INDEX

Big Data & Machine Learning in the Telecom Network

Mobile operators have long contended with tremendous amounts of data, and in the past relied on summarizing and aggregating data to operate their businesses, so the use and management of large datasets is not new. Telecom data meets the fundamental 3Vs criteria of big data: velocity, variety, and volume, and should be supported with a big data infrastructure (processing, storage, and analytics) for both real-time and offline analysis. 
Telecom big data spending includes distributed storage and computing Hadoop (and Spark) clusters, HDFS file systems, SQL and NoSQL software database frameworks, and other operational software. Telecom analytics software, such as for revenue assurance, business intelligence, strategic marketing, and network performance, are considered separately. The evolution from non-machine learning based descriptive analytics to machine learning driven predictive analytics is also considered.  
 
Revenue, or operator spending, is broken down by big data infrastructure (hardware, software, and services) and telecom analytics (machine learning and non-machine learning, and descriptive and predictive).  Forecasts are through 2021.  This research deals primarily with mobile telecom operators, but some operators include wireline operations, which are serviced by the same big data and machine learning infrastructures.
A selection of vendors in the telecom space are also surveyed.  
 

Table of Contents

  • 1. EXECUTIVE SUMMARY
  • 2. BIG DATA AND MACHINE LEARNING
    • 2.1. CHARACTERISTICS OF TELECOM BIG DATA
    • 2.2. SOURCES OF TELECOM BIG DATA
    • 2.3. ANALYTICS AND MACHINE LEARNING
      • 2.3.1. DESCRIPTIVE ANALYTICS
      • 2.3.2. PREDICTIVE ANALYTICS
      • 2.3.3. MACHINE LEARNING
      • 2.3.4. FEATURE ENGINEERING
  • 3. USE CASES
    • 3.1. PREDICTIVE ANALYTICS
    • 3.2. NETWORK MANAGEMENT
    • 3.3. PREDICTIVE MAINTENANCE
    • 3.4. SELF-ORGANIZING/OPTIMIZING NETWORK
    • 3.5. SALES AND MARKETING
    • 3.6. DYNAMIC PRICING
    • 3.7. STRATEGIC MARKETING
    • 3.8. NEW BUSINESS MODELS
    • 3.9. MACHINE LEARNING ON-DEVICE
      • 3.9.1. HOW MACHINE LEARNING ON THE DEVICE WORKS
      • 3.9.2. ON-DEVICE USE CASES
    • 3.10. CYBER SECURITY
    • 3.11. CUSTOMER EXPERIENCE MANAGEMENT
    • 3.12. DATA MONETIZATION AND OTHER USE CASES
  • 4. OUTLOOK
    • 4.1. WORLDWIDE TELECOM AND IT BIG DATA
    • 4.2. WORLDWIDE TELECOM BIG DATA BY COMPONENT
    • 4.3. REGIONAL TELECOM BIG DATA REVENUE
    • 4.4. REGIONAL ANALYTICS SOFTWARE REVENUE
    • 4.5. REGIONAL ML ANALYTICS SOFTWARE REVENUE
    • 4.6. REGIONAL NON-ML ANALYTICS SOFTWARE REVENUE
    • 4.7. WORLDWIDE ML REVENUES BY COMPONENT
    • 4.8. WORLDWIDE NON-ML REVENUES BY COMPONENT
  • 5. SUMMARY AND CONCLUSION
    • 5.1. RECOMMENDATIONS FOR OPERATORS.
    • 5.2. RECOMMENDATIONS FOR VENDORS
  • 6. VENDOR ACTIVITY
    • 6.1. ALLOT
    • 6.2. ARGYLE DATA
    • 6.3. ERICSSON
    • 6.4. GUAVUS
    • 6.5. HUAWEI
    • 6.6. INTEL
    • 6.7. NOKIA
    • 6.8. OPENWAVE MOBILITY
    • 6.9. PROCERA NETWORKS
    • 6.10. QUALCOMM
    • 6.11. ZTE