Data Analytics enables the successful management of the CSP’s business processes

Large quantities of network and customer data are readily available for CSPs (Communications Service Provider) to use for planning and improving their operations. Data analytics is applied to this big data to efficiently manage CSP processes such as infrastructure and network lifecycle management as well as product and service lifecycle management. Data analytics also helps the CSP to facilitate business processes such as marketing, offer management and customer relationship management.

CSP business process framework

Use cases in the business domain where data analytics is of great importance include management of marketing campaigns, churn prevention, customer acquisition, fraud prevention, product and service development, and the provision of competitive offers and packages. In terms of network lifecycle management, data analytics supports network design and planning, network maintenance, optimisation and improvement of customer experience, as well as network operations.

Four types of data analytics are used to interpret customer and network data and turn information into meaningful actions:

  • Descriptive analytics: what is the current performance of the company, service, or network
  • Diagnostic analytics: in case of underperformance, what are the root causes of the observed problems?
  • Predictive analytics: what is the probability that a future event or incident will occur.
  • Prescriptive analytics: Based on the analysis of what events have occurred, why they have occurred, and what alternative options are available, decide what action to recommend.
Data Analytics Types

AI and ML empower data analytics to manage network lifecycle processes

These four types of data analytics leverage artificial intelligence (AI) and machine learning (ML)/Deep learning methodologies to unlock the full potential of the customer and network data and drive the appropriate actions. ML uses statistical learning techniques and the training of data analytics models by using training data. Deep learning methodologies apply more advanced learning algorithms.


AI and ML methodologies enable comprehensive data analytics to support the management of both business and network lifecycle processes. Network lifecycle processes range from planning and design of network assets and configuration to network operations and network performance improvement and can be divided into the following categories:

  • Data Analysis: Analyse historical network performance and customer experience data
  • Demand Planning: Traffic analysis and predict future network load and capacity requirement
  • Investment Planning: Predict required investments and revenues for predicted network capacity levels
  • Design and Build: design, plan and deploy network assets and technology
  • Operate and Maintain: Maintain network performance and customer experience through day-to-day operations actions and carry out continuous improvement of network performance and customer experience
Network lifecycle management processes  

Examples of current applications of AI and ML data analytics methodologies for network lifecycle use cases include:

  • Predictive network design & capacity planning and network capex optimisation,  
  • Network monitoring, performance management and service assurance  
  • Customer experience management, geolocation, crowd source analysis  
  • Self Organising Networks (SON) and predictive network management  
  • Predictive energy saving  
  • Predictive maintenance  
  • Zero touch automation/operation and dark/virtual NOC/SOCs

The first mentioned AI/ML use case primarily concerns network lifecycle processes such as data analysis, demand planning, investment planning and Design & Build, while the others are more related to the Operate & Maintain network lifecycle process.

Omnitele’s AI/ML solution for Demand and Investment Planning

Omnitele applies its AI/ML platform PRIMEA to support CSPs in this field of predictive network design, capacity planning and network capex optimisation. Using advanced Machine Learning algorithms, Omnitele PRIMEA makes strategic decisions to guide operational network design and planning, and Omnitele PRIMEA predicts the customer experience impact of RAN optimisation actions and new investments.

Using the Omnitele PRIMEA AI/ML platform, Omnitele has successfully assisted CSPs with implementing network lifecycle processes such as:

  • RAN, spectrum and technology strategy definition
  • Spectrum valuation
  • Executing investment optimisation and budget planning
  • RAN design & planning, including 5G network design
  • Shared network design
  • RAN performance and customer experience optimisation
  • RAN capacity management
Network lifecycle use cases supported by Omnitele PRIMEA

Omnitele PRIMEA is complementary to other AI/ML tools like SON, predictive maintenance or zero-touch operation. The outputs of Omnitele PRIMEA drive these applications in both the Design and Build as well as the Operate and Maintain processes.

Omnitele PRIMEA applies AI and ML algorithms to perform data analytics on network performance and configuration data to predict how the future customer experience will evolve. Different network asset and network configuration scenarios can be simulated under different levels of traffic growth and available network investments. The choice of specific network development scenarios will be driven by strategy and/or business inputs, such as competitive performance gaps, introduction of new technologies or perceived substandard customer experience.

Contact us to learn more about how Omnitele can support you with your network lifecycle processes automation strategy and assist you with predictive network design, capacity planning and network capex optimisation.

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