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The Big Data Market: 2017 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

April 2017 | 498 pages | ID: B6F24A53F77EN
SNS Telecom & IT

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“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 over $57 Billion in 2017 alone. These investments are further expected to grow at a CAGR of approximately 10% over the next three years.

The “Big Data Market: 2017 – 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 2017 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 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.
1 CHAPTER 1: INTRODUCTION

1.1 Executive Summary
1.2 Topics Covered
1.3 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: BIG DATA ANALYTICS

3.1 What are Big Data Analytics?
3.2 The Importance of Analytics
3.3 Reactive vs. Proactive Analytics
3.4 Customer vs. Operational Analytics
3.5 Technology & Implementation Approaches
  3.5.1 Grid Computing
  3.5.2 In-Database Processing
  3.5.3 In-Memory Analytics
  3.5.4 Machine Learning & Data Mining
  3.5.5 Predictive Analytics
  3.5.6 NLP (Natural Language Processing)
  3.5.7 Text Analytics
  3.5.8 Visual Analytics
  3.5.9 Social Media, IT & Telco Network Analytics

4 CHAPTER 4: BIG DATA IN AUTOMOTIVE, AEROSPACE & TRANSPORTATION

4.1 Overview & Investment Potential
4.2 Key Applications
  4.2.1 Autonomous Driving
  4.2.2 Warranty Analytics for Automotive OEMs
  4.2.3 Predictive Aircraft Maintenance & Fuel Optimization
  4.2.4 Air Traffic Control
  4.2.5 Transport Fleet Optimization
  4.2.6 UBI (Usage Based Insurance)
4.3 Case Studies
  4.3.1 Delphi Automotive: Monetizing Connected Vehicles with Big Data
  4.3.2 Boeing: Making Flying More Efficient with Big Data
  4.3.3 BMW: Eliminating Defects in New Vehicle Models with Big Data
  4.3.4 Toyota Motor Corporation: Powering Smart Cars with Big Data
  4.3.5 Ford Motor Company: Making Efficient Transportation Decisions with Big Data

5 CHAPTER 5: BIG DATA IN BANKING & SECURITIES

5.1 Overview & Investment Potential
5.2 Key Applications
  5.2.1 Customer Retention & Personalized Product Offering
  5.2.2 Risk Management
  5.2.3 Fraud Detection
  5.2.4 Credit Scoring
5.3 Case Studies
  5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data
  5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data
  5.3.3 OTP Bank: Reducing Loan Defaults with Big Data
  5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data

6 CHAPTER 6: BIG DATA IN DEFENSE & INTELLIGENCE

6.1 Overview & Investment Potential
6.2 Key Applications
  6.2.1 Intelligence Gathering
  6.2.2 Battlefield Analytics
  6.2.3 Energy Saving Opportunities in the Battlefield
  6.2.4 Preventing Injuries on the Battlefield
6.3 Case Studies
  6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data
  6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data
  6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats
  6.3.4 Ministry of State Security, China: Predictive Policing with Big Data
  6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data

7 CHAPTER 7: BIG DATA IN EDUCATION

7.1 Overview & Investment Potential
7.2 Key Applications
  7.2.1 Information Integration
  7.2.2 Identifying Learning Patterns
  7.2.3 Enabling Student-Directed Learning
7.3 Case Studies
  7.3.1 Purdue University: Ensuring Successful Higher Education Outcomes with Big Data
  7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data
  7.3.3 Edith Cowen University: Increasing Student Retention with Big Data

8 CHAPTER 8: BIG DATA IN HEALTHCARE & PHARMA

8.1 Overview & Investment Potential
8.2 Key Applications
  8.2.1 Managing Population Health Efficiently
  8.2.2 Improving Patient Care with Medical Data Analytics
  8.2.3 Improving Clinical Development & Trials
  8.2.4 Drug Development: Improving Time to Market
8.3 Case Studies
  8.3.1 Amino: Healthcare Transparency with Big Data
  8.3.2 Novartis: Digitizing Healthcare with Big Data
  8.3.3 GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data
  8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data
  8.3.5 Roche: Personalizing Healthcare with Big Data
  8.3.6 Sanofi: Proactive Diabetes Care with Big Data

9 CHAPTER 9: BIG DATA IN SMART CITIES & INTELLIGENT BUILDINGS

9.1 Overview & Investment Potential
9.2 Key Applications
  9.2.1 Energy Optimization & Fault Detection
  9.2.2 Intelligent Building Analytics
  9.2.3 Urban Transportation Management
  9.2.4 Optimizing Energy Production
  9.2.5 Water Management
  9.2.6 Urban Waste Management
9.3 Case Studies
  9.3.1 Singapore: Building a Smart Nation with Big Data
  9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data
  9.3.3 OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data

10 CHAPTER 10: BIG DATA IN INSURANCE

10.1 Overview & Investment Potential
10.2 Key Applications
  10.2.1 Claims Fraud Mitigation
  10.2.2 Customer Retention & Profiling
  10.2.3 Risk Management
10.3 Case Studies
  10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data
  10.3.2 RSA Group: Improving Customer Relations with Big Data
  10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data

11 CHAPTER 11: BIG DATA IN MANUFACTURING & NATURAL RESOURCES

11.1 Overview & Investment Potential
11.2 Key Applications
  11.2.1 Asset Maintenance & Downtime Reduction
  11.2.2 Quality & Environmental Impact Control
  11.2.3 Optimized Supply Chain
  11.2.4 Exploration & Identification of Natural Resources
11.3 Case Studies
  11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data
  11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data
  11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data
  11.3.4 Brunei: Saving Natural Resources with Big Data

12 CHAPTER 12: BIG DATA IN WEB, MEDIA & ENTERTAINMENT

12.1 Overview & Investment Potential
12.2 Key Applications
  12.2.1 Audience & Advertising Optimization
  12.2.2 Channel Optimization
  12.2.3 Recommendation Engines
  12.2.4 Optimized Search
  12.2.5 Live Sports Event Analytics
  12.2.6 Outsourcing Big Data Analytics to Other Verticals
12.3 Case Studies
  12.3.1 Netflix: Improving Viewership with Big Data
  12.3.2 NFL (National Football League): Improving Stadium Experience with Big Data
  12.3.3 Walt Disney Company: Enhancing Theme Park Experience with Big Data
  12.3.4 Baidu: Reshaping Search Capabilities with Big Data
  12.3.5 Constant Contact: Effective Marketing with Big Data

13 CHAPTER 13: BIG DATA IN PUBLIC SAFETY & HOMELAND SECURITY

13.1 Overview & Investment Potential
13.2 Key Applications
  13.2.1 Cyber Crime Mitigation
  13.2.2 Crime Prediction Analytics
  13.2.3 Video Analytics & Situational Awareness
13.3 Case Studies
  13.3.1 DHS (U.S. Department of Homeland Security): Identifying Threats to Physical and Network Infrastructure with Big Data
  13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data
  13.3.3 Memphis Police Department: Crime Reduction with Big Data

14 CHAPTER 14: BIG DATA IN PUBLIC SERVICES

14.1 Overview & Investment Potential
14.2 Key Applications
  14.2.1 Public Sentiment Analysis
  14.2.2 Tax Collection & Fraud Detection
  14.2.3 Economic Analysis
  14.2.4 Predicting & Mitigating Disasters
14.3 Case Studies
  14.3.1 ONS (Office for National Statistics): Exploring the UK Economy with Big Data
  14.3.2 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data
  14.3.3 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data
  14.3.4 City of Chicago: Improving Government Productivity with Big Data
  14.3.5 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data
  14.3.6 Ambulance Victoria: Improving Patient Survival Rates with Big Data

15 CHAPTER 15: BIG DATA IN RETAIL, WHOLESALE & HOSPITALITY

15.1 Overview & Investment Potential
15.2 Key Applications
  15.2.1 Customer Sentiment Analysis
  15.2.2 Customer & Branch Segmentation
  15.2.3 Price Optimization
  15.2.4 Personalized Marketing
  15.2.5 Optimizing & Monitoring the Supply Chain
  15.2.6 In-Field Sales Analytics
15.3 Case Studies
  15.3.1 Walmart: Making Smarter Stocking Decision with Big Data
  15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data
  15.3.3 Marriott International: Elevating Guest Services with Big Data
  15.3.4 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data

16 CHAPTER 16: BIG DATA IN TELECOMMUNICATIONS

16.1 Overview & Investment Potential
16.2 Key Applications
  16.2.1 Network Performance & Coverage Optimization
  16.2.2 Customer Churn Prevention
  16.2.3 Personalized Marketing
  16.2.4 Tailored Location Based Services
  16.2.5 Fraud Detection
16.3 Case Studies
  16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data
  16.3.2 AT&T: Smart Network Management with Big Data
  16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data
  16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data
  16.3.5 Freedom Mobile: Optimizing Video Quality with Big Data
  16.3.6 Coriant: SaaS Based Analytics with Big Data

17 CHAPTER 17: BIG DATA IN UTILITIES & ENERGY

17.1 Overview & Investment Potential
17.2 Key Applications
  17.2.1 Customer Retention
  17.2.2 Forecasting Energy
  17.2.3 Billing Analytics
  17.2.4 Predictive Maintenance
  17.2.5 Maximizing the Potential of Drilling
  17.2.6 Production Optimization
17.3 Case Studies
  17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data
  17.3.2 British Gas: Improving Customer Service with Big Data
  17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data

18 CHAPTER 18: BIG DATA INDUSTRY ROADMAP & VALUE CHAIN

18.1 Big Data Industry Roadmap
  18.1.1 2017 – 2020: Investments in Predictive Analytics & SaaS-Based Big Data Offerings
  18.1.2 2020 – 2025: Growing Focus on Cognitive & Personalized Analytics
  18.1.3 2025 – 2030: Convergence with Future IoT Applications
18.2 The Big Data Value Chain
  18.2.1 Hardware Providers
    18.2.1.1 Storage & Compute Infrastructure Providers
    18.2.1.2 Networking Infrastructure Providers
  18.2.2 Software Providers
    18.2.2.1 Hadoop & Infrastructure Software Providers
    18.2.2.2 SQL & NoSQL Providers
    18.2.2.3 Analytic Platform & Application Software Providers
    18.2.2.4 Cloud Platform Providers
  18.2.3 Professional Services Providers
  18.2.4 End-to-End Solution Providers
  18.2.5 Vertical Enterprises

19 CHAPTER 19: STANDARDIZATION & REGULATORY INITIATIVES

19.1 ASF (Apache Software Foundation)
  19.1.1 Management of Hadoop
  19.1.2 Big Data Projects Beyond Hadoop
19.2 CSA (Cloud Security Alliance)
  19.2.1 BDWG (Big Data Working Group)
19.3 CSCC (Cloud Standards Customer Council)
  19.3.1 Big Data Working Group
19.4 DMG (Data Mining Group)
  19.4.1 PMML (Predictive Model Markup Language) Working Group
  19.4.2 PFA (Portable Format for Analytics) Working Group
19.5 IEEE (Institute of Electrical and Electronics Engineers) –Big Data Initiative
19.6 INCITS (InterNational Committee for Information Technology Standards)
  19.6.1 Big Data Technical Committee
19.7 ISO (International Organization for Standardization)
  19.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange
  19.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
  19.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques
  19.7.4 ISO/IEC JTC 1/WG 9: Big Data
  19.7.5 Collaborations with Other ISO Work Groups
19.8 ITU (International Telecommunications Union)
  19.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
  19.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks
  19.8.3 Other Relevant Work
19.9 Linux Foundation
  19.9.1 ODPi (Open Ecosystem of Big Data)
19.10 NIST (National Institute of Standards and Technology)
  19.10.1 NBD-PWG (NIST Big Data Public Working Group)
19.11 OASIS (Organization for the Advancement of Structured Information Standards)
  19.11.1 Technical Committees
19.12 ODaF (Open Data Foundation)
  19.12.1 Big Data Accessibility
19.13 ODCA (Open Data Center Alliance)
  19.13.1 Work on Big Data
19.14 OGC (Open Geospatial Consortium)
  19.14.1 Big Data DWG (Domain Working Group)
19.15 TM Forum
  19.15.1 Big Data Analytics Strategic Program
19.16 TPC (Transaction Processing Performance Council)
  19.16.1 TPC-BDWG (TPC Big Data Working Group)
19.17 W3C (World Wide Web Consortium)
  19.17.1 Big Data Community Group
  19.17.2 Open Government Community Group

20 CHAPTER 20: MARKET ANALYSIS & FORECASTS

20.1 Global Outlook for the Big Data Market
20.2 Submarket Segmentation
  20.2.1 Storage and Compute Infrastructure
  20.2.2 Networking Infrastructure
  20.2.3 Hadoop & Infrastructure Software
  20.2.4 SQL
  20.2.5 NoSQL
  20.2.6 Analytic Platforms & Applications
  20.2.7 Cloud Platforms
  20.2.8 Professional Services
20.3 Vertical Market Segmentation
  20.3.1 Automotive, Aerospace & Transportation
  20.3.2 Banking & Securities
  20.3.3 Defense & Intelligence
  20.3.4 Education
  20.3.5 Healthcare & Pharmaceutical
  20.3.6 Smart Cities & Intelligent Buildings
  20.3.7 Insurance
  20.3.8 Manufacturing & Natural Resources
  20.3.9 Media & Entertainment
  20.3.10 Public Safety & Homeland Security
  20.3.11 Public Services
  20.3.12 Retail, Wholesale & Hospitality
  20.3.13 Telecommunications
  20.3.14 Utilities & Energy
  20.3.15 Other Sectors
20.4 Regional Outlook
20.5 Asia Pacific
  20.5.1 Country Level Segmentation
  20.5.2 Australia
  20.5.3 China
  20.5.4 India
  20.5.5 Indonesia
  20.5.6 Japan
  20.5.7 Malaysia
  20.5.8 Pakistan
  20.5.9 Philippines
  20.5.10 Singapore
  20.5.11 South Korea
  20.5.12 Taiwan
  20.5.13 Thailand
  20.5.14 Rest of Asia Pacific
20.6 Eastern Europe
  20.6.1 Country Level Segmentation
  20.6.2 Czech Republic
  20.6.3 Poland
  20.6.4 Russia
  20.6.5 Rest of Eastern Europe
20.7 Latin & Central America
  20.7.1 Country Level Segmentation
  20.7.2 Argentina
  20.7.3 Brazil
  20.7.4 Mexico
  20.7.5 Rest of Latin & Central America
20.8 Middle East & Africa
  20.8.1 Country Level Segmentation
  20.8.2 Israel
  20.8.3 Qatar
  20.8.4 Saudi Arabia
  20.8.5 South Africa
  20.8.6 UAE
  20.8.7 Rest of the Middle East & Africa
20.9 North America
  20.9.1 Country Level Segmentation
  20.9.2 Canada
  20.9.3 USA
20.10 Western Europe
  20.10.1 Country Level Segmentation
  20.10.2 Denmark
  20.10.3 Finland
  20.10.4 France
  20.10.5 Germany
  20.10.6 Italy
  20.10.7 Netherlands
  20.10.8 Norway
  20.10.9 Spain
  20.10.10 Sweden
  20.10.11 UK
  20.10.12 Rest of Western Europe

21 CHAPTER 21: VENDOR LANDSCAPE

21.1 1010data
21.2 Absolutdata
21.3 Accenture
21.4 Actian Corporation
21.5 Adaptive Insights
21.6 Advizor Solutions
21.7 AeroSpike
21.8 AFS Technologies
21.9 Alation
21.10 Algorithmia
21.11 Alluxio
21.12 Alpine Data
21.13 Alteryx
21.14 AMD (Advanced Micro Devices)
21.15 Apixio
21.16 Arcadia Data
21.17 Arimo
21.18 ARM
21.19 AtScale
21.20 Attivio
21.21 Attunity
21.22 Automated Insights
21.23 AWS (Amazon Web Services)
21.24 Axiomatics
21.25 Ayasdi
21.26 Basho Technologies
21.27 BCG (Boston Consulting Group)
21.28 Bedrock Data
21.29 BetterWorks
21.30 Big Cloud Analytics
21.31 Big Panda
21.32 Birst
21.33 Bitam
21.34 Blue Medora
21.35 BlueData Software
21.36 BlueTalon
21.37 BMC Software
21.38 BOARD International
21.39 Booz Allen Hamilton
21.40 Boxever
21.41 CACI International
21.42 Cambridge Semantics
21.43 Capgemini
21.44 Cazena
21.45 Centrifuge Systems
21.46 CenturyLink
21.47 Chartio
21.48 Cisco Systems
21.49 Civis Analytics
21.50 ClearStory Data
21.51 Cloudability
21.52 Cloudera
21.53 Clustrix
21.54 CognitiveScale
21.55 Collibra
21.56 Concurrent Computer Corporation
21.57 Confluent
21.58 Contexti
21.59 Continuum Analytics
21.60 Couchbase
21.61 CrowdFlower
21.62 Databricks
21.63 DataGravity
21.64 Dataiku
21.65 Datameer
21.66 DataRobot
21.67 DataScience
21.68 DataStax
21.69 DataTorrent
21.70 Datawatch Corporation
21.71 Datos IO
21.72 DDN (DataDirect Networks)
21.73 Decisyon
21.74 Dell Technologies
21.75 Deloitte
21.76 Demandbase
21.77 Denodo Technologies
21.78 Digital Reasoning Systems
21.79 Dimensional Insight
21.80 Dolphin Enterprise Solutions Corporation
21.81 Domino Data Lab
21.82 Domo
21.83 DriveScale
21.84 Dundas Data Visualization
21.85 DXC Technology
21.86 Eligotech
21.87 Engineering Group (Engineering Ingegneria Informatica)
21.88 EnterpriseDB
21.89 eQ Technologic
21.90 Ericsson
21.91 EXASOL
21.92 Facebook
21.93 FICO (Fair Isaac Corporation)
21.94 Fractal Analytics
21.95 Fujitsu
21.96 Fuzzy Logix
21.97 Gainsight
21.98 GE (General Electric)
21.99 Glassbeam
21.100 GoodData Corporation
21.101 Google
21.102 Greenwave Systems
21.103 GridGain Systems
21.104 Guavus
21.105 H2O.ai
21.106 HDS (Hitachi Data Systems)
21.107 Hedvig
21.108 Hortonworks
21.109 HPE (Hewlett Packard Enterprise)
21.110 Huawei
21.111 IBM Corporation
21.112 iDashboards
21.113 Impetus Technologies
21.114 Incorta
21.115 InetSoft Technology Corporation
21.116 Infer
21.117 Infor
21.118 Informatica Corporation
21.119 Information Builders
21.120 Infosys
21.121 Infoworks
21.122 Insightsoftware.com
21.123 InsightSquared
21.124 Intel Corporation
21.125 Interana
21.126 InterSystems Corporation
21.127 Jedox
21.128 Jethro
21.129 Jinfonet Software
21.130 Juniper Networks
21.131 KALEAO
21.132 Keen IO
21.133 Kinetica
21.134 KNIME
21.135 Kognitio
21.136 Kyvos Insights
21.137 Lavastorm
21.138 Lexalytics
21.139 Lexmark International
21.140 Logi Analytics
21.141 Longview Solutions
21.142 Looker Data Sciences
21.143 LucidWorks
21.144 Luminoso Technologies
21.145 Maana
21.146 Magento Commerce
21.147 Manthan Software Services
21.148 MapD Technologies
21.149 MapR Technologies
21.150 MariaDB Corporation
21.151 MarkLogic Corporation
21.152 Mathworks
21.153 MemSQL
21.154 Metric Insights
21.155 Microsoft Corporation
21.156 MicroStrategy
21.157 Minitab
21.158 MongoDB
21.159 Mu Sigma
21.160 Neo Technology
21.161 NetApp
21.162 Nimbix
21.163 Nokia
21.164 NTT Data Corporation
21.165 Numerify
21.166 NuoDB
21.167 Nutonian
21.168 NVIDIA Corporation
21.169 Oblong Industries
21.170 OpenText Corporation
21.171 Opera Solutions
21.172 Optimal Plus
21.173 Oracle Corporation
21.174 Palantir Technologies
21.175 Panorama Software
21.176 Paxata
21.177 Pentaho Corporation
21.178 Pepperdata
21.179 Phocas Software
21.180 Pivotal Software
21.181 Prognoz
21.182 Progress Software Corporation
21.183 PwC (PricewaterhouseCoopers International)
21.184 Pyramid Analytics
21.185 Qlik
21.186 Quantum Corporation
21.187 Qubole
21.188 Rackspace
21.189 Radius Intelligence
21.190 RapidMiner
21.191 Recorded Future
21.192 Red Hat
21.193 Redis Labs
21.194 RedPoint Global
21.195 Reltio
21.196 RStudio
21.197 Ryft Systems
21.198 Sailthru
21.199 Salesforce.com
21.200 Salient Management Company
21.201 Samsung Group
21.202 SAP
21.203 SAS Institute
21.204 ScaleDB
21.205 ScaleOut Software
21.206 SCIO Health Analytics
21.207 Seagate Technology
21.208 Sinequa
21.209 SiSense
21.210 SnapLogic
21.211 Snowflake Computing
21.212 Software AG
21.213 Splice Machine
21.214 Splunk
21.215 Sqrrl
21.216 Strategy Companion Corporation
21.217 StreamSets
21.218 Striim
21.219 Sumo Logic
21.220 Supermicro (Super Micro Computer)
21.221 Syncsort
21.222 SynerScope
21.223 Tableau Software
21.224 Talena
21.225 Talend
21.226 Tamr
21.227 TARGIT
21.228 TCS (Tata Consultancy Services)
21.229 Teradata Corporation
21.230 ThoughtSpot
21.231 TIBCO Software
21.232 Tidemark
21.233 Toshiba Corporation
21.234 Trifacta
21.235 Unravel Data
21.236 VMware
21.237 VoltDB
21.238 Waterline Data
21.239 Western Digital Corporation
21.240 WiPro
21.241 Workday
21.242 Xplenty
21.243 Yellowfin International
21.244 Yseop
21.245 Zendesk
21.246 Zoomdata
21.247 Zucchetti

22 CHAPTER 22: CONCLUSION & STRATEGIC RECOMMENDATIONS

22.1 Big Data Technology: Beyond Data Capture & Analytics
22.2 Transforming IT from a Cost Center to a Profit Center
22.3 Can Privacy Implications Hinder Success?
22.4 Maximizing Innovation with Careful Regulation
22.5 Battling Organizational & Data Silos
22.6 Moving Big Data to the Cloud
22.7 Software vs. Hardware Investments
22.8 Vendor Share: Who Leads the Market?
22.9 Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena
22.10 Big Data Driving Wider IT Industry Investments
22.11 Assessing the Impact of IoT & M2M
22.12 Recommendations
  22.12.1 Big Data Hardware, Software & Professional Services Providers
  22.12.2 Enterprises

LIST OF FIGURES

Figure 1: Hadoop Architecture
Figure 2: Reactive vs. Proactive Analytics
Figure 3: Big Data Industry Roadmap
Figure 4: Big Data Value Chain
Figure 5: Key Aspects of Big Data Standardization
Figure 6: Global Big Data Revenue: 2017 - 2030 ($ Million)
Figure 7: Global Big Data Revenue by Submarket: 2017 - 2030 ($ Million)
Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2017 - 2030 ($ Million)
Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2017 - 2030 ($ Million)
Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2017 - 2030 ($ Million)
Figure 11: Global Big Data SQL Submarket Revenue: 2017 - 2030 ($ Million)
Figure 12: Global Big Data NoSQL Submarket Revenue: 2017 - 2030 ($ Million)
Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2017 - 2030 ($ Million)
Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2017 - 2030 ($ Million)
Figure 15: Global Big Data Professional Services Submarket Revenue: 2017 - 2030 ($ Million)
Figure 16: Global Big Data Revenue by Vertical Market: 2017 - 2030 ($ Million)
Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2017 - 2030 ($ Million)
Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2017 - 2030 ($ Million)
Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2017 - 2030 ($ Million)
Figure 20: Global Big Data Revenue in the Education Sector: 2017 - 2030 ($ Million)
Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2017 - 2030 ($ Million)
Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2017 - 2030 ($ Million)
Figure 23: Global Big Data Revenue in the Insurance Sector: 2017 - 2030 ($ Million)
Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2017 - 2030 ($ Million)
Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2017 - 2030 ($ Million)
Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2017 - 2030 ($ Million)
Figure 27: Global Big Data Revenue in the Public Services Sector: 2017 - 2030 ($ Million)
Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2017 - 2030 ($ Million)
Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2017 - 2030 ($ Million)
Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2017 - 2030 ($ Million)
Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2017 - 2030 ($ Million)
Figure 32: Big Data Revenue by Region: 2017 - 2030 ($ Million)
Figure 33: Asia Pacific Big Data Revenue: 2017 - 2030 ($ Million)
Figure 34: Asia Pacific Big Data Revenue by Country: 2017 - 2030 ($ Million)
Figure 35: Australia Big Data Revenue: 2017 - 2030 ($ Million)
Figure 36: China Big Data Revenue: 2017 - 2030 ($ Million)
Figure 37: India Big Data Revenue: 2017 - 2030 ($ Million)
Figure 38: Indonesia Big Data Revenue: 2017 - 2030 ($ Million)
Figure 39: Japan Big Data Revenue: 2017 - 2030 ($ Million)
Figure 40: Malaysia Big Data Revenue: 2017 - 2030 ($ Million)
Figure 41: Pakistan Big Data Revenue: 2017 - 2030 ($ Million)
Figure 42: Philippines Big Data Revenue: 2017 - 2030 ($ Million)
Figure 43: Singapore Big Data Revenue: 2017 - 2030 ($ Million)
Figure 44: South Korea Big Data Revenue: 2017 - 2030 ($ Million)
Figure 45: Taiwan Big Data Revenue: 2017 - 2030 ($ Million)
Figure 46: Thailand Big Data Revenue: 2017 - 2030 ($ Million)
Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2017 - 2030 ($ Million)
Figure 48: Eastern Europe Big Data Revenue: 2017 - 2030 ($ Million)
Figure 49: Eastern Europe Big Data Revenue by Country: 2017 - 2030 ($ Million)
Figure 50: Czech Republic Big Data Revenue: 2017 - 2030 ($ Million)
Figure 51: Poland Big Data Revenue: 2017 - 2030 ($ Million)
Figure 52: Russia Big Data Revenue: 2017 - 2030 ($ Million)
Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2017 - 2030 ($ Million)
Figure 54: Latin & Central America Big Data Revenue: 2017 - 2030 ($ Million)
Figure 55: Latin & Central America Big Data Revenue by Country: 2017 - 2030 ($ Million)
Figure 56: Argentina Big Data Revenue: 2017 - 2030 ($ Million)
Figure 57: Brazil Big Data Revenue: 2017 - 2030 ($ Million)
Figure 58: Mexico Big Data Revenue: 2017 - 2030 ($ Million)
Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2017 - 2030 ($ Million)
Figure 60: Middle East & Africa Big Data Revenue: 2017 - 2030 ($ Million)
Figure 61: Middle East & Africa Big Data Revenue by Country: 2017 - 2030 ($ Million)
Figure 62: Israel Big Data Revenue: 2017 - 2030 ($ Million)
Figure 63: Qatar Big Data Revenue: 2017 - 2030 ($ Million)
Figure 64: Saudi Arabia Big Data Revenue: 2017 - 2030 ($ Million)
Figure 65: South Africa Big Data Revenue: 2017 - 2030 ($ Million)
Figure 66: UAE Big Data Revenue: 2017 - 2030 ($ Million)
Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2017 - 2030 ($ Million)
Figure 68: North America Big Data Revenue: 2017 - 2030 ($ Million)
Figure 69: North America Big Data Revenue by Country: 2017 - 2030 ($ Million)
Figure 70: Canada Big Data Revenue: 2017 - 2030 ($ Million)
Figure 71: USA Big Data Revenue: 2017 - 2030 ($ Million)
Figure 72: Western Europe Big Data Revenue: 2017 - 2030 ($ Million)
Figure 73: Western Europe Big Data Revenue by Country: 2017 - 2030 ($ Million)
Figure 74: Denmark Big Data Revenue: 2017 - 2030 ($ Million)
Figure 75: Finland Big Data Revenue: 2017 - 2030 ($ Million)
Figure 76: France Big Data Revenue: 2017 - 2030 ($ Million)
Figure 77: Germany Big Data Revenue: 2017 - 2030 ($ Million)
Figure 78: Italy Big Data Revenue: 2017 - 2030 ($ Million)
Figure 79: Netherlands Big Data Revenue: 2017 - 2030 ($ Million)
Figure 80: Norway Big Data Revenue: 2017 - 2030 ($ Million)
Figure 81: Spain Big Data Revenue: 2017 - 2030 ($ Million)
Figure 82: Sweden Big Data Revenue: 2017 - 2030 ($ Million)
Figure 83: UK Big Data Revenue: 2017 - 2030 ($ Million)
Figure 84: Big Data Revenue in the Rest of Western Europe: 2017 - 2030 ($ Million)
Figure 85: Global Big Data Workload Distribution by Environment: 2017 - 2030 (%)
Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2017 - 2030 ($ Million)
Figure 87: Big Data Vendor Market Share (%)
Figure 88: Global IT Expenditure Driven by Big Data Investments: 2017 - 2030 ($ Million)
Figure 89: Global M2M Connections by Access Technology: 2017 - 2030 (Millions)


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