表紙
市場調査レポート

ビッグデータ市場(2015-2030年):市場機会・課題・戦略・エンドユーズ産業・予測

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

発行 Signals and Systems Telecom 商品コード 305292
出版日 ページ情報 英文 351 Pages
納期: 即日から翌営業日
価格
本日の銀行送金レート: 1USD=102.06円で換算しております。
Back to Top
ビッグデータ市場(2015-2030年):市場機会・課題・戦略・エンドユーズ産業・予測 The Big Data Market: 2015 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts
出版日: 2015年05月25日 ページ情報: 英文 351 Pages
概要

プライバシー上の懸念や企業による抵抗などの課題にも関わらず、ビッグデータへの投資が世界的に勢いを増しています。2015年におけるビッグデータへの投資額は約400億ドルとなると推計されており、今後5年にわたる投資額の成長率はCAGRで14%となると予測されています。

当レポートでは、世界のビッグデータ市場について調査し、ビッグデータの概要、市場成長推進因子と課題、産業別の市場機会と利用事例、産業ロードマップとバリューチェーン、詳細区分・エンドユーザー産業・地域/主要国別の市場規模の推移と予測、主要ベンダーのプロファイル、戦略的提言などをまとめています。

第1章 イントロダクション

第2章 ビッグデータの概要

  • ビッグデータとは
  • ビッグデータ処理の主なアプローチ
    • Hadoop
    • NoSQL
    • MPAD (超並列分析データベース)
    • インメモリー処理
    • ストリーム処理技術
    • Spark
    • その他
  • ビッグデータの主な特徴
    • 速度
    • 種類
    • 価値
  • 市場成長推進因子
    • メリットの認識
    • ビッグデータプラットフォームの成熟
    • ウェブ大手・政府・企業による継続的投資
    • データ量・速度・種類の増大
    • ベンダーの参入と提携
    • 参入の障壁を低くする技術動向
  • 市場の障壁
    • 分析専門家の不足
    • 不明確なビッグデータ戦略
    • ビッグデータ導入に対する組織の反対
    • 技術的課題:拡張性と保守
    • セキュリティとプライバシー

第3章 ビッグデータの産業別市場機会と利用事例

  • 自動車・航空宇宙・輸送
    • 予測的故障・不具合検知
    • 予測的航空機保守・燃料最適化
    • 航空交通管制
    • フリートの最適化
  • 銀行・証券
    • 顧客保持・カスタマイズ商品の提供
    • リスク管理
    • 詐欺検知
    • 信用審査
  • 防衛・諜報
    • 情報収集
    • 戦場における省エネの機会
    • 戦場における負傷の回避
  • 教育
    • 情報の統合
    • 学習パターンの特定
    • 生徒志向の学習の実現
  • 医療・医薬
    • 効率的な公衆衛生の管理
    • 医療データ分析による患者ケアの向上
    • 臨床開発と臨床試験の改善
    • TTMの高速化
  • スマートシティ&インテリジェントビル
    • エネルギーの最適化と故障検出
    • インテリジェントビル分析
    • 都市交通管理
    • エネルギー生産の最適化
    • 水管理
    • 都市廃棄物管理
  • 保険
    • 不正請求の低減
    • 顧客の保持・プロファイリング
    • リスク管理
  • 製造・天然資源
    • 設備資産の保守・ダウンタイムの削減
    • 品質および環境への影響の管理
    • サプライチェーンの最適化
    • 鉱泉・鉱床の探索と特定
    • 採掘の潜在力の最大化
    • 生産の最適化
  • ウェブ・メディア・エンターテインメント
    • オーディエンスと宣伝の最適化
    • 販路の最適化
    • 推薦エンジン
    • 最適化検索
    • ライブスポーツイベントの分析
    • ビッグデータ分析の他産業へのアウトソーシング
  • 公衆安全・国土安全保障
    • サイバー犯罪の低減
    • 犯罪予測分析
    • 映像分析と状況的認知
  • 公共サービス
    • 世論分析
    • 不正検知と予防
    • 経済分析
  • 小売・ホスピタリティ
    • 顧客感情の分析
    • 顧客と分岐分析
    • 価格の最適化
    • 個別化マーケティング
    • サプライチェーンの最適化
  • 通信
    • ネットワークパフォーマンスとカバレージの最適化
    • 顧客による解約の回避
    • 個別化マーケティング
    • 位置情報サービス
    • 不正検知
  • 公益事業・エネルギー
    • 顧客の保持
    • エネルギー予測
    • 請求分析
    • 予測的保守
    • タービン設置の最適化
  • 卸売取引
    • 現場売上分析
    • サプライチェーンのモニタリング

第4章 ビッグデータ産業のロードマップとバリューチェーン

  • ビッグデータ業界のロードマップ
  • ビッグデータのバリューチェーン
    • ハードウェアプロバイダー
    • ソフトウェアプロバイダー
    • 専門サービスプロバイダー
    • エンドツーエンドソリューションプロバイダー
    • 各産業の企業

第5章 ビッグデータ分析

  • ビッグデータ分析とは
  • 分析の重要性
  • 反応的分析 vs 積極的分析
  • 顧客分析 vs 運営分析
  • 技術と実装のアプローチ
    • グリッドコンピューティング
    • インデータベース処理
    • インメモリー分析
    • 機械学習とデータマイニング
    • 予測的分析
    • NLP (自然言語処理)
    • テキスト分析
    • ビジュアル分析
    • ソーシャルメディア・IT・通信ネットワーク分析
  • 産業別市場のケーススタディ
    • Amazon
    • Facebook
    • WIND Mobile
    • Boeing
    • The Walt Disney Company

第6章 標準化・法規制イニシアティブ

  • CSCC (Cloud Standards Customer Council)
  • NIST (米国国立標準技術研究所)
  • OASIS
  • ODaF (Open Data Foundation)
  • Open Data Center Alliance
  • CSA (Cloud Security Alliance)
  • ITU (国際電気通信連合)
  • ISO (国際標準化機構)

第7章 市場分析・予測

  • ビッグデータ市場の世界的展望
  • 部門別市場
    • ストレージ&演算インフラ
    • ネットワーキングインフラ
    • Hadoop&インフラソフトウェア
    • SQL
    • NoSQL
    • 分析プラットフォーム&アプリケーション
    • クラウドプラットフォーム
    • 専門サービス
  • 産業別市場
    • 自動車・航空宇宙・輸送
    • 銀行・証券
    • 防衛・諜報
    • 教育
    • 医療・医薬
    • スマートシティ・インテリジェントビル
    • 製造・天然資源
    • メディア・エンターテインメント
    • 公衆安全・国土安全保障
    • 公共サービス
    • 小売・ホスピタリティ
    • 公益事業・エネルギー
    • 卸売取引
    • その他
  • 地域市場
    • アジア太平洋
    • 東欧
    • 中南米
    • 中東・アフリカ
    • 北米
    • 西欧

第8章 ベンダー環境

第9章 総論・戦略的提言

図表

このページに掲載されている内容は最新版と異なる場合があります。詳細はお問い合わせください。

目次

"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
Back to Top