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市場調查報告書

巨量資料市場:產業案例與市場分析·預測

The Big Data Market: Business Case, Market Analysis & Forecasts 2016 - 2021

出版商 Mind Commerce 商品編碼 315018
出版日期 內容資訊 英文 186 Pages
商品交期: 最快1-2個工作天內
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巨量資料市場:產業案例與市場分析·預測 The Big Data Market: Business Case, Market Analysis & Forecasts 2016 - 2021
出版日期: 2016年11月21日 內容資訊: 英文 186 Pages
簡介

本報告提供全球巨量資料市場展望的相關調查,提供您巨量資料技術概要,推動市場成長的要素與阻礙要素,主要產業的用途,巨量資料的價值鏈,巨量資料分析的技術·實行方法,市場收益的變化與預測,功能·各地區的明細,主要經營者簡介等詳細資訊。

第1章 簡介

第2章 摘要整理

第3章 巨量資料的技術和產業案例

  • 巨量資料定義
  • 巨量資料的主要特徵
  • 巨量資料技術
    • Hadoop
    • 其他Apache計劃
    • NoSQL
      • Hbase
      • Cassandra
      • Mongo DB
      • Riak
      • CouchDB
    • MPP資料庫
    • 其他技術·新興技術
      • Storm
      • Drill
      • Dremel
      • SAP HANA
      • Gremlin & Giraph
    • 新的範例·技巧
      • 串流分析
      • 雲端技術
      • Google Search
      • kasutamaizuanaritikaru工具
      • 網際網路關鍵字
      • 遊戲化
  • 巨量資料發展藍圖
  • 市場促進因素
    • 資料的音量和種類
    • 企業·通訊業者擴大引進巨量資料
    • 巨量資料軟體的成熟化
    • 大型網站持續投資巨量資料
    • 產業推進因素
  • 市場障礙
    • 隱私和安全性:一「大」阻障
    • 員工的再教育和組織的耐性
    • 缺乏明確的巨量資料策略
    • 技術與課題:擴充性維護
    • 巨量資料開發的專門性

第4章 進行巨量資料投資的主要產業

  • 產業用網際網路·M2M
    • M2M的巨量資料
    • 各產業市場機會
  • 零售·飯店
    • 改善預測準確度·庫存管理
    • 購買模式的判斷
    • 旅館產業的利用案例
    • 個性化行銷
  • 媒體
    • 社群媒體
    • 社群遊戲分析
    • 透過其他產業部門利用社群遊戲分析
    • 網際網路關鍵字搜尋
  • 公共事業
    • 運用資料分析
    • 未來的應用區域
  • 金融服務
    • 違法行為分析·削減與風險分析
    • 加盟店的獎勵計劃
    • 客戶的區分化
    • 保持客戶·提供個別商品
    • 保險公司
  • 醫療·醫藥品
    • 藥物開發
    • 醫療資料分析
    • 案例研究:心率模式的特定
  • 通訊
    • 通訊業者的分析:客戶/利用分析·服務的最佳化
    • 巨量資料分析工具
    • 語音分析
    • 新產品與服務
  • 政府·國防安全保障
    • 巨量資料研究
    • 統計分析
    • 語言翻譯
    • 公共用應用程式的開發
    • 犯罪的追蹤
    • 資訊收集
    • 詐欺檢測·收益生成
  • 其他的部門
    • 航太
    • 運輸·物流:車隊利用的最佳化
    • 運動:統計的實時處理
    • 教育
    • 製造

第5章 巨量資料的價值鏈

  • 巨量資料價值鏈的片斷化
  • 資料的取得和供應
  • 資料倉儲和商業智慧
  • 分析和虛擬化
  • 行動和業務流程管理(BPM)
  • 資料管治

第6章 巨量資料分析

  • 所謂巨量資料分析
  • 巨量資料分析的重要性
  • 分析:反應式與主動式
  • 技術·實行方法
    • 網格計算
    • 界內資料庫網格計算
    • 數據庫處理
    • In-Memory(記憶體內)分析
    • 資料探勘
    • 預測分析
    • 自然語言處理
    • 文本分析
    • 視覺化分析及工具
    • 相關規則
    • 分類樹分析
    • 機器學習
    • 迴歸分析
    • 社群網路分析

第7章 規格·倡議

  • Cloud Standards Customer Council:巨量資料工作小組
  • NIST(National Institute of Standards and Technology):巨量資料工作小組
  • OASIS
  • Open Data Foundation
  • ODCA(Open Data Center Alliance)
  • CSA(Cloud Security Alliance)
  • ITU
  • ISO

第8章 全球市場與未來預測

  • 全球巨量資料市場預測
  • 各地區的巨量資料市場預測
  • 巨量資料收益的預測:各產品區隔
    • 資料庫管理系統的投資
    • 資料整合工具的投資
    • 應用基礎設施及中介軟體的投資
    • 商業智慧及分析平台的投資
    • 巨量資料專業服務的投資

第9章 巨量資料市場主要企業

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目錄

Overview:

The management of unstructured data (e.g. Big Data), the leveraging of analytics tools to derive value, and the integration between Cloud, Internet of Things (IoT), and enterprise operational technology are key focus areas for large companies across virtually every industry vertical. However, Big Data and Analytics tools are not limited to large companies as products and services are emerging that are democratizing data for smaller companies.

A new data economy is developing in which the data associated with corporate products and services becomes almost as value as the company offerings themselves. New models are emerging to reduce friction across the value chain including enhanced Big Data as a Service (BDaaS) offerings. BDaaS is anticipated to make cross-industry, cross-company, and even cross-competitor data exchange a reality that adds value across the ecosystem with minimized security and privacy concerns.

This report provides an in-depth assessment of the global Big Data market, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry with forecasting from 2016 to 2021.

Topics covered in the report include:

  • Big Data Technology: A review of the underlying technologies that resolve big data complexities
  • Big Data Use Cases: A review of investments sectors and specific use cases for the Big Data market
  • The Big Data Value Chain: An analysis of the value chain of Big Data and the major players involved within it
  • The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
  • Big Data Vendor Assessment: An assessment of the vendor landscape of leading players within the Big Data market
  • Market Analysis and Forecasts: A global and regional assessment of the market size and forecasts for 2016 to 2021

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.

Key Findings:

  • The global Big Data Market will reach $72B USD by 2021 with a CAGR of 20.8%
  • Western Europe will be a market leader with $20.9B USD by 2021 with CAGR of 22.2%
  • Business Intelligence Tools and Analytics Platforms will reach $15.8B USD globally by 2021
  • Professional Services remains the leading revenue area through 2021 with CAGR of 23.8%
  • Open development tools and communities are driving innovation in key areas such as Cloud and IoT

Report Benefits:

  • Detailed forecasts 2016 - 2021
  • Learn about Big Data technologies
  • Identify leading market segments
  • Identify key players and strategies
  • Identify opportunities in data analytics
  • Understand market drivers and barriers
  • Understand the business case for Big Data
  • Understand regulatory issues and initiatives

Companies in Report:

  • 1010Data
  • Accenture
  • Actian Corporation
  • Amazon
  • Apache Software Foundation
  • APTEAN
  • Booz Allen Hamilton
  • Bosch Software innovations: Bosch IoT Suite
  • Capgemini
  • Cisco Systems
  • Cloudera
  • CRAY Inc.
  • Computer Science Corporation
  • DataDirect Network
  • Dell
  • Deloitte
  • EMC
  • Facebook
  • Fujitsu
  • General Electric
  • GoodData Corporation
  • Google
  • Guavus
  • HP
  • Hitachi Data Systems
  • Hortonworks
  • IBM
  • Informatica
  • Intel
  • Jasper (Cisco)
  • Juniper Networks
  • Marklogic
  • Microsoft
  • MongoDB
  • MU Sigma
  • Netapp
  • NTT Data
  • Open Text (Actuate Corporation)
  • Opera Solutions
  • Oracle
  • Pentaho
  • Qlik Tech
  • Quantum
  • Rackspace
  • Revolution Analytics
  • Salesforce
  • SAP
  • SAS Institute
  • Sisense
  • Software AG/Terracotta
  • Splunk
  • Sqrrl
  • Supermicro
  • Tableau Software
  • Tata Consultancy Services
  • Teradata
  • Think Big Analytics
  • TIBCO
  • Tidemark Systems
  • VMware (Part of EMC)
  • Wipro
  • Workday (Platfora)
  • Zettics Target Audience:
  • Network service providers
  • Systems integration companies
  • Big Data and Analytics companies
  • Advertising and media companies
  • Enterprise across all industry verticals
  • Cloud and IoT product and service providers

Table of Contents

1. Background of the Study

  • 1.1. Introduction
  • 1.2. Scope of the Report
  • 1.3. Target Audience
  • 1.4. Companies in Report
<>2. Executive Summary

3. Big Data Technology and Business Case

  • 3.1. Defining Big Data
  • 3.2. Key Characteristics of Big Data
    • 3.2.1. Volume
    • 3.2.2. Variety
    • 3.2.3. Velocity
    • 3.2.4. Variability
    • 3.2.5. Complexity
  • 3.3. Big Data Technology
    • 3.3.1. Hadoop
      • 3.3.1.1. Other Apache Projects
    • 3.3.2. NoSQL
      • 3.3.2.1. Hbase
      • 3.3.2.2. Cassandra
      • 3.3.2.3. Mongo DB
      • 3.3.2.4. Riak
      • 3.3.2.5. CouchDB
    • 3.3.3. MPP Databases
    • 3.3.4. Other Emerging Technologies
      • 3.3.4.1. Storm
      • 3.3.4.2. Drill
      • 3.3.4.3. Dremel
      • 3.3.4.4. SAP HANA
      • 3.3.4.5. Gremlin & Giraph
  • 3.4. New Paradigms and Techniques
    • 3.4.1. Streaming Analytics
    • 3.4.2. Cloud Technology
    • 3.4.3. Google Search
    • 3.4.4. Customize Analytical Tools
    • 3.4.5. Internet Keywords
    • 3.4.6. Gamification
  • 3.5. Big Data Roadmap
  • 3.6. Market Drivers
    • 3.6.1. Data Volume & Variety
    • 3.6.2. Increasing Adoption of Big Data by Enterprises and Telecom
    • 3.6.3. Maturation of Big Data Software
    • 3.6.4. Continued Investments in Big Data by Web Giants
    • 3.6.5. Business Drivers
  • 3.7. Market Barriers
    • 3.7.1. Privacy and Security: The 'Big' Barrier
    • 3.7.2. Workforce Re-skilling and Organizational Resistance
    • 3.7.3. Lack of Clear Big Data Strategies
    • 3.7.4. Technical Challenges: Scalability & Maintenance
    • 3.7.5. Big Data Development Expertise

4. Key Investment Sectors for Big Data

  • 4.1. Industrial Internet and Machine-to-Machine
    • 4.1.1. Big Data in M2M
    • 4.1.2. Vertical Opportunities
  • 4.2. Retail and Hospitality
    • 4.2.1. Improving Accuracy of Forecasts & Stock Management
    • 4.2.2. Determining Buying Patterns
    • 4.2.3. Hospitality Use Cases
    • 4.2.4. Personalized Marketing
  • 4.3. Media
    • 4.3.1. Social Media
    • 4.3.2. Social Gaming Analytics
    • 4.3.3. Usage of Social Media Analytics by Other Verticals
    • 4.3.4. Internet Keyword Search
  • 4.4. Utilities
    • 4.4.1. Analysis of Operational Data
    • 4.4.2. Application Areas for the Future
  • 4.5. Financial Services
    • 4.5.1. Fraud Analysis, Mitigation & Risk Profiling
    • 4.5.2. Merchant-Funded Reward Programs
    • 4.5.3. Customer Segmentation
    • 4.5.4. Customer Retention & Personalized Product Offering
    • 4.5.5. Insurance Companies
  • 4.6. Healthcare and Pharmaceutical
    • 4.6.1. Drug Development
    • 4.6.2. Medical Data Analytics
    • 4.6.3. Case Study: Identifying Heartbeat Patterns
  • 4.7. Telecommunications
    • 4.7.1. Telco Analytics: Customer/Usage Profiling and Service Optimization
    • 4.7.2. Big Data Analytic Tools
    • 4.7.3. Speech Analytics
    • 4.7.4. New Products and Services
  • 4.8. Government and Homeland Security
    • 4.8.1. Big Data Research
    • 4.8.2. Statistical Analysis
    • 4.8.3. Language Translation
    • 4.8.4. Developing New Applications for the Public
    • 4.8.5. Tracking Crime
    • 4.8.6. Intelligence Gathering
    • 4.8.7. Fraud Detection & Revenue Generation
  • 4.9. Other Sectors
    • 4.9.1. Aviation
    • 4.9.2. Transportation & Logistics: Optimizing Fleet Usage
    • 4.9.3. Sports: Real-Time Processing of Statistics
    • 4.9.4. Education
    • 4.9.5. Manufacturing

5. The Big Data Value Chain

  • 5.1. How Fragmented is the Big Data Value Chain?
  • 5.2. Data Acquisitioning & Provisioning
  • 5.3. Data Warehousing & Business Intelligence
  • 5.4. Analytics & Visualization
  • 5.5. Actioning and Business Process Management
  • 5.6. Data Governance

6. Big Data Analytics

  • 6.1. What is Big Data Analytics?
  • 6.2. The Importance of Big Data Analytics
  • 6.3. Reactive vs. Proactive Analytics
  • 6.4. Technology and Implementation Approaches
    • 6.4.1. Grid Computing
    • 6.4.2. In-Database processing
    • 6.4.3. In-Memory Analytics
    • 6.4.4. Data Mining
    • 6.4.5. Predictive Analytics
    • 6.4.6. Natural Language Processing
    • 6.4.7. Text Analytics
    • 6.4.8. Visual Analytics
    • 6.4.9. Association Rule Learning
    • 6.4.10. Classification Tree Analysis
    • 6.4.11. Machine Learning
    • 6.4.12. Neural Networks
    • 6.4.13. Multilayer Perceptron
    • 6.4.14. Radial Basis Functions
      • 6.4.14.1. Support Vector Machines
      • 6.4.14.2. Naíve Bayes
      • 6.4.14.3. K-nearest Neighbors
    • 6.4.15. Geospatial Predictive Modelling
    • 6.4.16. Regression Analysis
    • 6.4.17. Social Network Analysis

7. Standardization and Regulatory Initiatives

  • 7.1. Cloud Standards Customer Council
  • 7.2. National Institute of Standards and Technology
  • 7.3. OASIS
  • 7.4. Open Data Foundation
  • 7.5. Open Data Center Alliance
  • 7.6. Cloud Security Alliance
  • 7.7. International Telecommunications Union
  • 7.8. International Organization for Standardization

8. Global Markets and Forecasts for Big Data

  • 8.1. Global Big Data Markets 2016-2021
  • 8.2. Regional Markets for Big Data 2016-2021
  • 8.3. Big Data Revenue by Product Segment 2016-2021
    • 8.3.1. Investments in Database Management Systems
    • 8.3.2. Investments in Big Data Integration Tools
    • 8.3.3. Investments in Application Infrastructure and Middleware
    • 8.3.4. Investments in Business Intelligence Tools and Analytics Platforms
    • 8.3.5. Big Data Investments in Professional Services 2016-2021

9. Key Players in the Big Data Market

  • 9.1. Vendor Assessment Matrix
  • 9.2. 1010Data
  • 9.3. Accenture
  • 9.4. Actian Corporation
  • 9.5. Amazon
  • 9.6. Apache Software Foundation
  • 9.7. APTEAN
  • 9.8. Booz Allen Hamilton
  • 9.9. Bosch Software Innovations: Bosch IoT Suite
  • 9.10. Capgemini
  • 9.11. Cisco Systems
  • 9.12. Cloudera
  • 9.13. CRAY Inc.
  • 9.14. Computer Science Corporation
  • 9.15. DataDirect Network
  • 9.16. Dell
  • 9.17. Deloitte
  • 9.18. EMC
  • 9.19. Facebook
  • 9.20. Fujitsu
  • 9.21. General Electric
  • 9.22. GoodData Corporation
  • 9.23. Google
  • 9.24. Guavus
  • 9.25. HP
  • 9.26. Hitachi Data Systems
  • 9.27. Hortonworks
  • 9.28. IBM
  • 9.29. Informatica
  • 9.30. Intel
  • 9.31. Jasper (Cisco)
  • 9.32. Juniper Networks
  • 9.33. Marklogic
  • 9.34. Microsoft
  • 9.35. MongoDB
  • 9.36. MU Sigma
  • 9.37. Netapp
  • 9.38. NTT Data
  • 9.39. Open Text (Actuate Corporation)
  • 9.40. Opera Solutions
  • 9.41. Oracle
  • 9.42. Pentaho
  • 9.43. Qlik Tech
  • 9.44. Quantum
  • 9.45. Rackspace
  • 9.46. Revolution Analytics
  • 9.47. Salesforce
  • 9.48. SAP
  • 9.49. SAS Institute
  • 9.50. Sisense
  • 9.51. Software AG/Terracotta
  • 9.52. Splunk
  • 9.53. Sqrrl
  • 9.54. Supermicro
  • 9.55. Tableau Software
  • 9.56. Tata Consultancy Services
  • 9.57. Teradata
  • 9.58. Think Big Analytics
  • 9.59. TIBCO
  • 9.60. Tidemark Systems
  • 9.61. VMware (EMC)
  • 9.62. Wipro
  • 9.63. Workday (Platfora)
  • 9.64. Zettics

Figures

  • Figure 1: Key Characteristics of Big Data
  • Figure 2: NoSQL vs Legacy DB Performance Comparisons
  • Figure 3: Roadmap Big Data Technologies 2016 - 2030
  • Figure 4: The Big Data Value Chain
  • Figure 5: Big Data Value Flow
  • Figure 6: Big Data Analytics
  • Figure 7: Global Big Data Markets 2016 - 2021
  • Figure 8: Regional Big Data Markets 2016 - 2021
  • Figure 9: Investments in Database Management Systems 2016 - 2021
  • Figure 10: Investments in Data Integration and Quality Tools 2016 - 2021
  • Figure 11: Investments in Application Infrastructure and Middleware 2016 - 2021
  • Figure 12: Investments in Business Intelligence Tools and Analytics Platforms 2016 - 2021
  • Figure 13: Big Data Investments in Professional Services 2016 - 2021
  • Figure 14: Big Data Vendor Ranking Matrix

Tables

  • Table 1: Global Big Data Markets 2016 - 2021
  • Table 2: Regional Big Data Markets 2016 - 2021
  • Table 3: Big Data Markets by Product Segments 2016 - 2021
  • Table 4: Investments in Database Management Systems 2016 - 2021
  • Table 5: Investments in Data Integration Tools 2016 - 2021
  • Table 6: Investments in Application Infrastructure and Middleware 2016 - 2021
  • Table 7: Investments in Business Intelligence Tools and Analytics Platforms 2016 - 2021
  • Table 8: Big Data Investments in Professional Services 2016 - 2021
  • Table 9: Big Data Analytics Platforms by Company
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