2014

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

LONDON, July 17, 2014 /PRNewswire/ -- Reportbuyer.com has added a new market research report:

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

https://www.reportbuyer.com/product/2164339/The-Big-Data-Market-2014-–-2020---Opportunities-Challenges-Strategies-Industry-Verticals-and-Forecasts.html

"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.

Topics Covered:
The report covers the following topics:
- Big Data ecosystem
- Market drivers and barriers
- Big Data technology, standardization and regulatory initiatives
- Big Data industry roadmap and value chain
- Analysis and use cases for 15 vertical markets
- Big Data analytics technology and case studies
- Big Data vendor market share
- Company profiles and strategies of 90 Big Data ecosystem players
- Strategic recommendations for Big Data hardware, software and professional services vendors and enterprises
- Exclusive interview transcripts of 4 players in the Big Data ecosystem
- Market analysis and forecasts from 2014 till 2020

Forecast Segmentation:
Market forecasts and historical figures are provided for each of the following submarkets and their categories:
- Hardware, Software & Professional Services
Hardware
Software
Professional Services

- Horizontal Submarkets
Storage & Compute Infrastructure
Networking Infrastructure
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services

- Vertical Submarkets
Automotive, Aerospace & Transportation
Banking & Securities
Defense & Intelligence
Education
Healthcare & Pharmaceutical
Smart Cities & Intelligent Buildings
Insurance
Manufacturing & Natural Resources
Web, Media & Entertainment
Public Safety & Homeland Security
Public Services
Retail & Hospitality
Telecommunications
Utilities & Energy
Wholesale Trade
Others

- Regional Markets
Asia Pacific
Eastern Europe
Latin & Central America
Middle East & Africa
North America
Western Europe

- Country Markets
Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA

Key Findings:

- The report has the following key findings:
- In 2014 Big Data vendors will pocket nearly $30 Billion from hardware, software and professional services revenues
- Big Data investments are further expected to grow at a CAGR of nearly 17% over the next 6 years, eventually accounting for $76 Billion by the end of 2020
- The market is ripe for acquisitions of pure-play Big Data startups, as competition heats up between IT incumbents
- Nearly every large scale IT vendor maintains a Big Data portfolio
- At present, hardware sales and professional services account for more than 70% of all Big Data investments
- Going forward, software vendors, particularly those in the Big Data analytics segment, are expected to significantly increase their stake in the Big Data market as it matures

Key Questions Answered:

- How big is the Big Data ecosystem?
- How is the ecosystem evolving by segment and region?
- What will the market size be in 2020 and at what rate will it grow?
- What trends, challenges and barriers are influencing its growth?
- Who are the key Big Data software, hardware and services vendors and what are their strategies?
- How much are vertical enterprises investing in Big Data?
- What opportunities exist for Big Data analytics?
- Which countries and verticals will see the highest percentage of Big Data investments?

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

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


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

https://www.reportbuyer.com/product/2164339/The-Big-Data-Market-2014-–-2020---Opportunities-Challenges-Strategies-Industry-Verticals-and-Forecasts.html

For more information:
Sarah Smith
Research Advisor at Reportbuyer.com
Email: query@reportbuyer.com
Tel: +44 208 816 85 48
Website: www.reportbuyer.com

 

 

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