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市場調查報告書

巨量資料市場(2015-2030年):市場機會·課題·策略·最終用途產業·預測

The Big Data Market: 2015 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

出版商 Signals and Systems Telecom 商品編碼 305292
出版日期 內容資訊 英文 351 Pages
商品交期: 最快1-2個工作天內
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巨量資料市場(2015-2030年):市場機會·課題·策略·最終用途產業·預測 The Big Data Market: 2015 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts
出版日期: 2015年05月25日 內容資訊: 英文 351 Pages
簡介

儘管有隱私上的疑慮和企業的阻力等課題,巨量資料的投資仍然全球性增加。2015年巨量資料的投資額推測約達400億美金,預測今後5年投資額成長率將以CAGR14%的速度成長。

本報告提供全球巨量資料市場相關調查,提供您巨量資料概要,市場促進因素與課題,各產業的市場機會和使用案例,產業藍圖和價值鏈,詳細區分·終端用戶產業·地區/主要國家別市場規模的變化與預測,並彙整主要供應商簡介,策略性建議等資料。

第1章 簡介

第2章 巨量資料概要

  • 所謂巨量資料
  • 巨量資料處理主要的方法
    • Hadoop
    • NoSQL
    • MPAD (大規模並行分析數據庫)
    • In-Memory(記憶體內)處理
    • 串流處理技術
    • Spark
    • 其他
  • 巨量資料的主要特徵
    • 數量
    • 速度
    • 種類
    • 價值
  • 市場促進因素
    • 對優點的認識
    • 巨量資料平台的成熟
    • 網站大企業·政府·企業的持續投資
    • 資料數量·速度·種類的增加
    • 供應商的進入和合作
    • 打入門檻低的技術趨勢
  • 市場障礙
    • 缺乏分析專家
    • 不明確的巨量資料策略
    • 組織對巨量資料引進的反對
    • 技術課題:擴充性與維護
    • 安全和隱私

第3章 巨量資料在各產業市場機會和使用案例

  • 汽車·航太·運輸
    • 預測性故障·缺點偵測
    • 預測性飛機維護·燃料最佳化
    • 航空交通管制
    • 車隊(編隊)的最佳化
  • 銀行·證券
    • 客戶保持·客製化商品的提供
    • 風險管理
    • 詐騙偵測
    • 信用審查
  • 國防·諜報
    • 資訊收集
    • 戰場上節能的機會
    • 戰場上的負傷迴避
  • 教育
    • 資訊的整合
    • 鎖定學習模式
    • 實現學生意願性學習
  • 醫療·醫藥
    • 有效率的管理公共衛生
    • 透過醫療資料分析改善病人的照顧
    • 改善臨床開發與臨床試驗
    • TTM的高速化
  • 智慧城市&智慧建築
    • 能源的最佳化和故障檢測
    • 智慧建築分析
    • 城市交通管理
    • 能源生產的最佳化
    • 水資源管理
    • 城市廢棄物管理
  • 保險
    • 降低詐欺申請
    • 客戶的保持·分析
    • 風險管理
  • 製造·天然資源
    • 設備資產的維護·削減停機時間
    • 管理品質及對環境的影響
    • 供應鏈的最佳化
    • 礦泉·礦床的探索與鎖定
    • 開採潛在力的最大化
    • 生產的最佳化
  • 網站·媒體·娛樂
    • 觀眾和宣傳的最佳化
    • 銷路的最佳化
    • 推薦
    • 最佳化搜尋
    • 實況錄音運動活動分析
    • 巨量資料其他產業的外包
  • 公共安全·國防安全保障
    • 降低電腦網路犯罪
    • 犯罪預測分析
    • 影像分析與情形的認識
  • 公共服務
    • 公眾意見分析
    • 詐欺偵測與預防
    • 經濟分析
  • 零售·飯店
    • 客戶感情分析
    • 客戶分類與分析
    • 價格的最佳化
    • 個體化行銷
    • 供應鏈的最佳化
  • 通訊
    • 網路性能和覆蓋範圍的最佳化
    • 避免客戶流失
    • 個體化行銷
    • 定位服務
    • 詐欺偵測
  • 公共產業·能源
    • 保持客戶
    • 能源預測
    • 申請分析
    • 預測性維護
    • 渦輪機安裝的最佳化
  • 批發交易
    • 現場銷售額分析
    • 供應鏈的監測

第4章 巨量資料產業發展藍圖和價值鏈

  • 巨量資料產業發展藍圖
  • 巨量資料的價值鏈
    • 硬體設備供應商
    • 軟體供應商
    • 專門服務供應商
    • 端到端解決方案供應商
    • 各產業的企業

第5章 巨量資料分析

  • 巨量資料分析與
  • 分析的重要性
  • 反應的分析 vs 積極的分析
  • 客戶分析 vs 營運分析
  • 技術和實行的方法
    • 柵格計算
    • 界內資料庫處理
    • In-Memory(記憶體內)分析
    • 機器學習資料與探勘
    • 預測的分析
    • NLP (自然地語言處理)
    • 文本分析
    • visual分析
    • 社群媒體·IT·通訊網路分析
  • 各產業市場案例研究
    • Amazon
    • Facebook
    • WIND Mobile
    • Boeing
    • The Walt Disney Company

第6章 標準化·法律規章主張

  • CSCC (Cloud Standards Customer Council)
  • NIST (美國國立標準技術研究所)
  • OASIS
  • ODaF (Open Data Foundation)
  • Open Data Center Alliance
  • CSA (Cloud Security Alliance)
  • ITU (國際電信聯盟)
  • ISO (國際標準化組織)

第7章 市場分析·預測

  • 巨量資料市場全球性展望
  • 各部門市場
    • 儲存&演算基礎設施
    • 網路基礎設施
    • Hadoop&基礎設施軟體
    • SQL
    • NoSQL
    • 分析平台&應用
    • 雲端平台
    • 專門服務
  • 各產業市場
    • 汽車·航太·運輸
    • 銀行·證券
    • 國防·諜報
    • 教育
    • 醫療·醫藥
    • 智慧城市·智慧建築
    • 製造·天然資源
    • 媒體·娛樂
    • 公共安全·國防安全保障
    • 公共服務
    • 零售·飯店
    • 公共產業·能源
    • 批發交易
    • 其他
  • 地區市場
    • 亞太地區
    • 東歐
    • 中南美
    • 中東·非洲
    • 北美
    • 西歐

第8章 供應商環境

第9章 總論·策略性建議

圖表

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目錄

"Big Data" originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.

Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for nearly $40 Billion in 2015 alone. These investments are further expected to grow at a CAGR of 14% over the next 5 years.

The "Big Data Market: 2015 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts" report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2015 through to 2030. Historical figures are also presented for 2010, 2011, 2012, 2013 and 2014. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

Table of Contents

1 Chapter 1: Introduction

  • 1.1 Executive Summary
  • 1.2 Topics Covered
  • 1.3 Historical Revenue & Forecast Segmentation
  • 1.4 Key Questions Answered
  • 1.5 Key Findings
  • 1.6 Methodology
  • 1.7 Target Audience
  • 1.8 Companies & Organizations Mentioned

2 Chapter 2: An Overview of Big Data

  • 2.1 What is Big Data?
  • 2.2 Key Approaches to Big Data Processing
    • 2.2.1 Hadoop
    • 2.2.2 NoSQL
    • 2.2.3 MPAD (Massively Parallel Analytic Databases)
    • 2.2.4 In-memory Processing
    • 2.2.5 Stream Processing Technologies
    • 2.2.6 Spark
    • 2.2.7 Other Databases & Analytic Technologies
  • 2.3 Key Characteristics of Big Data
    • 2.3.1 Volume
    • 2.3.2 Velocity
    • 2.3.3 Variety
    • 2.3.4 Value
  • 2.4 Market Growth Drivers
    • 2.4.1 Awareness of Benefits
    • 2.4.2 Maturation of Big Data Platforms
    • 2.4.3 Continued Investments by Web Giants, Governments & Enterprises
    • 2.4.4 Growth of Data Volume, Velocity & Variety
    • 2.4.5 Vendor Commitments & Partnerships
    • 2.4.6 Technology Trends Lowering Entry Barriers
  • 2.5 Market Barriers
    • 2.5.1 Lack of Analytic Specialists
    • 2.5.2 Uncertain Big Data Strategies
    • 2.5.3 Organizational Resistance to Big Data Adoption
    • 2.5.4 Technical Challenges: Scalability & Maintenance
    • 2.5.5 Security & Privacy Concerns

3 Chapter 3: Vertical Opportunities & Use Cases for Big Data

  • 3.1 Automotive, Aerospace & Transportation
    • 3.1.1 Predictive Warranty Analysis
    • 3.1.2 Predictive Aircraft Maintenance & Fuel Optimization
    • 3.1.3 Air Traffic Control
    • 3.1.4 Transport Fleet Optimization
  • 3.2 Banking & Securities
    • 3.2.1 Customer Retention & Personalized Product Offering
    • 3.2.2 Risk Management
    • 3.2.3 Fraud Detection
    • 3.2.4 Credit Scoring
  • 3.3 Defense & Intelligence
    • 3.3.1 Intelligence Gathering
    • 3.3.2 Energy Saving Opportunities in the Battlefield
    • 3.3.3 Preventing Injuries on the Battlefield
  • 3.4 Education
    • 3.4.1 Information Integration
    • 3.4.2 Identifying Learning Patterns
    • 3.4.3 Enabling Student-Directed Learning
  • 3.5 Healthcare & Pharmaceutical
    • 3.5.1 Managing Population Health Efficiently
    • 3.5.2 Improving Patient Care with Medical Data Analytics
    • 3.5.3 Improving Clinical Development & Trials
    • 3.5.4 Improving Time to Market
  • 3.6 Smart Cities & Intelligent Buildings
    • 3.6.1 Energy Optimization & Fault Detection
    • 3.6.2 Intelligent Building Analytics
    • 3.6.3 Urban Transportation Management
    • 3.6.4 Optimizing Energy Production
    • 3.6.5 Water Management
    • 3.6.6 Urban Waste Management
  • 3.7 Insurance
    • 3.7.1 Claims Fraud Mitigation
    • 3.7.2 Customer Retention & Profiling
    • 3.7.3 Risk Management
  • 3.8 Manufacturing & Natural Resources
    • 3.8.1 Asset Maintenance & Downtime Reduction
    • 3.8.2 Quality & Environmental Impact Control
    • 3.8.3 Optimized Supply Chain
    • 3.8.4 Exploration & Identification of Wells & Mines
    • 3.8.5 Maximizing the Potential of Drilling
    • 3.8.6 Production Optimization
  • 3.9 Web, Media & Entertainment
    • 3.9.1 Audience & Advertising Optimization
    • 3.9.2 Channel Optimization
    • 3.9.3 Recommendation Engines
    • 3.9.4 Optimized Search
    • 3.9.5 Live Sports Event Analytics
    • 3.9.6 Outsourcing Big Data Analytics to Other Verticals
  • 3.10 Public Safety & Homeland Security
    • 3.10.1 Cyber Crime Mitigation
    • 3.10.2 Crime Prediction Analytics
    • 3.10.3 Video Analytics & Situational Awareness
  • 3.11 Public Services
    • 3.11.1 Public Sentiment Analysis
    • 3.11.2 Fraud Detection & Prevention
    • 3.11.3 Economic Analysis
  • 3.12 Retail & Hospitality
    • 3.12.1 Customer Sentiment Analysis
    • 3.12.2 Customer & Branch Segmentation
    • 3.12.3 Price Optimization
    • 3.12.4 Personalized Marketing
    • 3.12.5 Optimized Supply Chain
  • 3.13 Telecommunications
    • 3.13.1 Network Performance & Coverage Optimization
    • 3.13.2 Customer Churn Prevention
    • 3.13.3 Personalized Marketing
    • 3.13.4 Location Based Services
    • 3.13.5 Fraud Detection
  • 3.14 Utilities & Energy
    • 3.14.1 Customer Retention
    • 3.14.2 Forecasting Energy
    • 3.14.3 Billing Analytics
    • 3.14.4 Predictive Maintenance
    • 3.14.5 Turbine Placement Optimization
  • 3.15 Wholesale Trade
    • 3.15.1 In-field Sales Analytics
    • 3.15.2 Monitoring the Supply Chain

4 Chapter 4: Big Data Industry Roadmap & Value Chain

  • 4.1 Big Data Industry Roadmap
    • 4.1.1 2010 - 2013: Initial Hype and the Rise of Analytics
    • 4.1.2 2014 - 2017: Emergence of SaaS Based Big Data Solutions
    • 4.1.3 2018 - 2020: Growing Adoption of Scalable Machine Learning
    • 4.1.4 2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics
  • 4.2 The Big Data Value Chain
    • 4.2.1 Hardware Providers
      • 4.2.1.1 Storage & Compute Infrastructure Providers
      • 4.2.1.2 Networking Infrastructure Providers
    • 4.2.2 Software Providers
      • 4.2.2.1 Hadoop & Infrastructure Software Providers
      • 4.2.2.2 SQL & NoSQL Providers
      • 4.2.2.3 Analytic Platform & Application Software Providers
      • 4.2.2.4 Cloud Platform Providers
    • 4.2.3 Professional Services Providers
    • 4.2.4 End-to-End Solution Providers
    • 4.2.5 Vertical Enterprises

5 Chapter 5: Big Data Analytics

  • 5.1 What are Big Data Analytics?
  • 5.2 The Importance of Analytics
  • 5.3 Reactive vs. Proactive Analytics
  • 5.4 Customer vs. Operational Analytics
  • 5.5 Technology & Implementation Approaches
    • 5.5.1 Grid Computing
    • 5.5.2 In-Database Processing
    • 5.5.3 In-Memory Analytics
    • 5.5.4 Machine Learning & Data Mining
    • 5.5.5 Predictive Analytics
    • 5.5.6 NLP (Natural Language Processing)
    • 5.5.7 Text Analytics
    • 5.5.8 Visual Analytics
    • 5.5.9 Social Media, IT & Telco Network Analytics
  • 5.6 Vertical Market Case Studies
    • 5.6.1 Amazon - Delivering Cloud Based Big Data Analytics
    • 5.6.2 Facebook - Using Analytics to Monetize Users with Advertising
    • 5.6.3 WIND Mobile - Using Analytics to Monitor Video Quality
    • 5.6.4 Coriant Analytics Services - SaaS Based Big Data Analytics for Telcos
    • 5.6.5 Boeing - Analytics for the Battlefield
    • 5.6.6 The Walt Disney Company - Utilizing Big Data and Analytics in Theme Parks

6 Chapter 6: Standardization & Regulatory Initiatives

  • 6.1 CSCC (Cloud Standards Customer Council) - Big Data Working Group
  • 6.2 NIST (National Institute of Standards and Technology) - Big Data Working Group
  • 6.3 OASIS -Technical Committees
  • 6.4 ODaF (Open Data Foundation)
  • 6.5 Open Data Center Alliance
  • 6.6 CSA (Cloud Security Alliance) - Big Data Working Group
  • 6.7 ITU (International Telecommunications Union)
  • 6.8 ISO (International Organization for Standardization) and Others

7 Chapter 7: Market Analysis & Forecasts

  • 7.1 Global Outlook of the Big Data Market
  • 7.2 Submarket Segmentation
    • 7.2.1 Storage and Compute Infrastructure
    • 7.2.2 Networking Infrastructure
    • 7.2.3 Hadoop & Infrastructure Software
    • 7.2.4 SQL
    • 7.2.5 NoSQL
    • 7.2.6 Analytic Platforms & Applications
    • 7.2.7 Cloud Platforms
    • 7.2.8 Professional Services
  • 7.3 Vertical Market Segmentation
    • 7.3.1 Automotive, Aerospace & Transportation
    • 7.3.2 Banking & Securities
    • 7.3.3 Defense & Intelligence
    • 7.3.4 Education
    • 7.3.5 Healthcare & Pharmaceutical
    • 7.3.6 Smart Cities & Intelligent Buildings
    • 7.3.7 Insurance
    • 7.3.8 Manufacturing & Natural Resources
    • 7.3.9 Media & Entertainment
    • 7.3.10 Public Safety & Homeland Security
    • 7.3.11 Public Services
    • 7.3.12 Retail & Hospitality
    • 7.3.13 Telecommunications
    • 7.3.14 Utilities & Energy
    • 7.3.15 Wholesale Trade
    • 7.3.16 Other Sectors
  • 7.4 Regional Outlook
  • 7.5 Asia Pacific
    • 7.5.1 Country Level Segmentation
    • 7.5.2 Australia
    • 7.5.3 China
    • 7.5.4 India
    • 7.5.5 Indonesia
    • 7.5.6 Japan
    • 7.5.7 Malaysia
    • 7.5.8 Pakistan
    • 7.5.9 Philippines
    • 7.5.10 Singapore
    • 7.5.11 South Korea
    • 7.5.12 Taiwan
    • 7.5.13 Thailand
    • 7.5.14 Rest of Asia Pacific
  • 7.6 Eastern Europe
    • 7.6.1 Country Level Segmentation
    • 7.6.2 Czech Republic
    • 7.6.3 Poland
    • 7.6.4 Russia
    • 7.6.5 Rest of Eastern Europe
  • 7.7 Latin & Central America
    • 7.7.1 Country Level Segmentation
    • 7.7.2 Argentina
    • 7.7.3 Brazil
    • 7.7.4 Mexico
    • 7.7.5 Rest of Latin & Central America
  • 7.8 Middle East & Africa
    • 7.8.1 Country Level Segmentation
    • 7.8.2 Israel
    • 7.8.3 Qatar
    • 7.8.4 Saudi Arabia
    • 7.8.5 South Africa
    • 7.8.6 UAE
    • 7.8.7 Rest of the Middle East & Africa
  • 7.9 North America
    • 7.9.1 Country Level Segmentation
    • 7.9.2 Canada
    • 7.9.3 USA
  • 7.10 Western Europe
    • 7.10.1 Country Level Segmentation
    • 7.10.2 Denmark
    • 7.10.3 Finland
    • 7.10.4 France
    • 7.10.5 Germany
    • 7.10.6 Italy
    • 7.10.7 Netherlands
    • 7.10.8 Norway
    • 7.10.9 Spain
    • 7.10.10 Sweden
    • 7.10.11 UK
    • 7.10.12 Rest of Western Europe

8 Chapter 8: Vendor Landscape

  • 8.1 1010data
  • 8.2 Accenture
  • 8.3 Actian Corporation
  • 8.4 Actuate Corporation
  • 8.5 Adaptive Insights
  • 8.6 Advizor Solutions
  • 8.7 AeroSpike
  • 8.8 AFS Technologies
  • 8.9 Alpine Data Labs
  • 8.10 Alteryx
  • 8.11 Altiscale
  • 8.12 Antivia
  • 8.13 Arcplan
  • 8.14 Attivio
  • 8.15 Automated Insights
  • 8.16 AWS (Amazon Web Services)
  • 8.17 Ayasdi
  • 8.18 Basho
  • 8.19 BeyondCore
  • 8.20 Birst
  • 8.21 Bitam
  • 8.22 Board International
  • 8.23 Booz Allen Hamilton
  • 8.24 Capgemini
  • 8.25 Cellwize
  • 8.26 Centrifuge Systems
  • 8.27 CenturyLink
  • 8.28 Chartio
  • 8.29 Cisco Systems
  • 8.30 ClearStory Data
  • 8.31 Cloudera
  • 8.32 Comptel
  • 8.33 Concurrent
  • 8.34 Contexti
  • 8.35 Couchbase
  • 8.36 CSC (Computer Science Corporation)
  • 8.37 DataHero
  • 8.38 Datameer
  • 8.39 DataRPM
  • 8.40 DataStax
  • 8.41 Datawatch Corporation
  • 8.42 DDN (DataDirect Network)
  • 8.43 Decisyon
  • 8.44 Dell
  • 8.45 Deloitte
  • 8.46 Denodo Technologies
  • 8.47 Digital Reasoning
  • 8.48 Dimensional Insight
  • 8.49 Domo
  • 8.50 Dundas Data Visualization
  • 8.51 Eligotech
  • 8.52 EMC Corporation
  • 8.53 Engineering Group (Engineering Ingegneria Informatica)
  • 8.54 eQ Technologic
  • 8.55 Facebook
  • 8.56 FICO
  • 8.57 Fractal Analytics
  • 8.58 Fujitsu
  • 8.59 Fusion-io
  • 8.60 GE (General Electric)
  • 8.61 GoodData Corporation
  • 8.62 Google
  • 8.63 Guavus
  • 8.64 HDS (Hitachi Data Systems)
  • 8.65 Hortonworks
  • 8.66 HP
  • 8.67 IBM
  • 8.68 iDashboards
  • 8.69 Incorta
  • 8.70 InetSoft Technology Corporation
  • 8.71 InfiniDB
  • 8.72 Infor
  • 8.73 Informatica Corporation
  • 8.74 Information Builders
  • 8.75 Intel
  • 8.76 Jedox
  • 8.77 Jinfonet Software
  • 8.78 Juniper Networks
  • 8.79 Knime
  • 8.80 Kofax
  • 8.81 Kognitio
  • 8.82 L-3 Communications
  • 8.83 Lavastorm Analytics
  • 8.84 Logi Analytics
  • 8.85 Looker Data Sciences
  • 8.86 LucidWorks
  • 8.87 Manthan Software Services
  • 8.88 MapR
  • 8.89 MarkLogic
  • 8.90 MemSQL
  • 8.91 Microsoft
  • 8.92 MicroStrategy
  • 8.93 MongoDB (formerly 10gen)
  • 8.94 Mu Sigma
  • 8.95 NTT Data
  • 8.96 Neo Technology
  • 8.97 NetApp
  • 8.98 OpenText Corporation
  • 8.99 Opera Solutions
  • 8.100 Oracle
  • 8.101 Palantir Technologies
  • 8.102 Panorama Software
  • 8.103 ParStream
  • 8.104 Pentaho
  • 8.105 Phocas
  • 8.106 Pivotal Software
  • 8.107 Platfora
  • 8.108 Prognoz
  • 8.109 PwC
  • 8.110 Pyramid Analytics
  • 8.111 Qlik
  • 8.112 Quantum Corporation
  • 8.113 Qubole
  • 8.114 Rackspace
  • 8.115 RainStor
  • 8.116 RapidMiner
  • 8.117 Recorded Future
  • 8.118 Revolution Analytics
  • 8.119 RJMetrics
  • 8.120 Salesforce.com
  • 8.121 Sailthru
  • 8.122 Salient Management Company
  • 8.123 SAP
  • 8.124 SAS Institute
  • 8.125 SGI
  • 8.126 SiSense
  • 8.127 Software AG
  • 8.128 Splice Machine
  • 8.129 Splunk
  • 8.130 Sqrrl
  • 8.131 Strategy Companion
  • 8.132 Supermicro
  • 8.133 SynerScope
  • 8.134 Tableau Software
  • 8.135 Talend
  • 8.136 Targit
  • 8.137 TCS (Tata Consultancy Services)
  • 8.138 Teradata
  • 8.139 Think Big Analytics
  • 8.140 ThoughtSpot
  • 8.141 TIBCO Software
  • 8.142 Tidemark
  • 8.143 VMware (EMC Subsidiary)
  • 8.144 WiPro
  • 8.145 Yellowfin International
  • 8.146 Zettics
  • 8.147 Zoomdata
  • 8.148 Zucchetti

9 Chapter 9: Conclusion & Strategic Recommendations

  • 9.1 Big Data Technology: Beyond Data Capture & Analytics
  • 9.2 Transforming IT from a Cost Center to a Profit Center
  • 9.3 Can Privacy Implications Hinder Success?
  • 9.4 Will Regulation have a Negative Impact on Big Data Investments?
  • 9.5 Battling Organization & Data Silos
  • 9.6 Software vs. Hardware Investments
  • 9.7 Vendor Share: Who Leads the Market?
  • 9.8 Big Data Driving Wider IT Industry Investments
  • 9.9 Assessing the Impact of IoT & M2M
  • 9.10 Recommendations
    • 9.10.1 Big Data Hardware, Software & Professional Services Providers
    • 9.10.2 Enterprises

List of Figures

  • Figure 1: Big Data Industry Roadmap
  • Figure 2: The Big Data Value Chain
  • Figure 3: Reactive vs. Proactive Analytics
  • Figure 4: Global Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 5: Global Big Data Revenue by Submarket: 2015 - 2030 ($ Million)
  • Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 9: Global Big Data SQL Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 10: Global Big Data NoSQL Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 13: Global Big Data Professional Services Submarket Revenue: 2015 - 2030 ($ Million)
  • Figure 14: Global Big Data Revenue by Vertical Market: 2015 - 2030 ($ Million)
  • Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2015 - 2030 ($ Million)
  • Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2015 - 2030 ($ Million)
  • Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2015 - 2030 ($ Million)
  • Figure 18: Global Big Data Revenue in the Education Sector: 2015 - 2030 ($ Million)
  • Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2015 - 2030 ($ Million)
  • Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2015 - 2030 ($ Million)
  • Figure 21: Global Big Data Revenue in the Insurance Sector: 2015 - 2030 ($ Million)
  • Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2015 - 2030 ($ Million)
  • Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2015 - 2030 ($ Million)
  • Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2015 - 2030 ($ Million)
  • Figure 25: Global Big Data Revenue in the Public Services Sector: 2015 - 2030 ($ Million)
  • Figure 26: Global Big Data Revenue in the Retail & Hospitality Sector: 2015 - 2030 ($ Million)
  • Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2015 - 2030 ($ Million)
  • Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2015 - 2030 ($ Million)
  • Figure 29: Global Big Data Revenue in the Wholesale Trade Sector: 2015 - 2030 ($ Million)
  • Figure 30: Global Big Data Revenue in Other Vertical Sectors: 2015 - 2030 ($ Million)
  • Figure 31: Big Data Revenue by Region: 2015 - 2030 ($ Million)
  • Figure 32: Asia Pacific Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 33: Asia Pacific Big Data Revenue by Country: 2015 - 2030 ($ Million)
  • Figure 34: Australia Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 35: China Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 36: India Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 37: Indonesia Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 38: Japan Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 39: Malaysia Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 40: Pakistan Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 41: Philippines Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 42: Singapore Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 43: South Korea Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 44: Taiwan Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 45: Thailand Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 46: Big Data Revenue in the Rest of Asia Pacific: 2015 - 2030 ($ Million)
  • Figure 47: Eastern Europe Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 48: Eastern Europe Big Data Revenue by Country: 2015 - 2030 ($ Million)
  • Figure 49: Czech Republic Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 50: Poland Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 51: Russia Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 52: Big Data Revenue in the Rest of Eastern Europe: 2015 - 2030 ($ Million)
  • Figure 53: Latin & Central America Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 54: Latin & Central America Big Data Revenue by Country: 2015 - 2030 ($ Million)
  • Figure 55: Argentina Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 56: Brazil Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 57: Mexico Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 58: Big Data Revenue in the Rest of Latin & Central America: 2015 - 2030 ($ Million)
  • Figure 59: Middle East & Africa Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 60: Middle East & Africa Big Data Revenue by Country: 2015 - 2030 ($ Million)
  • Figure 61: Israel Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 62: Qatar Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 63: Saudi Arabia Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 64: South Africa Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 65: UAE Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 66: Big Data Revenue in the Rest of the Middle East & Africa: 2015 - 2030 ($ Million)
  • Figure 67: North America Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 68: North America Big Data Revenue by Country: 2015 - 2030 ($ Million)
  • Figure 69: Canada Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 70: USA Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 71: Western Europe Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 72: Western Europe Big Data Revenue by Country: 2015 - 2030 ($ Million)
  • Figure 73: Denmark Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 74: Finland Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 75: France Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 76: Germany Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 77: Italy Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 78: Netherlands Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 79: Norway Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 80: Spain Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 81: Sweden Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 82: UK Big Data Revenue: 2015 - 2030 ($ Million)
  • Figure 83: Big Data Revenue in the Rest of Western Europe: 2015 - 2030 ($ Million)
  • Figure 84: Global Big Data Revenue by Hardware, Software & Professional Services ($ Million): 2015 - 2030
  • Figure 85: Big Data Vendor Market Share (%)
  • Figure 86: Global IT Expenditure Driven by Big Data Investments: 2015 - 2030 ($ Million)
  • Figure 87: Global M2M Connections by Access Technology (Millions): 2015 - 2030
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