The Big Data Market: 2014 – 2020 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Date: June 10, 2014
Pages: 289
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Report type: Strategic Report
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The Big Data Market: 2014 – 2020 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts
“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 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 $30 Billion in 2014 alone. These investments are further expected to grow at a CAGR of 17% over the next 6 years.

The “Big Data Market: 2014 – 2020 – 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 2014 through to 2020. Historical figures are also presented for 2010, 2011, 2012 and 2013. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 34 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 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 Approaches to Big Data Processing
  2.2.1 Hadoop
  2.2.2 NoSQL
  2.2.3 MPAD (Massively Parallel Analytic Databases)
  2.2.4 Others & 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 & Beyond: Large Scale Proliferation of Scalable Machine Learning
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 Japan
  7.5.6 South Korea
  7.5.7 Pakistan
  7.5.8 Thailand
  7.5.9 Indonesia
  7.5.10 Malaysia
  7.5.11 Taiwan
  7.5.12 Philippines
  7.5.13 Singapore
  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 South Africa
  7.8.3 UAE
  7.8.4 Qatar
  7.8.5 Saudi Arabia
  7.8.6 Israel
  7.8.7 Rest of the Middle East & Africa
7.9 North America
  7.9.1 Country Level Segmentation
  7.9.2 USA
  7.9.3 Canada
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 Spain
  7.10.8 Sweden
  7.10.9 Norway
  7.10.10 UK
  7.10.11 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 AeroSpike
8.6 Alpine Data Labs
8.7 Alteryx
8.8 AWS (Amazon Web Services)
8.9 Attivio
8.10 Basho
8.11 Booz Allen Hamilton
8.12 InfiniDB
8.13 Capgemini
8.14 Cellwize
8.15 CenturyLink
8.16 Cisco Systems
8.17 Cloudera
8.18 Comptel
8.19 Contexti
8.20 Couchbase
8.21 CSC (Computer Science Corporation)
8.22 Datameer
8.23 DataStax
8.24 DDN (DataDirect Network)
8.25 Dell
8.26 Deloitte
8.27 Digital Reasoning
8.28 EMC Corporation
8.29 Facebook
8.30 Fractal Analytics
8.31 Fujitsu
8.32 Fusion-io
8.33 GE (General Electric)
8.34 GoodData Corporation
8.35 Google
8.36 Guavus
8.37 HDS (Hitachi Data Systems)
8.38 Hortonworks
8.39 HP
8.40 IBM
8.41 Informatica Corporation
8.42 Information Builders
8.43 Intel
8.44 Jaspersoft
8.45 Juniper Networks
8.46 Kognitio
8.47 Lavastorm Analytics
8.48 LucidWorks
8.49 MapR
8.50 MarkLogic
8.51 Microsoft
8.52 MicroStrategy
8.53 MongoDB (formerly 10gen)
8.54 Mu Sigma
8.55 NTT Data
8.56 Neo Technology
8.57 NetApp
8.58 Opera Solutions
8.59 Oracle
8.60 Palantir Technologies
8.61 ParStream
8.62 Pentaho
8.63 Platfora
8.64 Pivotal Software
8.65 PwC
8.66 QlikTech
8.67 Quantum Corporation
8.68 Rackspace
8.69 RainStor
8.70 Revolution Analytics
8.71 Salesforce.com
8.72 Sailthru
8.73 SAP
8.74 SAS Institute
8.75 SGI
8.76 SiSense
8.77 Software AG/Terracotta
8.78 Splunk
8.79 Sqrrl
8.80 Supermicro
8.81 Tableau Software
8.82 Talend
8.83 TCS (Tata Consultancy Services)
8.84 Teradata
8.85 Think Big Analytics
8.86 TIBCO Software
8.87 Tidemark
8.88 VMware (EMC Subsidiary)
8.89 WiPro
8.90 Zettics

9 CHAPTER 9: EXPERT OPINION – INTERVIEW TRANSCRIPTS

9.1 Comptel
9.2 Lavastorm Analytics
9.3 ParStream
9.4 Sailthru

10 CHAPTER 10: CONCLUSION & STRATEGIC RECOMMENDATIONS

10.1 Big Data Technology: Beyond Data Capture & Analytics
10.2 Transforming IT from a Cost Center to a Profit Center
10.3 Can Privacy Implications Hinder Success?
10.4 Will Regulation have a Negative Impact on Big Data Investments?
10.5 Battling Organization & Data Silos
10.6 Software vs. Hardware Investments
10.7 Vendor Share: Who Leads the Market?
10.8 Big Data Driving Wider IT Industry Investments
10.9 Assessing the Impact of IoT & M2M
10.10 Recommendations
  10.10.1 Big Data Hardware, Software & Professional Services Providers
  10.10.2 Enterprises

SNS Research's latest report indicates that global spending on Big Data technology is expected to reach nearly $30 Billion by the end of 2014.  Originally used as a term to describe datasets whose size is beyond the ability of traditional databases, the scope of Big Data 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 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 $30 Billion in 2014 alone. These investments are further expected to grow at a CAGR of 17% over the next 6 years. 

The “Big Data Market: 2014 – 2020” 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 2014 through to 2020. Historical figures are also presented for 2010, 2011, 2012 and 2013. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 34 countries.

The material was prepared in June, 2014.

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: 2010 - 2020 ($ Million)
Figure 5: Global Big Data Revenue by Submarket: 2010 - 2020 ($ Million)
Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2010 - 2020 ($ Million)
Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2010 - 2020 ($ Million)
Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2010 - 2020 ($ Million)
Figure 9: Global Big Data SQL Submarket Revenue: 2010 - 2020 ($ Million)
Figure 10: Global Big Data NoSQL Submarket Revenue: 2010 - 2020 ($ Million)
Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2010 - 2020 ($ Million)
Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2010 - 2020 ($ Million)
Figure 13: Global Big Data Professional Services Submarket Revenue: 2010 - 2020 ($ Million)
Figure 14: Global Big Data Revenue by Vertical Market: 2010 - 2020 ($ Million)
Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2010 - 2020 ($ Million)
Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2010 - 2020 ($ Million)
Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2010 - 2020 ($ Million)
Figure 18: Global Big Data Revenue in the Education Sector: 2010 - 2020 ($ Million)
Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2010 - 2020 ($ Million)
Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2010 - 2020 ($ Million)
Figure 21: Global Big Data Revenue in the Insurance Sector: 2010 - 2020 ($ Million)
Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2010 - 2020 ($ Million)
Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2010 - 2020 ($ Million)
Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2010 - 2020 ($ Million)
Figure 25: Global Big Data Revenue in the Public Services Sector: 2010 - 2020 ($ Million)
Figure 26: Global Big Data Revenue in the Retail & Hospitality Sector: 2010 - 2020 ($ Million)
Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2010 - 2020 ($ Million)
Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2010 - 2020 ($ Million)
Figure 29: Global Big Data Revenue in the Wholesale Trade Sector: 2010 - 2020 ($ Million)
Figure 30: Global Big Data Revenue in Other Vertical Sectors: 2010 - 2020 ($ Million)
Figure 31: Big Data Revenue by Region: 2010 - 2020 ($ Million)
Figure 32: Asia Pacific Big Data Revenue: 2010 - 2020 ($ Million)
Figure 33: Asia Pacific Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 34: Australia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 35: China Big Data Revenue: 2010 - 2020 ($ Million)
Figure 36: India Big Data Revenue: 2010 - 2020 ($ Million)
Figure 37: Japan Big Data Revenue: 2010 - 2020 ($ Million)
Figure 38: South Korea Big Data Revenue: 2010 - 2020 ($ Million)
Figure 39: Pakistan Big Data Revenue: 2010 - 2020 ($ Million)
Figure 40: Thailand Big Data Revenue: 2010 - 2020 ($ Million)
Figure 41: Indonesia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 42: Malaysia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 43: Taiwan Big Data Revenue: 2010 - 2020 ($ Million)
Figure 44: Philippines Big Data Revenue: 2010 - 2020 ($ Million)
Figure 45: Singapore Big Data Revenue: 2010 - 2020 ($ Million)
Figure 46: Big Data Revenue in the Rest of Asia Pacific: 2010 - 2020 ($ Million)
Figure 47: Eastern Europe Big Data Revenue: 2010 - 2020 ($ Million)
Figure 48: Eastern Europe Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 49: Czech Republic Big Data Revenue: 2010 - 2020 ($ Million)
Figure 50: Poland Big Data Revenue: 2010 - 2020 ($ Million)
Figure 51: Russia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 52: Big Data Revenue in the Rest of Eastern Europe: 2010 - 2020 ($ Million)
Figure 53: Latin & Central America Big Data Revenue: 2010 - 2020 ($ Million)
Figure 54: Latin & Central America Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 55: Argentina Big Data Revenue: 2010 - 2020 ($ Million)
Figure 56: Brazil Big Data Revenue: 2010 - 2020 ($ Million)
Figure 57: Mexico Big Data Revenue: 2010 - 2020 ($ Million)
Figure 58: Big Data Revenue in the Rest of Latin & Central America: 2010 - 2020 ($ Million)
Figure 59: Middle East & Africa Big Data Revenue: 2010 - 2020 ($ Million)
Figure 60: Middle East & Africa Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 61: South Africa Big Data Revenue: 2010 - 2020 ($ Million)
Figure 62: UAE Big Data Revenue: 2010 - 2020 ($ Million)
Figure 63: Qatar Big Data Revenue: 2010 - 2020 ($ Million)
Figure 64: Saudi Arabia Big Data Revenue: 2010 - 2020 ($ Million)
Figure 65: Israel Big Data Revenue: 2010 - 2020 ($ Million)
Figure 66: Big Data Revenue in the Rest of the Middle East & Africa: 2010 - 2020 ($ Million)
Figure 67: North America Big Data Revenue: 2010 - 2020 ($ Million)
Figure 68: North America Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 69: USA Big Data Revenue: 2010 - 2020 ($ Million)
Figure 70: Canada Big Data Revenue: 2010 - 2020 ($ Million)
Figure 71: Western Europe Big Data Revenue: 2010 - 2020 ($ Million)
Figure 72: Western Europe Big Data Revenue by Country: 2010 - 2020 ($ Million)
Figure 73: Denmark Big Data Revenue: 2010 - 2020 ($ Million)
Figure 74: Finland Big Data Revenue: 2010 - 2020 ($ Million)
Figure 75: France Big Data Revenue: 2010 - 2020 ($ Million)
Figure 76: Germany Big Data Revenue: 2010 - 2020 ($ Million)
Figure 77: Italy Big Data Revenue: 2010 - 2020 ($ Million)
Figure 78: Spain Big Data Revenue: 2010 - 2020 ($ Million)
Figure 79: Sweden Big Data Revenue: 2010 - 2020 ($ Million)
Figure 80: Norway Big Data Revenue: 2010 - 2020 ($ Million)
Figure 81: UK Big Data Revenue: 2010 - 2020 ($ Million)
Figure 82: Big Data Revenue in the Rest of Western Europe: 2010 - 2020 ($ Million)
Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services ($ Million): 2010 - 2020
Figure 84: Big Data Vendor Market Share (%)
Figure 85: Global IT Expenditure Driven by Big Data Investments: 2010 - 2020 ($ Million)
Figure 86: Global M2M Connections by Access Technology (Millions): 2011 - 2020

LIST OF COMPANIES MENTIONED

1010DATA
Accel Partners
Accenture
Actian Corporation
Actuate Corporation
adMarketplace
Adobe
ADP
AeroSpike
AlchemyDB
Aldeasa
Alpine Data Labs
Alteryx
Amazon.com
AMD
AnalyticsIQ
Antic Entertainment
AOL
Apple
AppNexus
Ascendas
AT&T
Attivio
AutoZone
Avvasi
AWS (Amazon Web Services)
Axiata Group
Bank of America
Basho
Beeline Kazakhstan
Betfair
BlueKai
Bluelock
BMC Software
BMW
Boeing
Booz Allen Hamilton
Box, Inc.
Buffalo Studios
BurstaBit
CaixaTarragona
Capgemini
Cellwize
CenturyLink
Chang
China Telecom
CIA (Central Intelligence Agency)
Cisco Systems
Citywire
Cloudera
Coca-Cola
Comptel
Concur
Contexti
Coriant
Couchbase
CSA (Cloud Security Alliance)
CSC (Computer Science Corporation)
CSCC (Cloud Standards Customer Council)
Datameer
DataStax
DDN (DataDirect Network)
Dell
Deloitte
Delta
Department of Commerce
Deutsche Bank
Deutsche Telekom
Digital Reasoning
Dollar General
Dotomi
eBay
El Corte Inglés
Electronic Arts
EMC Corporation
Equifax
Ericsson
Ernst & Young
E-Touch
European Space Agency
eXelate
Experian
Facebook
FedEx
Ferguson
Ford
Fractal Analytics
Fujitsu
Fusion-io
Gamegos
Ganz
GE (General Electric)
Goldman Sachs
GoodData Corporation
Google
Greylock Partners
GTRI (Georgia Tech Research Institute)
Guavus
Hadapt
HDS (Hitachi Data Systems)
Hortonworks
HP
Hyve Solutions
IBM
IEC (International Electrotechnical Commission)
Ignition Partners
InfiniDB
Infobright
Informatica Corporation
Information Builders
In-Q-Tel
Intel
Internap Network Services Corporation
Intucell
Inversis Banco
ISO (International Organization for Standardization)
ITT Corporation
ITU (International Telecommunications Union)
J.P. Morgan
Jaspersoft
Johnson & Johnson
JP Morgan
Juguettos
Juniper Networks
Kabam
Karmasphere
KDDI
Kixeye
Kobo
Kognitio
KPMG
KT (Korea Telecom)
Lavastorm Analytics
LG CNS
LinkedIn
LucidWorks
Mahindra Satyam
MapR
MarkLogic
Marriott International
Mayfield fund
McDonnell Ventures
McGraw Hill Education
MediaMind
Meritech Capital Partners
Microsoft
MicroStrategy
mig33
MongoDB
Motorola
Movistar
Mu Sigma
Myrrix
Nami Media
Navteq
Neo Technology
NetApp
NetFlix
Nexon
NIST (National Institute of Standards and Technology)
North Bridge
NTT Data
NTT DoCoMo
NYSE (New York Stock Exchange)
OASIS
ODaF (Open Data Foundation)
Open Data Center Alliance
Opera Solutions
Oracle
Orange
Orbitz
Palantir Technologies
Panorama Software
ParAccel
ParStream
Pentaho
Pervasive Software
Pivotal Software
Platfora
Playtika
Pokemon
Proctor and Gamble
Pronovias
PwC
QlikTech
Quantum Corporation
Quiterian
Rackspace
RainStor
Relational Technology
Renault
ReNet Tecnologia
Rentrak
Revolution Analytics
RiteAid
Robi Axiata
Royal Dutch Shell
Sabre
Sailthru
Sain Engineering
Salesforce.com
Samsung
SAP
SAS Institute
Savvis
Scoreloop
Seagate Technology
SGI
Shuffle Master
Simba Technologies
SiSense
Skyscanner
SmugMug
Snapdeal
Software AG
Sojo Studios
SolveDirect
Sony
Southern States Cooperative
Splunk
Spotme
Sqrrl
Starbucks
Supermicro
Tableau Software
Talend
Tango
TapJoy
TCS (Tata Consultancy Services)
Telefónica
Tencent
Teradata
Terracotta
Terremark
The Hut Group
The Knot
The Ladders
The Trade Desk
Think Big Analytics
Thomson Reuters
TIBCO Software
Tidemark
TubeMogul
Tunewiki
U.S. Air Force
U.S. Army
U.S. Navy
Ubiquisys
UBS
Umami TV
UN (United Nations)
Unilever
US Xpress
Venture Partners
Verizon
Versant
Vertica
VIMPELCOM
VMware (EMC Subsidiary)
VNG
Vodafone
Volkswagen
Walt Disney Company
WIND Mobile
WiPro
Xclaim
Xyratex
Yael Software
Zettics
Zynga

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