Data and Analytics for better business outcomes
Unlock the value of data with telecom analytics and Artificial Intelligence (AI); multi-vendor, cross-domain Data and Analytics solutions that transform data at scale into actionable insight.
Superior service experience demands optimal telecom analytics
Data insights that drive better business outcomes come from a multitude of sources within the network and IT systems, in addition to external sources such as partner applications.
When coupled with triggers and action (ideally automated), telecom analytics, in the form of mediation and intelligent data processing, addresses several business imperatives – from facilitating accurate charging and billing to increasing operational efficiencies, reducing processing times and enabling effective decision making,
Across the business, data insights contribute positively to:
- service experience (including service quality and customer experience)
- customer experience, including customer loyalty / reducing churn
- employee attrition reduction
- revenue growth
- operational efficiency
Real-time, multi-vendor data processing and analytics
Unlock data monetization |
Improve network quality |
Reduce upfront costs with SaaS AI/ML |
Drive insights to business outcomes |
AI, analytics, and automation
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Unlock the power of AI to enrich OSS/BSS
Bring data driven insights to the C-suite for decision making
Consider the quality of data from the start
Many data sources generate data in different formats and with disparate types of information. Data ingestion is the entry point in the data engineering process and might span batch files, transactional events and streaming events. In contrast to traditional batch file processing, stream processing requires latency in the order of seconds or milliseconds. Disparate datasets need treatment and processing before they can be stored or injected in more specialized data pipelines downstream.
Look for these key capabilities to help your business:
- Versatility: Address the diversity of data with different types of information and in different format types.
- Flexibility: Filter irrelevant data and enrich data with metadata to support better analytics.
- Scalability: Respond to data fluctuation and high traffic peaks, handle the changing flow of information while maintaining efficiency.
- Real-time processing: Data should be ready to be consumed in real time, enabling use cases that operationalize machine learning (ML) models, for example anomaly detection.
Troubleshoot and resolve service experience issues faster, more accurately, more efficiently
Telecom analytics, 5G Core and software probes combine to improve service experience
Together with software probes and 5G Core, the pre-integrated Ericsson Expert Analytics solution improves service quality and 5G experience, together with implementing the centralized NWDAF 5G function. Customer centric monitoring of events and service quality identifies potential problems, feeding troubleshooting processes and workflows to resolve issues and optimize service experience.
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Data and Analytics portfolio
Customers demand customization, quality, consistency and relevance in all their services.
With 5G and its enablement, actionable data and AI powered insights can optimize the customer experience. And with the expected increase of 5G-related data in the network, traditional approaches to data collection are unsustainable.
New services, encrypted traffic and streaming data from multiple sources cannot be measured and managed by traditional metrics and traditional means.
No single data source contains enough information to truly understand the totality of the situation.
New approaches are needed to:
- Drive data from the network to inform the business in the pursuit of data driven operations.
- Leverage AI to unlock customer, service and operations insights to drive OSS/BSS efficiency and business growth
- Extract customer-centric intelligence so actions can be taken based on insights and recommendations.
- Make data-driven business decisions and realize new revenue through a variety of use cases .
- Improve service experience, including customer experience and service quality.
- Exceed customer expectations, within resource constraints, optimizing network capacity and operations.
- Let data silos give way to data integrity and data coherency .
- Establish a common framework for all BSS and OSS components capturing AI insights to achieve data monetization and operational excellence