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通信構造化データ、ビッグデータ、解析市場:ビジネス事例、分析、予測 2015-2020年

Market for Telecom Structured Data, Big Data, and Analytics: Business Case, Analysis and Forecasts 2015 - 2020

 

出版社 出版年月電子版価格 ページ数
Mind Commerce
マインドコマース
2015年3月US$2,995
シングルユーザライセンス
153

サマリー

Overview

The telecommunications industry is investing heavily in developing the analytical tools and services to take advantage of both their traditional structured data and unstructured (big) data resources.  The goals of each carrier program vary, but share some commonalities including the desire to improve business intelligence gathering, customer care and operations.  Carriers are also working diligently to better understand how to monetize data assets, which is often manifest in new products and services at the business-to-business (B2B) level.

This report provides an in-depth assessment of the global Big Data and telecom analytics markets, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry from 2015 to 2020.  All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

  • Telecom network operators
  • Telecom infrastructure suppliers
  • Big Data and analytics companies
  • Data as a Service (DaaS) companies
  • Cloud-based service providers of all types
  • Data processing and management companies
  • Application Programmer Interface (API) companies
  • Public investment organizations including investment banks
  • Private investment including hedge funds and private equity

Report Benefits:

  • Forecasts telecom related Big Data from 2015 to 2020
  • Understand the emerging need for Big Data mediation
  • Identify telecom structured data services and solutions
  • Identify sources of data from next generation applications
  • Understand unstructured (Big) data systems and solutions
  • Learn about sources of data in telecom systems and processes
  • Understand the role and importance of deep packet inspection


目次

Table of Contents

1              Introduction       11

1.1          Executive Summary        11
1.2          Topics Covered 13
1.3          Key Findings       14
1.4          Target Audience               15
1.5          Companies Mentioned 16

2              Big Data Technology and Business Case 19

2.1          Structured vs. Unstructured Data             19
2.1.1      Structured Database Services in Telecom              20
2.1.2      Unstructured Data from Apps and Databases in Telecom              21
2.1.3      Emerging Hybrid (Structured/Unstructured) Database Services  22
2.2          Defining Big Data              25
2.3          Key Characteristics of Big Data   25
2.3.1      Volume                26
2.3.2      Variety 26
2.3.3      Velocity                26
2.3.4      Variability            26
2.3.5      Complexity         27
2.4          Capturing Data through Detection and Social Systems     27
2.4.1      Data in Social Systems    29
2.4.2      Detection and Sensors  31
2.4.3      Sensors in the Consumer Sector               33
2.4.4      Sensors in Industry         34
2.5          Big Data Technology       34
2.5.1      Hadoop                35
2.5.1.1   MapReduce       35
2.5.1.2   HDFS     35
2.5.1.3   Other Apache Projects  35
2.5.2      NoSQL  35
2.5.2.1   Hbase   36
2.5.2.2   Cassandra           36
2.5.2.3   Mongo DB           36
2.5.2.4   Riak        36
2.5.2.5   CouchDB              37
2.5.3      MPP Databases                37
2.5.4      Others and Emerging Technologies          37
2.5.4.1   Storm    37
2.5.4.2   Drill        37
2.5.4.3   Dremel 38
2.5.4.4   SAP HANA           38
2.5.4.5   Gremlin & Giraph             38
2.6          Business Drivers for Telecom Big Data and Analytics         38
2.6.1      Continued Growth of Mobile Broadband              39
2.6.2      Competition from New Types of Service Providers           40
2.6.3      New Technology Investment     40
2.6.4      Need for New KPIs         40
2.6.5      Artificial Intelligence and Machine Learning         41
2.7          Market Barriers                45
2.7.1      Privacy and Security: The 'Big' Barrier     45
2.7.2      Workforce Re-skilling and Organizational Resistance        46
2.7.3      Lack of Clear Big Data Strategies                46
2.7.4      Technical Challenges: Scalability and Maintenance            46

3              Key Big Data Investment Sectors              48

3.1          Industrial Internet and M2M      48
3.1.1      Big Data in M2M               48
3.1.2      Vertical Opportunities   48
3.2          Retail and Hospitality      48
3.2.1      Improving Accuracy of Forecasts and Stock Management             49
3.2.2      Determining Buying Patterns      49
3.2.3      Hospitality Use Cases     49
3.3          Media   49
3.3.1      Social Media       49
3.3.2      Social Gaming Analytics 50
3.3.3      Usage of Social Media Analytics by Other Verticals           50
3.4          Utilities 50
3.4.1      Analysis of Operational Data       50
3.4.2      Application Areas for the Future               50
3.5          Financial Services             51
3.5.1      Fraud Analysis & Risk Profiling    51
3.5.2      Merchant-Funded Reward Programs      51
3.5.3      Customer Segmentation              51
3.5.4      Insurance Companies    51
3.6          Healthcare and Pharmaceutical 51
3.6.1      Drug Development         52
3.6.2      Medical Data Analytics   52
3.6.3      Case Study: Identifying Heartbeat Patterns         52
3.7          Telecom Companies       52
3.7.1      Telco Analytics: Customer/Usage Profiling and Service Optimization        52
3.7.2      Speech Analytics              53
3.7.3      Other Use Cases              53
3.8          Government and Homeland Security      53
3.8.1      Developing New Applications for the Public         53
3.8.2      Tracking Crime  53
3.8.3      Intelligence Gathering   54
3.8.4      Fraud Detection and Revenue Generation           54
3.9          Other Sectors    54
3.9.1      Aviation: Air Traffic Control          54
3.9.2      Transportation and Logistics: Optimizing Fleet Usage       54
3.9.3      Sports: Real-Time Processing of Statistics              55

4              The Big Data Value Chain              56

4.1          Fragmentation in the Big Data Value Chain           56
4.2          Data Acquisitioning and Provisioning       57
4.3          Data Warehousing and Business Intelligence       57
4.4          Analytics and Virtualization          57
4.5          Actioning and Business Process Management (BPM)      58
4.6          Data Governance             58

5              Big Data in Telecom Analytics      59

5.1          Telecom Analytics Market 2015 - 2020    59
5.2          Improving Subscriber Experience             60
5.2.1      Generating a Full Spectrum View of the Subscriber          60
5.2.2      Creating Customized Experiences and Targeted Promotions        60
5.2.3      Central Big Data Repository: Key to Customer Satisfaction            60
5.2.4      Reduce Costs and Increase Market Share             61
5.3          Building Smarter Networks          61
5.3.1      Understanding Network Utilization         61
5.3.2      Improving Network Quality and Coverage            61
5.3.3      Combining Telecom Data with Public Data Sets: Real-Time Event Management   61
5.3.4      Leveraging M2M for Telecom Analytics  62
5.3.5      M2M, Deep Packet Inspection and Big Data: Identifying & Fixing Network Defects            62
5.4          Churn/Risk Reduction and New Revenue Streams            62
5.4.1      Predictive Analytics         62
5.4.2      Identifying Fraud and Bandwidth Theft  63
5.4.3      Creating New Revenue Streams               63
5.5          Telecom Analytics Case Studies 63
5.5.1      T-Mobile USA: Churn Reduction by 50%                63
5.5.2      Vodafone: Using Telco Analytics to Enable Navigation     64
5.6          Carriers, Analytics, and Data as a Service (DaaS) 64
5.6.1      Carrier Data Management Operational Strategies             65
5.6.2      Network vs. Subscriber Analytics              65
5.6.3      Data and Analytics Opportunities to Third Parties              66
5.6.4      Carriers to offer Data as s Service (DaaS) on B2B Basis     67
5.6.5      DaaS Planning and Strategies      67
5.6.6      Carrier Monetization of Data with DaaS 71
5.7          Opportunities for Carriers in Cloud Analytics        73
5.7.1      Carrier NFV and Cloud Analytics                73
5.7.2      Carrier Cloud OSS/BSS Analytics                73
5.7.3      Carrier Cloud Services, Data, and Analytics           74
5.7.4      Carrier Performance Management and the Cloud Analytics          75

6              Structured Data in Telecom Analytics      77

6.1          Telecom Data Sources and Repositories                77
6.1.1      Subscriber Data                77
6.1.2      Subscriber Presence and Location Data  78
6.1.3      Business Data: Toll-free and other Directory Services      82
6.1.4      Network Data: Deriving Data from Network Operations 83
6.2          Telecom Data Mining     85
6.2.1      Data Sources: Rating, Charging, and Billing Examples        86
6.2.2      Privacy Issues    87
6.3          Telecom Database Services         88
6.3.1      Calling Name Identity     88
6.3.2      Subscriber Data Management (SDM) Services    93
6.3.3      Other Data-intensive Service Areas         95
6.3.4      Emerging Service Area: Identity Verification        96
6.4          Structured Telecom Data Analytics           96
6.4.1      Dealing with Telecom Data Fragmentation           97
6.4.2      Deep Packet Inspection                99

7              Key Players in the Big Data Market           103

7.1          Vendor Assessment Matrix         103
7.2          Apache Software Foundation     103
7.3          Accenture           104
7.4          Amazon               104
7.5          APTEAN (Formerly CDC Software)            104
7.6          Cisco Systems    105
7.7          Cloudera              105
7.8          Dell        105
7.9          EMC       105
7.10        Facebook            106
7.11        GoodData Corporation  106
7.12        Google 106
7.13        Guavus 107
7.14        Hitachi Data Systems      107
7.15        Hortonworks     107
7.16        HP          108
7.17        IBM        108
7.18        Informatica         108
7.19        Intel       108
7.20        Jaspersoft           109
7.21        Microsoft            109
7.22        MongoDB (Formerly 10Gen)       109
7.23        MU Sigma           110
7.24        Netapp 110
7.25        Opera Solutions                110
7.26        Oracle   111
7.27        ParStream           111
7.28        Pentaho               111
7.29        Platfora                111
7.30        Qliktech               112
7.31        Quantum             112
7.32        Rackspace           112
7.33        Revolution Analytics       112
7.34        Salesforce           113
7.35        SAP        113
7.36        SAS Institute      114
7.37        Sisense 114
7.38        Software AG/Terracotta               114
7.39        Splunk  115
7.40        Sqrrl       115
7.41        Supermicro         115
7.42        Tableau Software            116
7.43        Teradata              116
7.44        Think Big Analytics           116
7.45        Tidemark Systems           116
7.46        VMware (Part of EMC)  117

8              Market Analysis                118

8.1          Market for Structured Telecom Data Services     118
8.2          Market for Unstructured (Big) Data Services       122
8.2.1      Big Data Revenue 2015 - 2020     122
8.2.2      Big Data Revenue by Functional Area 2015 - 2020              123
8.2.3      Big Data Revenue by Region 2015 - 2020                124

9              Summary and Recommendations             126

9.1          Key Success Factors for Carriers                127
9.1.1      Leverage Real-time Data              127
9.1.2      Recognize that Analytics is Not Business Intelligence       128
9.1.3      Provide Data Discovery Services                130
9.1.4      Provide Big Data and Analytics to Enterprise Customers 133
9.2          The Role of Intermediaries in the Ecosystem       133
9.2.1      Cloud and Big Data Intermediation           134
9.2.2      Security, Communications, Billing, and Settlement           135
9.2.3      The Case for Data as a Service (DaaS)      137

10           Appendix: Understanding Big Data Analytics       142

10.1        What is Big Data Analytics?          142
10.2        The Importance of Big Data Analytics      143
10.3        Reactive vs. Proactive Analytics 144
10.4        Technology and Implementation Approaches     146
10.4.1    Grid Computing                146
10.4.2    In-Database processing 146
10.4.3    In-Memory Analytics      149
10.4.4    Data Mining        149
10.4.5    Predictive Analytics         151
10.4.6    Natural Language Processing      153
10.4.7    Text Analytics    157
10.4.8    Visual Analytics 158
10.4.9    Association Rule Learning             159
10.4.10  Classification Tree Analysis          160
10.4.11  Machine Learning            160
10.4.11.1              Neural Networks             162
10.4.11.2              Multilayer Perceptron (MLP)      163
10.4.11.3              Radial Basis Functions    165
10.4.11.4              Support Vector Machines            165
10.4.11.5              Naïve Bayes       165
10.4.11.6              k-nearest Neighbours    166
10.4.11.7              Geospatial Predictive Modelling                167
10.4.12  Regression Analysis        167
10.4.13  Social Network Analysis                168

Figures

Figure 1: Hybrid Data in Next Generation Applications    24
Figure 2: Big Data Components  25
Figure 3: Big Data Sources            28
Figure 4: Capturing Data from Detection Systems and Sensors    32
Figure 5: Capturing Data across Sectors  33
Figure 6: AI Structure     42
Figure 7: The Big Data Value Chain            56
Figure 8: Telco Analytics Investments Driven by Big Data: 2015 - 2020      59
Figure 9: Different Data Types within Telco Environment               69
Figure 10: Presence-enabled Application              81
Figure 11: Calling Name (CNAM) Service Operation          89
Figure 12: Subscriber Data Management (SDM) Ecosystem          94
Figure 13: Data Fragmented across Telecom Databases  98
Figure 14: Telecom Deep Packet Inspection Revenue 2015 - 2020              102
Figure 15: Big Data Vendor Ranking Matrix           103
Figure 16: Unified Communications Incoming Call Routing             120
Figure 17: Network Level Outbound Call Management   121
Figure 18: Big Data Revenue: 2015 - 2020              123
Figure 19: Big Data Revenue by Functional Area: 2015 - 2020       124
Figure 20: Big Data Revenue by Region: 2015 - 2020         125
Figure 21: Data Mediation for Structured and Unstructured Data               134
Figure 21: Cloud and Big Data Intermediation      135
Figure 22: Data Security, Billing and Settlement  137
Figure 24: Big Data as a Service (BDaaS)  139

 

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