In the current radically growing digital era, Telecom industry has become more competitive due to business inflows from over-the-top service providers and evolving business models. Monitoring and maintaining a better QoS without compensating on Customer Satisfaction and OpEx becomes a challenge for the operators. In addition, analysts predict that the data generated from the multitude of complex networks and applications, would rise from peta-scale to zeta-scale. Survival of the best depends on the adoption of best-in-class solution for data orchestration, performance monitoring, predictive network maintenance, proactive suggestions, etc.

Big data along with Machine Learning and Artificial Intelligence would be the key differentiators for any telecom operator to set themselves ahead of the competitors. Data Management and visualization of the orchestrated data to build predictive engines, would bring down the OpEx as well as MTTR. Applying artificial intelligence as an add-on would deter the dependency on L1/L2 support engineers.

Telecom sector has understood the significance of ML and AI as it helps them to derive insights of Customers, Network Structure Organization, Network Elements’ performance, Data flow across multiple components, Financial expenditure, Application performance, vendor management, etc. Some derived use cases include, understanding Customer requirements across multiple geographies, region-wise best-fit product offerings, manage user profiles, derive patterns on Network Element behavior to predict anomaly, Service assurance, Test case prioritization, Network Planning, etc.

Prodapt with its expertise on Big Data, Machine Learning and Artificial Intelligence, could help any telecom operator to tap potential insights from their existing Database and Network Components. Prodapt’s capabilities include:

  • Data Ingestion / Data Orchestration
  • Data Mining
  • Data Visualization
    • Dashboards
    • Business Insights & Intelligence
    • Descriptive Analytics
  • ML Model Building
    • Predictive Analytics
    • Prescriptive Analytics
    • Statistical Modeling
    • Deep Learning
    • Reinforcement Learning
  • ML Model Deployment
    • Micro-services
    • DevOps
  • ML Performance Tuning
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