, and the bill describe the same event is a data engineering problem before it is an analytics problem.
Telecom data analytics at MinervaDB starts from the mediated usage fact: every voice, messaging, data, and signalling record, normalised across 4G, 5G NSA and SA, fixed, and wholesale sources, keyed to a subscriber, a device, a cell or access node, and a rated product, with the network quality indicators of the session carried on the same row. Every downstream question in an operator, from churn propensity to revenue leakage to where the next site goes, is a query on that fact once it exists and a reconciliation project until it does.
The practice sits within our data engineering and data strategy work and feeds our decision intelligence, MLOps, and enterprise generative AI practices for communications. Our engineers operate the Apache Cassandra and Redis and Valkey tiers behind subscriber state and session control, the Oracle and PostgreSQL systems behind billing, CRM, and inventory, the Kafka pipelines that move network events, and the ClickHouse and cloud warehouse platforms telecom data analytics is served from, so a platform from us is sized from measured event rates and rehearsed for the traffic peaks and outage storms that decide the quarter.
Telecom Data Analytics Services, End to End
Six workstreams, delivered as a bounded programme, embedded engineering, or 24×7 managed data operations.
xDR Mediation & Usage Data Platforms
Telecom data analytics begins here: ingestion of CDR, EDR, IPDR, and signalling records from mediation, packet core, IMS, and roaming partners into one usage fact with a common subscriber, device, cell, and product key. Late, duplicate, and re-sent records are deduplicated and versioned so a rated event can always be traced to the network record it came from.
Network Performance & Customer Experience Analytics
Telecom data analytics for the network: performance-management counters, probe and trace data, and RAN and core KPIs joined to the subscribers and sessions they affected. Per-cell, per-service, and per-customer quality of experience scored continuously on ClickHouse, so a degradation is known by the customer it touched before the complaint arrives, and capacity planning runs on measured demand rather than averages.
Churn, ARPU & Customer Value Modelling
Telecom data analytics for the customer: feature pipelines from usage, experience, billing, care interactions, and device lifecycle into churn propensity, next-best-offer, and customer lifetime value models, served into CRM and campaign systems inside the retention window. Delivered with our MLOps consulting practice so models are reproducible, monitored for drift, and retrained on evidence.
Revenue Assurance & Fraud Analytics
Telecom data analytics for the money: usage reconciled to rating and billing per event class and per period, leakage located by switch, product, and tariff, and fraud patterns such as SIM-box, IRSF, and subscription fraud detected on streaming usage within minutes. Every variance is explainable to the source record, which is what an auditor and a regulator both ask for.
Subscriber Master Data & Network Inventory Governance
One subscriber and one cell hierarchy across BSS, OSS, HLR/HSS/UDM, and inventory, with effective-dated history for number ports, SIM swaps, device changes, and site re-plans. Consent, residency, and retention rules enforced at the platform, delivered with our data governance consulting practice.
Platform Engineering & Managed Operations
The telecom data analytics platform itself: streaming ingestion, lakehouse core, warehouse and real-time tiers, semantic layer, and FinOps for the whole estate, then 24×7 operations under SLOs. Cassandra, Kafka, ClickHouse, Oracle, and PostgreSQL operated by engineers who run them in production for operators today.
Where Telecom Data Analytics Gets Its Data, and Why It Is Hard
The network, the customer, and the bill each describe the same session in their own vocabulary. The map below is what every telecom data analytics platform has to reconcile before the first KPI is trustworthy.
Network telemetry is the largest and the least structured. In telecom data analytics, performance counters arrive per vendor and per release with their own schemas and time bases; probes and traces are sampled; the 5G core emits per-session records that dwarf 4G volumes. We land every source as received, version its schema, and normalise in code, so a vendor upgrade re-flows rather than breaks.
Mediation is authoritative for what was used, not for who used it. xDRs carry identifiers, not customers. Resolving IMSI, MSISDN, and IMEI to an account and a product, as of the moment of the event and through ports and swaps, is the join that turns usage into revenue and experience analytics.
Billing and the network live in different clocks. Rating cycles, bill runs, and adjustments describe the month; the network describes the second. Reconciling them per event class and per period, with the differences explained rather than written off, is what revenue assurance actually is.
- One subscriber key across HSS/UDM, CRM, billing, and care with effective dating through ports, swaps, and migrations
- One cell and site hierarchy across RAN inventory, planning tools, and vendor counters, including re-plans and refarming
- Schema-versioned ingestion of vendor counters and 3GPP records so releases re-flow rather than break dashboards
- Deduplication and late-record handling for xDRs; every rated event traceable to its network record
- Session quality indicators carried on the usage fact at record level, not joined later from a sample
- Usage, rating, and billing reconciled per event class per period with variances located to source
The Telecom Data Analytics Platform We Engineer
Streaming ingestion at carrier scale, a lakehouse core with the harmonised usage fact, a real-time tier for network and fraud operations, a warehouse tier for finance and marketing, and a semantic layer every function reads.
Cassandra and Kafka at the Edge of the Platform
Telecom data analytics inherits the platforms the network already depends on. Subscriber state, session context, and policy decisions live in Cassandra and Redis or Valkey because they must answer in milliseconds at any scale; usage and network events move over Kafka because nothing else keeps up at carrier volumes. We operate these tiers in production for operators, which means the analytics platform is fed from them without adding load or latency to the systems serving live traffic. Our Cassandra consulting practice owns that boundary.
ClickHouse for Network Operations and Fraud in Real Time
Billions of xDRs and counters a day, queried by cell, subscriber, and minute, is a workload that warehouses answer slowly and expensively and that row stores cannot answer at all. ClickHouse serves per-cell quality of experience, degradation detection, fraud scoring on streaming usage, and capacity dashboards in sub-second time from the same harmonised core, with tiered storage keeping the regulatory retention window affordable. Delivered through our ClickHouse partner practice at ChistaDATA.
Oracle, PostgreSQL, and the Warehouse for the Business
Telecom data analytics has to agree with the bill. Rating, billing, CRM, and inventory usually live in Oracle or PostgreSQL, extracted by CDC so the platform sees adjustments and credits as they happen; finance, marketing, and regulatory reporting are served from Snowflake or BigQuery on the same reconciled history; churn and capacity models run on Databricks or Spark over the lakehouse core. Platform choice is scored on measured workload and operated through our cloud FinOps practice.
Where Telecom Data Analytics Pays Back First
Sequenced by the data each one needs, because a use case that depends on the harmonised usage fact cannot precede the harmonisation.
| Use case | What it needs from the platform | How we measure it |
|---|---|---|
| Revenue assurance and leakage recovery | Mediated usage reconciled to rating and billing per event class and period; variance located by switch, tariff, and product | Leakage identified and recovered as a share of revenue, and variance explained to source within the billing cycle |
| Fraud detection on streaming usage | Usage events enriched with subscriber, device, and destination context in-stream; rules and models scored within minutes | Fraud loss per month, detection latency, and false-positive rate at the case-handling level |
| Customer experience and proactive care | Session quality on the usage fact, per-cell and per-subscriber QoE scores, joined to care interactions | Complaints predicted before contact, repeat-contact rate, and NPS movement in affected segments |
| Churn propensity and retention | Usage, experience, billing, care, and device features at subscriber grain; monitored models served into CRM | Retention lift in treated cohorts against holdouts, and revenue retained per campaign |
| Capacity planning and site investment | Cell-level demand and quality history, subscriber value by cell, forecast growth by service | Congested cells resolved before quality thresholds are breached; investment ranked by revenue at risk |
| Wholesale, roaming, and interconnect settlement | Partner records matched to own usage, rate cards, and disputes with an audit trail | Settlement variance and dispute cycle time, per partner and per period |
Telecom Data Analytics Operated to the Same Standard as the Network
A churn model trained on usage from a feed that silently dropped a switch is a wrong decision with a confident number attached. We run telecom data platforms with the operating discipline of our 24×7 Remote DBA practice.
Under managed telecom data analytics operations, every telecom data analytics feed and dataset carries three SLOs: ingestion lag (age of the newest record from each switch, node, and BSS source in each tier), completeness (records per source against mediation and switch totals, subscribers and cells present against inventory), and retention (records held for exactly the mandated window per jurisdiction and record class, and provably deleted after it). Schema changes from vendor releases are detected and versioned before they reach a dashboard.
Incidents follow the same severity model as our database support: a missing usage feed from a switch during a bill run is an S1 with a 15-minute response target; a late counter load is an S2. Every incident closes with a root-cause analysis and a preventive action. Before each launch, tariff change, or migration we rehearse the load with replayed traffic, confirm capacity on the real-time tier, and freeze subscriber and cell hierarchy changes for the window.
- 24×7 monitoring of feed arrival per switch and node, schema drift, record-count variance, and identifier conflicts
- Usage-to-rating-to-billing reconciliation per event class per period with variances located to source
- Retention and deletion enforced per jurisdiction and record class, with evidence for the regulator
- Data residency, lawful-access boundaries, and consent enforced at the platform, not in the dashboard
- Steward queue for number ports, SIM swaps, site re-plans, and product catalogue changes
- Runbooks with verification before and validation after every model, hierarchy, or schema change
Why Operators Choose MinervaDB for Telecom Data Analytics
We operate the network-facing databases and the analytics platform
Most telecom data analytics consulting starts at the dashboard. Our engineers operate the Cassandra, Redis, Oracle, and PostgreSQL systems subscriber state, billing, and inventory live in, the Kafka pipelines that carry usage, and the ClickHouse and cloud platforms the analytics is served from, so there is one accountable team from switch to insight.
Vendor-neutral, measurement-driven
We sell no platform licences and earn no referral fees. Every platform and model recommendation names the measurement that justifies it, and we will say when the estate you already own, including your vendor's own analytics suite, is the right answer.
Production posture from day one
Feeds, hierarchies, and models are treated as production systems. Changes are staged and reversible, subscriber and cell hierarchy edits carry approval gates, and every procedure states its blast radius and rollback path before it runs.
Knowledge transfer by default
Mediation rules, harmonisation logic, model documentation, retention policy as code, and runbooks are handed over. Your team should be able to run the platform we build; if they choose to have us keep running it, that is a decision, not a dependency.
"An operator already knows everything about every session. The job is to make the network, the customer, and the bill agree on it, fast enough to act while the session is still happening."
— The MinervaDB Data Engineering TeamHow a Telecom Data Analytics Engagement Runs
Discover
Inventory of network, mediation, BSS, OSS, and partner sources; measured event rates and schema churn; identifier conflicts across HSS, CRM, and billing; retention obligations by jurisdiction; the questions nobody can answer today.
Design
Reference architecture, harmonisation and subscriber master design, semantic layer, real-time versus warehouse tiering, platform scorecard, cost model, and a staged plan starting with the highest-value use case.
Build & Validate
Streaming and batch pipelines, harmonisation, masters, semantic layer, and serving tiers delivered as code; reconciliation to mediation and billing totals; first use case live on production traffic; runbooks before cut-over.
Operate & Improve
SLO-governed operations through launches and bill runs, monthly reviews with network, customer, and finance owners, model retraining on evidence, and knowledge transfer until your team owns the platform.
Telecom Data Analytics: Frequently Asked Questions
What does MinervaDB's telecom data analytics service include?
xDR mediation and usage data platforms, network performance and customer experience analytics, churn and customer value modelling, revenue assurance and fraud analytics, subscriber master data and network inventory governance, and platform engineering with 24×7 managed operations. Each can be delivered as a bounded programme, embedded engineering, or managed data operations.
Do you work with the network-facing systems or only the analytics layer?
Both, and that is the point. Telecom data analytics is fed from Cassandra, Redis or Valkey, Kafka, Oracle, and PostgreSQL tiers that also serve live traffic, and our engineers operate those in production for operators. The analytics platform is designed to consume from them without adding load or latency to session control, policy, or billing.
How do you handle vendor counter and record formats that change with every release?
Every source is landed as received and its schema versioned; normalisation to a common model runs as code, so a vendor release re-flows through the pipeline rather than breaking downstream dashboards. Where a release changes a counter's meaning, the change is recorded in the catalogue and every KPI that depends on it is flagged for review.
Can you meet data residency, lawful-access, and retention requirements?
Yes. Residency is enforced by where each tier is deployed and where the platform is permitted to replicate; retention is enforced per jurisdiction and record class with deletion evidence; lawful-access boundaries are kept outside the analytics platform entirely. These are platform controls with audit trails, not dashboard filters, and they are designed with your legal and regulatory teams before the first record is loaded.
Which platforms do you recommend for telecom data analytics?
Whichever fits the measured workload: Kafka and Flink for streaming; Apache Iceberg or Delta Lake as the lakehouse core; ClickHouse for real-time network, experience, and fraud analytics at xDR scale; Snowflake or BigQuery for finance and marketing; Databricks or Spark for churn and capacity models; Cassandra and Redis or Valkey where they already serve the network. We are vendor-neutral and often recommend keeping the platform an operator already owns.
How does telecom data analytics relate to your AI services?
The harmonised platform is what decision intelligence for retention and capacity, MLOps for churn, fraud, and experience models, and generative AI assistants for care and network operations all depend on. Most operator AI programmes that stall do so because the mediation and harmonisation beneath them were never engineered.
Let's Make Your Network Data as Trustworthy as Your Bill
Talk to a MinervaDB principal consultant about the usage feeds, the experience analytics, or the revenue assurance platform in front of you. The first conversation is always with an engineer, never a salesperson.
Schedule a Consultation → Download the MinervaDB Corporate Flyer (PDF) →Telecom and Real-Time Data Resources from MinervaDB
- Data Engineering Consulting and Managed Pipelines
- Data Strategy and Analytics Consulting
- Apache Cassandra Consulting
- ClickHouse Consulting
- Data Governance Consulting
- Data Quality SLOs: Indicators, Error Budgets and Burn-Rate Alerts
- Feature Store Architecture on PostgreSQL and ClickHouse
- Model Drift Monitoring on ClickHouse
- TM Forum Information Framework (SID)
- 3GPP specification series