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【分析レポート:技術】通信ネットワークのビッグデータと機械学習

Big Data & Machine Learning in the Telecom Network

3Q 2016 | Technology Analysis Report | AN-2302 | 32 pages | 9 tables | 9 charts | PDF

 

出版社 出版年月価格 ページ数図表数
ABI Research
ABIリサーチ
2016年9月お問い合わせください 32 18

サマリー

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.


目次

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

 

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プレスリリース

[プレスリリース原文]

Mobile Broadband Operators Transform with Big Data and Machine Learning on the Journey to Digital Service Providers

 

Scottsdale, Arizona - 11 Oct 2016

Mobile broadband operators are ramping up spend for big data and machine learning as they transform into digital service providers. With a long history of handling huge datasets, and with their path now blazed by the IT ecosystem, mobile operators will devote more than $50 billion to big data and machine learning analytics through 2021, forecasts ABI Research. Machine learning technologies will lead operators to profoundly change how they manage the telecom business.   

“Machine learning-based predictive analytics are applicable to all aspects of the telecom business,” says Joe Hoffman, Managing Director and Vice President at ABI Research. “It is important that operators master and internalize these technologies and not rely solely on their vendors’ expertise. Executives that overlook big data and machine learning risk irrelevance.” 

Machine learning can deliver benefits across operators’ telecom operations with financially-oriented applications, including fraud mitigation and revenue assurance, which currently make the most compelling cases. Legacy analytics are rule-based solutions that cannot keep pace with the criminal element, but machine learning excels at spotting trending anomalies. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization. Even sales, marketing, and customer experience teams will benefit as machine learning helps to innovate and reengineer business processes.

Telecom big data solutions include the commercial IT kit; the open source, Java-based Hadoop ecosystem, SQL/NoSQL data management, and orchestration platforms. Spending on this infrastructure will exceed $7 billion in 2021. But the biggest growth and most value comes from using predictive analytics to improve telecom business performance, with machine-learning-based predictive analytics to grow at nearly 50% CAGR and reach $12 billion through 2021.

Leading infrastructure vendors—Ericsson, Huawei, Nokia and ZTE—are delivering big data and machine learning solutions oriented toward network operations. Even Hadoop/NoSQL startups like Argyle Data, and chip vendors, led by Intel and Qualcomm, are delivering solutions pertinent to the telecom operator. 

“These are exciting times for mobile broadband as we see the convergence of IT and telecom, virtualization with software-defined networking, or SDN, and network function virtualization, or NFV, the adoption of artificial intelligence machine learning, and the ubiquitous coverage of all-IP 4G and 5G networks,” concludes Hoffman. “With the rise of commercial cloud infrastructure and machine learning services, every mobile operator can be a big data company. In just a few years, we will see the mobile networks of tomorrow manifest into giant, distributed supercomputers, with radios attached, continuously reengineered by machine learning.”

These findings are from ABI Research’s Big Data & Machine Learning in the Telecom Network and Telco Big Data, Analytics and Machine Learning market data. The reports are part of the company’s Future Networks sector, which includes research, data, and analyst insights.

 

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