Preparing OSS/BSS Data for Machine Learning Models

Telecom networks generate vast amounts of data daily, yet few operators fully leverage this information for automation. This results in fragmented systems, holding critical analytics in silos, and leaving networks reactive instead of predictive.

Modern networks require intelligence that spans operations and business. Traditional approaches fail to meet customer expectations for problem resolution or predictive service adjustments. The solution lies in architectures that break down barriers between customer, network, and revenue systems.

Quick Take

• Unified access to data is non-negotiable for predictive and autonomous network solutions.
• Consolidating siloed systems helps technical and business teams make informed decisions.
• Real-world implementations demonstrate improvements in risk mitigation and operational agility.
• Fragmented data limits the ability of artificial intelligence to automate workflows or detect problems.

OSS/BSS data: What is it?

OSS and BSS are two types of systems used in the telecommunications industry. They generate and store large amounts of data.

1. OSS operational data. Responsible for the technical part of the network operation.

Data types:

• Network data. Information about traffic, load, signal quality, and failures.
• Configuration data. Equipment settings, routing, and connection parameters.
• Monitoring and log files. Errors, events, fault signals.
• Resource management data. Frequency usage, number resource, IP addresses.

2. BSS business data. Responsible for the client and commercial side.

Data types:

• Subscriber data. User profiles, personal settings.
• Billing and tariffs. Call history, Internet sessions, invoices, tariffs, and payments.
• CRM data. Customer support requests, complaints, and interaction history.
• Sales and Marketing. Connection statistics, response to promotions.

Impact on operations and improved experience

Single sign-on streamlines workflows and transforms customer outcomes. Proactive service adjustments now occur before users notice slowdowns, and automated problem resolution processes reduce the volume of requests from early adopters.

This allows operators to predict capacity needs, personalize offerings, and address customer churn risks.

Data Management Strategies for Modern Telecoms

Operators need frameworks that handle large data streams. Architectures that balance scale and availability are a priority, a principle embodied in the V³ x A³ framework.

Implementing single data stores with proven tools

Consolidating disparate information sources starts with platforms. Reduction and mirroring capabilities create single points of access to disparate data sets. This approach addresses three key challenges:

• Scale. Transaction records, network sensors, and customer interactions generate petabytes of data daily.
• Velocity. Real-time processing turns raw metrics into actionable information.
• Diversity. Structured payment data coexists with unstructured social feedback.

Ensuring data accuracy and operational consistency

Accuracy requires validation systems. This requires dedicated platforms, where API-driven pipelines support:

• Availability. 24/7 access for technical and business teams.
• Accessibility. Secure, seamless cross-departmental sharing.
• Affordability. Cost control through automated quality checks.

With integrations with multiple cloud providers, it provides reliable information flows that help you make decisions.

Using OSS/BSS AI training data for autonomous telecom operations

Autonomous telecom operations is an approach where a mobile or fixed network manages its configurations, monitoring, detection, and resolution of problems with minimal human intervention.

How OSS/BSS data becomes training for AI

1. OSS data

• Traffic and load. AI models learn to predict peak hours and balance the network.
• Crashes and log files. AI detects failure patterns and builds predictive service.
• Quality of service. Automatic resource reconfiguration to support SLA.

2. BSS data.

• User behavior. AI predicts churn prediction and selects personalized tariffs
• Financial data. Financial data. Detects fraudulent schemes, anomalous transactions, and potential billing errors.
• CRM data. Chatbot models for customer support, automatic resolution of typical requests.

Agent AI: Enabling Proactive and Predictive Decision Making

Analyzing patterns in isolated workflows helps technology agents detect capacity bottlenecks or risks of service degradation hours before they impact users. Rather than simply flagging anomalies, these systems execute predefined protocols, such as redirecting traffic during congestion or adjusting bandwidth allocation.

Cloud Innovation: Driving OSS/BSS Modernization

Traditionally, OSS and BSS have operated in on-premises data centers. But with the advent of, IoT, and AI, there is a need to:

• Scale, compute, and storage.
• Provide flexibility for new services.
• Reduce infrastructure costs.

Move to cloud models for scalability

Migration from on-premises systems unlocks transformational benefits:

• Scale industry. Helps adjust capacity during traffic peaks or launch new services.
• Cost efficiency. Move from fixed capital expenditures to pay-per-use models.
• Scalability. Increase resources for peak loads.
• Flexibility and rapid service adoption. Operators quickly deploy new services without purchasing expensive equipment.
• Cost reduction. Pay-as-you-go, lower hardware investment.
• Integrate with AI/ML. The cloud provides computing resources for models that analyze OSS/BSS data for autonomous operations.
• Global availability. Cloud OSS/BSS is centrally serviced for multiple countries or regions.

Integrating Generative and Agent-Based AI into Telecom OSS/BSS Systems

Telecom operators are implementing intelligent systems, changing network management from reactive surveillance to predictive management. These technologies analyze patterns in infrastructure and customer interactions.

Using AI to optimize the network in real time

Telemetry is the automatic data collection and transmission from devices, systems, or sensors to a monitoring or analytics center.

Key benefits:

• Problem detection. For example, equipment overheating or speed drops in real time.
• Automatic launch of self-healing scenarios.
• Transfer of BSS commands to CRM. The customer receives a message even before he has time to complain.

Building a Unified Data Ecosystem for OSS Operations

A unified data ecosystem for OSS is an integrated platform where all network, service, equipment, and customer data is collected, unified, and analyzed in real time.

Goal: Ensure network transparency and enable autonomous telecom operations.

Key Components

Data Collection

• Telemetry from equipment.
• Logs from OSS systems, billing, CRM, NOC/SOC.
• External sources, IoT, partner systems.

Unification and storage

• Data Lake or Data Fabric for storing heterogeneous data.
• Standards: OpenTelemetry, TM Forum Open APIs.
• A unified data model.

Analytics and AI/ML

• Real-time anomaly detection.
• Predictive Maintenance for equipment.
• Self-Organizing Networks for 5G.
• AI monitors network conditions in real time, ensuring service assurance and adherence to SLA.

Integration with BSS

• Automatic coordination of OSS with BSS.
• Integration with BSS enables AI-driven customer analytics to personalize offerings and improve engagement.

Automation and orchestration

• Using AI Ops + intent-based networking.
• Self-healing: the system fixes the problem itself.

Advantages of a single OSS data ecosystem

1. Transparency. The operator sees the entire network as a whole organism.
2. Autonomy. AI makes decisions instead of a person.
3. OPEX reduction. Lower costs for manual operations.
4. Quality of services. Automatic SLA support.
5. Scalability. Readiness for 5G, IoT, and edge services.

FAQ

How does the modernization of legacy systems improve the delivery of telecommunications services?

It allows telecom operators to increase data processing speed, automate operations, ensure service continuity, and introduce new services for customers.

What tools ensure reliable data quality in cloud environments?

In cloud environments, reliable data quality is ensured by ETL/ELT platform tools, data management systems, Data Quality, and Master Data Management solutions, observability and monitoring frameworks.

Why are open APIs important for OSS/BSS transformation?

Open APIs provide standardized integration between different systems, simplify data exchange, accelerate the launch of new services, and allow operators to scale the ecosystem easily.

How do agent-based AI systems improve network reliability?

Agent-based AI systems improve network reliability by analyzing telemetry and detecting anomalies in real time.

What security measures protect unified telecom data ecosystems?

Protected by multi-layered security, including end-to-end encryption, network segmentation, Zero Trust access control, telemetry monitoring, and AI.