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The Big Data Market: 2016 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Published: Jun 10, 2016 | Pages: 390 | Publisher: SNS Research | Industry: Telecommunications | Report Format: Electronic (PDF)

“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 $46 Billion in 2016 alone. These investments are further expected to grow at a CAGR of 12% over the next four years.

The “Big Data Market: 2016 - 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 2016 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.

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 14 vertical markets
 - Big Data analytics technology and case studies
 - Big Data vendor market share
 - Company profiles and strategies of 150 Big Data ecosystem players
 - Strategic recommendations for Big Data hardware, software and professional services vendors and enterprises
 - Market analysis and forecasts from 2016 till 2030

Historical Revenue & Forecast Segmentation

Market forecasts and historical revenue figures are provided for each of the following submarkets and their subcategories:

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, Wholesale & Hospitality
 - Telecommunications
 - Utilities & Energy
 - 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, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK,  USA

Key Questions Answered 
The report provides answers to the following key questions:
 - 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?

Key Findings 
The report has the following key findings: 
 - In 2016, Big Data vendors will pocket over $46 Billion from hardware, software and professional services revenues.
 - Big Data investments are further expected to grow at a CAGR of 12% over the next four years, eventually accounting for over $72 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, the market is largely dominated by hardware sales and professional services in terms of revenue.
 - Going forward, software vendors, particularly those in the Big Data analytics segment, are expected to significantly increase their stake in the Big Data market.
 - By the end of 2020, SNS Research expects Big Data software revenue to exceed hardware investments by over $7 Billion.

List of Companies Mentioned
1010data
Accel Partners
Accenture
Actian Corporation
Actuate Corporation
Adaptive Insights
adMarketplace
Adobe
ADP
Advizor Solutions
AeroSpike
AFS Technologies
Alameda County Social Services Agency
AlchemyDB
Aldeasa
Alpine Data Labs
Alteryx
Altiscale
Altosoft
Amazon.com
Ambulance Victoria
AMD
AnalyticsIQ
Antic Entertainment
Antivia
AOL
Apple
AppNexus
Arcplan
Ascendas
AT&T
Attivio
Automated Insights
AutoZone
Avvasi
AWS (Amazon Web Services)
Axiata Group
Ayasdi
BAE Systems
Baidu
Bank of America
Basho
Beeline Kazakhstan
Betfair
BeyondCore
Birst
Bitam
BlueKai
Bluelock 
BMC Software
BMW
Board International
Boeing
Booz Allen Hamilton
Box
British Gas
BT Group
Buffalo Studios
BurstaBit
CaixaTarragona
Capgemini
CBA (Commonwealth Bank of Australia)
Cellwize
Centrifuge Systems
CenturyLink
CETC (China Electronics Technology Group)
Chang
Chartio
Chevron Technology Ventures
China Telecom
Chinese Ministry of State Security
CIA (Central Intelligence Agency)
Cisco Systems
Citywire
ClearStory Data
Cloudera
Coca-Cola
Comptel
Concur
Concurrent
Constant Contact
Contexti
Coriant
Couchbase
CSA (Cloud Security Alliance)
CSC (Computer Science Corporation)
CSCC (Cloud Standards Customer Council)
DataHero
Datameer
DataRPM
DataStax
Datawatch Corporation
DDN (DataDirect Network)
Decisyon
Dell
Deloitte
Delta
Denodo Technologies
Deutsche Bank
Digital Reasoning
Dimensional Insight
Dollar General 
Domo
Dotomi
Dow Chemical Company
DT (Deutsche Telekom)
Dubai Police
Dundas Data Visualization
eBay
Edith Cowen University
El Corte Inglés
Electronic Arts
Eligotech
EMC Corporation
Engineering Group (Engineering Ingegneria Informatica)
eQ Technologic
Equifax
Ericsson
Ernst & Young 
E-Touch
European Space Agency
eXelate
Experian
Facebook
FDNY (Fire Department of the City of New York)
FedEx
Ferguson Enterprises
FICO
Ford Motor Company
Foundation Medicine
Fractal Analytics
French DGSE (General Directorate for External Security)
Fujitsu
Fusion-io
Gamegos
Ganz
GE (General Electric)
Glasgow City Council
Goldman Sachs
GoodData Corporation
Google
Greylock Partners
GSK (GlaxoSmithKline)
GTRI (Georgia Tech Research Institute) 
Guavus
Hadapt
HDS (Hitachi Data Systems)
Hortonworks
HPE (Hewlett Packard Enterprise)
HSBC Group
Hyve Solutions
IBM
iDashboards
IEC (International Electrotechnical Commission)
Ignition Partners 
Incorta
InetSoft Technology Corporation
InfiniDB
Infobright
Infor
Informatica Corporation
Information Builders
In-Q-Tel
Intel Corporation
Internap Network Services Corporation
Intucell
Inversis Banco
ISO (International Organization for Standardization)
ITT Corporation
ITU (International Telecommunications Union)
J.P. Morgan
Jaspersoft
Jedox
Jinfonet Software
JJ Food Service
Johnson & Johnson
JPMorgan Chase & Co.
Juguettos
Juniper Networks
Kabam 
Karmasphere
KDDI
Kixeye 
Knime
Kobo
Kofax
Kognitio
KPMG
KT (Korea Telecom)
L-3 Communications
L-3 Data Tactics
Lavastorm Analytics
LG CNS
LinkedIn
Logi Analytics
Logos Technologies
Looker Data Sciences
LucidWorks
Maana
Mahindra Satyam
Manthan Software Services
MapR
MarkLogic
Marriott International
Mayfield fund
McDonnell Ventures
McGraw Hill Education
MediaMind
Memphis Police Department
MemSQL
Meritech Capital Partners
Michelin
Microsoft
MicroStrategy
mig33
MongoDB
MongoDB (Formerly 10gen)
Movistar
Mu Sigma
Myrrix 
Nami Media
NASA (National Aeronautics and Space Administration)
Navteq
Neo Technology
NetApp
NetFlix 
New York State Department of Taxation and Finance
Nexon
Nextbio
NFL (National Football League)
NIST (National Institute of Standards and Technology)
North Bridge
Northwest Analytics
Nottingham Trent University
Novartis
NSA (National Security Agency)
NTT Data
NTT DoCoMo
Nutonian
NYSE (New York Stock Exchange)
OASIS
ODaF (Open Data Foundation)
Ofcom
Oncor Electric Delivery
Open Data Center Alliance
OpenText Corporation
Opera Solutions
Optimal+
Oracle Corporation
Orange
Orbitz
OTP Bank
OVG Real Estate
Palantir Technologies
Panorama Software
ParAccel 
ParStream
Pentaho
Pervasive Software
Pfizer
Phocas
Pivotal Software
Platfora
Playtika
Primerica
Proctor and Gamble
Prognoz
Pronovias
Purdue University
PwC
Pyramid Analytics
Qlik
QPC
Quantum Corporation
Qubole
Quiterian 
Rackspace
RainStor
RapidMiner
Recorded Future
Relational Technology
Renault 
ReNet Tecnologia
Rentrak 
Revolution Analytics
RiteAid
RJMetrics
Robi Axiata
Roche
Royal Dutch Shell
Royal Navy
RSA Group
Sabre
Sailthru
Sain Engineering
Salesforce.com
Salient Management Company
Samsung 
Sanofi
SAP
SAS Institute
Saudi Aramco Energy Ventures
Savvis 
Scoreloop
Seagate Technology
SGI
Shuffle Master
Simba Technologies 
SiSense
Skyscanner
SmugMug
Snapdeal
Software AG
Sojo Studios
SolveDirect
Sony Corporation
Southern States Cooperative 
SpagoBI Labs
Splice Machine
Splunk
Spotfire
Spotme
Sqrrl
Starbucks
Strategy Companion
Supermicro
Syncsort
SynerScope
Tableau Software
Talend
Tango
TapJoy
Targit
TCS (Tata Consultancy Services)
Telefónica
Tencent
TEOCO
Teradata
Terracotta
Terremark
Tesco
Thales Group
The Hut Group
The Knot
The Ladders
The Trade Desk 
Think Big Analytics
Thomson Reuters
ThoughtSpot
TIBCO Software
Tidemark
T-Mobile USA
Toyota Motor Corporation
TubeMogul
Tunewiki
U.S. Air Force
U.S. Army
U.S. CBP (Customs and Border Protection)
U.S. Coast Guard
U.S. Department of Commerce
U.S. DHS (Department of Homeland Security)
U.S. ICE (Immigration and Customs Enforcement)
U.S. Navy
Ubiquisys 
UBS
UIEvolution
Umami TV
UN (United Nations) 
Unilever
US Xpress
Venture Partners
Verizon Communications
Versant
Vertica
VIMPELCOM
VMware
VNG
Vodafone
Volkswagen
Walmart
Walt Disney Company
WIND Mobile
WiPro
Xclaim
Xyratex
Yael Software
Yellowfin International
Zebra Technologies
Zendesk
Zettics
Zoomdata
Zucchetti
Zurich Insurance Group
Zynga
 Table of Contents

1	Chapter 1: Introduction	21
1.1	Executive Summary	21
1.2	Topics Covered	23
1.3	Historical Revenue & Forecast Segmentation	24
1.4	Key Questions Answered	26
1.5	Key Findings	27
1.6	Methodology	28
1.7	Target Audience	29
1.8	Companies & Organizations Mentioned	30
		
2	Chapter 2: An Overview of Big Data	34
2.1	What is Big Data?	34
2.2	Key Approaches to Big Data Processing	34
2.2.1	Hadoop	35
2.2.2	NoSQL	36
2.2.3	MPAD (Massively Parallel Analytic Databases)	36
2.2.4	In-memory Processing	37
2.2.5	Stream Processing Technologies	37
2.2.6	Spark	38
2.2.7	Other Databases & Analytic Technologies	38
2.3	Key Characteristics of Big Data	39
2.3.1	Volume	39
2.3.2	Velocity	39
2.3.3	Variety	39
2.3.4	Value	40
2.4	Market Growth Drivers	41
2.4.1	Awareness of Benefits	41
2.4.2	Maturation of Big Data Platforms	41
2.4.3	Continued Investments by Web Giants, Governments & Enterprises	42
2.4.4	Growth of Data Volume, Velocity & Variety	42
2.4.5	Vendor Commitments & Partnerships	42
2.4.6	Technology Trends Lowering Entry Barriers	43
2.5	Market Barriers	43
2.5.1	Lack of Analytic Specialists	43
2.5.2	Uncertain Big Data Strategies	43
2.5.3	Organizational Resistance to Big Data Adoption	44
2.5.4	Technical Challenges: Scalability & Maintenance	44
2.5.5	Security & Privacy Concerns	44
		
3	Chapter 3: Big Data Analytics	46
3.1	What are Big Data Analytics?	46
3.2	The Importance of Analytics	46
3.3	Reactive vs. Proactive Analytics	47
3.4	Customer vs. Operational Analytics	48
3.5	Technology & Implementation Approaches	48
3.5.1	Grid Computing	48
3.5.2	In-Database Processing	49
3.5.3	In-Memory Analytics	49
3.5.4	Machine Learning & Data Mining	49
3.5.5	Predictive Analytics	50
3.5.6	NLP (Natural Language Processing)	50
3.5.7	Text Analytics	51
3.5.8	Visual Analytics	52
3.5.9	Social Media, IT & Telco Network Analytics	52
		
4	Chapter 4: Big Data in Automotive, Aerospace & Transportation	53
4.1	Overview & Investment Potential	53
4.2	Key Applications	53
4.2.1	Warranty Analytics for Automotive OEMs	54
4.2.2	Predictive Aircraft Maintenance & Fuel Optimization	54
4.2.3	Air Traffic Control	55
4.2.4	Transport Fleet Optimization	55
4.3	Case Studies	55
4.3.1	Boeing: Making Flying More Efficient with Big Data	56
4.3.2	BMW: Eliminating Defects in New Vehicle Models with Big Data	57
4.3.3	Toyota Motor Corporation: Powering Smart Cars with Big Data	58
4.3.4	Ford Motor Company: Making Efficient Transportation Decisions with Big Data	59
		
5	Chapter 5: Big Data in Banking & Securities	60
5.1	Overview & Investment Potential	60
5.2	Key Applications	60
5.2.1	Customer Retention & Personalized Product Offering	60
5.2.2	Risk Management	61
5.2.3	Fraud Detection	61
5.2.4	Credit Scoring	61
5.3	Case Studies	61
5.3.1	HSBC Group: Avoiding Regulatory Penalties with Big Data	62
5.3.2	JPMorgan Chase & Co.: Improving Business Processes with Big Data	63
5.3.3	OTP Bank: Reducing Loan Defaults with Big Data	64
5.3.4	CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data	65
		
6	Chapter 6: Big Data in Defense & Intelligence	66
6.1	Overview & Investment Potential	66
6.2	Key Applications	66
6.2.1	Intelligence Gathering	66
6.2.2	Battlefield Analytics	67
6.2.3	Energy Saving Opportunities in the Battlefield	67
6.2.4	Preventing Injuries on the Battlefield	68
6.3	Case Studies	69
6.3.1	U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data	69
6.3.2	Royal Navy: Empowering Submarine Warfare with Big Data	70
6.3.3	NSA (National Security Agency): Capitalizing on Big Data to Detect Threats	71
6.3.4	Chinese Ministry of State Security: Predictive Policing with Big Data	72
6.3.5	French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data	73
		
7	Chapter 7: Big Data in Education	75
7.1	Overview & Investment Potential	75
7.2	Key Applications	75
7.2.1	Information Integration	75
7.2.2	Identifying Learning Patterns	76
7.2.3	Enabling Student-Directed Learning	76
7.3	Case Studies	76
7.3.1	Purdue University: Ensuring Successful Higher Education Outcomes with Big Data	77
7.3.2	Nottingham Trent University: Successful Student Outcomes with Big Data	78
7.3.3	Edith Cowen University: Increasing Student Retention with Big Data	79
		
8	Chapter 8: Big Data in Healthcare & Pharma	80
8.1	Overview & Investment Potential	80
8.2	Key Applications	80
8.2.1	Managing Population Health Efficiently	80
8.2.2	Improving Patient Care with Medical Data Analytics	81
8.2.3	Improving Clinical Development & Trials	81
8.2.4	Drug Development: Improving Time to Market	81
8.3	Case Studies	82
8.3.1	Novartis: Digitizing Healthcare with Big Data	82
8.3.2	GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data	83
8.3.3	Pfizer: Developing Effective and Targeted Therapies with Big Data	84
8.3.4	Roche: Personalizing Healthcare with Big Data	85
8.3.5	Sanofi: Proactive Diabetes Care with Big Data	86
		
9	Chapter 9: Big Data in Smart Cities & Intelligent Buildings	88
9.1	Overview & Investment Potential	88
9.2	Key Applications	88
9.2.1	Energy Optimization & Fault Detection	88
9.2.2	Intelligent Building Analytics	89
9.2.3	Urban Transportation Management	89
9.2.4	Optimizing Energy Production	89
9.2.5	Water Management	90
9.2.6	Urban Waste Management	90
9.3	Case Studies	90
9.3.1	Singapore: Building a Smart Nation with Big Data	90
9.3.2	Glasgow City Council: Promoting Smart City Efforts with Big Data	92
9.3.3	OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data	93
		
10	Chapter 10: Big Data in Insurance	94
10.1	Overview & Investment Potential	94
10.2	Key Applications	94
10.2.1	Claims Fraud Mitigation	94
10.2.2	Customer Retention & Profiling	95
10.2.3	Risk Management	95
10.3	Case Studies	95
10.3.1	Zurich Insurance Group: Enhancing Risk Management with Big Data	95
10.3.2	RSA Group: Improving Customer Relations with Big Data	97
10.3.3	Primerica: Improving Insurance Sales Force Productivity with Big Data	98
		
11	Chapter 11: Big Data in Manufacturing & Natural Resources	99
11.1	Overview & Investment Potential	99
11.2	Key Applications	99
11.2.1	Asset Maintenance & Downtime Reduction	99
11.2.2	Quality & Environmental Impact Control	100
11.2.3	Optimized Supply Chain	100
11.2.4	Exploration & Identification of Natural Resources	100
11.3	Case Studies	101
11.3.1	Intel Corporation: Cutting Manufacturing Costs with Big Data	101
11.3.2	Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data	102
11.3.3	Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data	103
11.3.4	Brunei: Saving Natural Resources with Big Data	104
		
12	Chapter 12: Big Data in Web, Media & Entertainment	105
12.1	Overview & Investment Potential	105
12.2	Key Applications	105
12.2.1	Audience & Advertising Optimization	106
12.2.2	Channel Optimization	106
12.2.3	Recommendation Engines	106
12.2.4	Optimized Search	106
12.2.5	Live Sports Event Analytics	107
12.2.6	Outsourcing Big Data Analytics to Other Verticals	107
12.3	Case Studies	107
12.3.1	NFL (National Football League): Improving Stadium Experience with Big Data	107
12.3.2	Walt Disney Company: Enhancing Theme Park Experience with Big Data	109
12.3.3	Baidu: Reshaping Search Capabilities with Big Data	110
12.3.4	Constant Contact: Effective Marketing with Big Data	111
		
13	Chapter 13: Big Data in Public Safety & Homeland Security	112
13.1	Overview & Investment Potential	112
13.2	Key Applications	112
13.2.1	Cyber Crime Mitigation	113
13.2.2	Crime Prediction Analytics	113
13.2.3	Video Analytics & Situational Awareness	113
13.3	Case Studies	114
13.3.1	U.S. DHS (Department of Homeland Security): Identifying Threats to Physical and Network Infrastructure with Big Data	114
13.3.2	Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data	115
13.3.3	Memphis Police Department: Crime Reduction with Big Data	116
		
14	Chapter 14: Big Data in Public Services	117
14.1	Overview & Investment Potential	117
14.2	Key Applications	117
14.2.1	Public Sentiment Analysis	117
14.2.2	Tax Collection & Fraud Detection	118
14.2.3	Economic Analysis	118
14.3	Case Studies	118
14.3.1	New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data	118
14.3.2	Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data	119
14.3.3	City of Chicago: Improving Government Productivity with Big Data	120
14.3.4	FDNY (Fire Department of the City of New York): Fighting Fires with Big Data	121
14.3.5	Ambulance Victoria: Improving Patient Survival Rates with Big Data	122
		
15	Chapter 15: Big Data in Retail, Wholesale & Hospitality	124
15.1	Overview & Investment Potential	124
15.2	Key Applications	124
15.2.1	Customer Sentiment Analysis	125
15.2.2	Customer & Branch Segmentation	125
15.2.3	Price Optimization	125
15.2.4	Personalized Marketing	125
15.2.5	Optimizing & Monitoring the Supply Chain	126
15.2.6	In-field Sales Analytics	126
15.3	Case Studies	126
15.3.1	Walmart: Making Smarter Stocking Decision with Big Data	127
15.3.2	Tesco: Reducing Supermarket Energy Bills with Big Data	128
15.3.3	Marriott International: Elevating Guest Services with Big Data	129
15.3.4	JJ Food Service: Predictive Wholesale Shopping Lists with Big Data	130
		
16	Chapter 16: Big Data in Telecommunications	131
16.1	Overview & Investment Potential	131
16.2	Key Applications	131
16.2.1	Network Performance & Coverage Optimization	131
16.2.2	Customer Churn Prevention	132
16.2.3	Personalized Marketing	132
16.2.4	Tailored Location Based Services	132
16.2.5	Fraud Detection	132
16.3	Case Studies	133
16.3.1	BT Group: Hunting Down Nuisance Callers with Big Data	133
16.3.2	AT&T: Smart Network Management with Big Data	134
16.3.3	T-Mobile USA: Cutting Down Churn Rate with Big Data	135
16.3.4	TEOCO: Helping Service Providers Save Millions with Big Data	136
16.3.5	WIND Mobile: Optimizing Video Quality with Big Data	137
16.3.6	Coriant: SaaS Based Analytics with Big Data	138
		
17	Chapter 17: Big Data in Utilities & Energy	139
17.1	Overview & Investment Potential	139
17.2	Key Applications	139
17.2.1	Customer Retention	139
17.2.2	Forecasting Energy	140
17.2.3	Billing Analytics	140
17.2.4	Predictive Maintenance	140
17.2.5	Maximizing the Potential of Drilling	140
17.2.6	Production Optimization	141
17.3	Case Studies	141
17.3.1	Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data	141
17.3.2	British Gas: Improving Customer Service with Big Data	142
17.3.3	Oncor Electric Delivery: Intelligent Power Grid Management with Big Data	143
		
18	Chapter 18: Big Data Industry Roadmap & Value Chain	144
18.1	Big Data Industry Roadmap	144
18.1.1	2010 – 2013: Initial Hype and the Rise of Analytics	144
18.1.2	2014 – 2017: Emergence of SaaS Based Big Data Solutions	145
18.1.3	2018 – 2020: Growing Adoption of Scalable Machine Learning	146
18.1.4	2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics	146
18.2	The Big Data Value Chain	147
18.2.1	Hardware Providers	147
18.2.1.1	Storage & Compute Infrastructure Providers	147
18.2.1.2	Networking Infrastructure Providers	148
18.2.2	Software Providers	149
18.2.2.1	Hadoop & Infrastructure Software Providers	149
18.2.2.2	SQL & NoSQL Providers	149
18.2.2.3	Analytic Platform & Application Software Providers	149
18.2.2.4	Cloud Platform Providers	150
18.2.3	Professional Services Providers	150
18.2.4	End-to-End Solution Providers	150
18.2.5	Vertical Enterprises	150
		
19	Chapter 19: Standardization & Regulatory Initiatives	151
19.1	CSCC (Cloud Standards Customer Council) – Big Data Working Group	151
19.2	NIST (National Institute of Standards and Technology) – Big Data Working Group	152
19.3	OASIS –Technical Committees	153
19.4	ODaF (Open Data Foundation)	154
19.5	Open Data Center Alliance	154
19.6	CSA (Cloud Security Alliance) – Big Data Working Group	155
19.7	ITU (International Telecommunications Union)	156
19.8	ISO (International Organization for Standardization) and Others	156
		
20	Chapter 20: Market Analysis & Forecasts	157
20.1	Global Outlook of the Big Data Market	157
20.2	Submarket Segmentation	158
20.2.1	Storage and Compute Infrastructure	159
20.2.2	Networking Infrastructure	160
20.2.3	Hadoop & Infrastructure Software	161
20.2.4	SQL	162
20.2.5	NoSQL	163
20.2.6	Analytic Platforms & Applications	164
20.2.7	Cloud Platforms	165
20.2.8	Professional Services	166
20.3	Vertical Market Segmentation	167
20.3.1	Automotive, Aerospace & Transportation	168
20.3.2	Banking & Securities	169
20.3.3	Defense & Intelligence	170
20.3.4	Education	171
20.3.5	Healthcare & Pharmaceutical	172
20.3.6	Smart Cities & Intelligent Buildings	173
20.3.7	Insurance	174
20.3.8	Manufacturing & Natural Resources	175
20.3.9	Media & Entertainment	176
20.3.10	Public Safety & Homeland Security	177
20.3.11	Public Services	178
20.3.12	Retail, Wholesale & Hospitality	179
20.3.13	Telecommunications	180
20.3.14	Utilities & Energy	181
20.3.15	Other Sectors	182
20.4	Regional Outlook	183
20.5	Asia Pacific	184
20.5.1	Country Level Segmentation	184
20.5.2	Australia	185
20.5.3	China	185
20.5.4	India	186
20.5.5	Indonesia	186
20.5.6	Japan	187
20.5.7	Malaysia	187
20.5.8	Pakistan	188
20.5.9	Philippines	188
20.5.10	Singapore	189
20.5.11	South Korea	189
20.5.12	Taiwan	190
20.5.13	Thailand	190
20.5.14	Rest of Asia Pacific	191
20.6	Eastern Europe	192
20.6.1	Country Level Segmentation	192
20.6.2	Czech Republic	193
20.6.3	Poland	193
20.6.4	Russia	194
20.6.5	Rest of Eastern Europe	194
20.7	Latin & Central America	195
20.7.1	Country Level Segmentation	195
20.7.2	Argentina	196
20.7.3	Brazil	196
20.7.4	Mexico	197
20.7.5	Rest of Latin & Central America	197
20.8	Middle East & Africa	198
20.8.1	Country Level Segmentation	198
20.8.2	Israel	199
20.8.3	Qatar	199
20.8.4	Saudi Arabia	200
20.8.5	South Africa	200
20.8.6	UAE	201
20.8.7	Rest of the Middle East & Africa	201
20.9	North America	202
20.9.1	Country Level Segmentation	202
20.9.2	Canada	203
20.9.3	USA	203
20.10	Western Europe	204
20.10.1	Country Level Segmentation	204
20.10.2	Denmark	205
20.10.3	Finland	205
20.10.4	France	206
20.10.5	Germany	206
20.10.6	Italy	207
20.10.7	Netherlands	207
20.10.8	Norway	208
20.10.9	Spain	208
20.10.10	Sweden	209
20.10.11	UK	209
20.10.12	Rest of Western Europe	210
		
21	Chapter 21: Vendor Landscape	211
21.1	1010data	211
21.2	Accenture	213
21.3	Actian Corporation	215
21.4	Actuate Corporation	217
21.5	Adaptive Insights	219
21.6	Advizor Solutions	220
21.7	AeroSpike	221
21.8	AFS Technologies	223
21.9	Alpine Data Labs	224
21.10	Alteryx	225
21.11	Altiscale	227
21.12	Antivia	228
21.13	Arcplan	229
21.14	Attivio	230
21.15	Automated Insights	232
21.16	AWS (Amazon Web Services)	233
21.17	Ayasdi	235
21.18	Basho	236
21.19	BeyondCore	238
21.20	Birst	239
21.21	Bitam	240
21.22	Board International	241
21.23	Booz Allen Hamilton	242
21.24	Capgemini	244
21.25	Cellwize	246
21.26	Centrifuge Systems	247
21.27	CenturyLink	248
21.28	Chartio	249
21.29	Cisco Systems	250
21.30	ClearStory Data	252
21.31	Cloudera	253
21.32	Comptel	255
21.33	Concurrent	257
21.34	Contexti	258
21.35	Couchbase	259
21.36	CSC (Computer Science Corporation)	261
21.37	DataHero	262
21.38	Datameer	263
21.39	DataRPM	264
21.40	DataStax	265
21.41	Datawatch Corporation	266
21.42	DDN (DataDirect Network)	267
21.43	Decisyon	268
21.44	Dell	269
21.45	Deloitte	271
21.46	Denodo Technologies	272
21.47	Digital Reasoning	273
21.48	Dimensional Insight	274
21.49	Domo	275
21.50	Dundas Data Visualization	276
21.51	Eligotech	277
21.52	EMC Corporation	278
21.53	Engineering Group (Engineering Ingegneria Informatica)	279
21.54	eQ Technologic	280
21.55	Facebook	281
21.56	FICO	283
21.57	Fractal Analytics	284
21.58	Fujitsu	285
21.59	Fusion-io	287
21.60	GE (General Electric)	288
21.61	GoodData Corporation	289
21.62	Google	290
21.63	Guavus	291
21.64	HDS (Hitachi Data Systems)	292
21.65	Hortonworks	293
21.66	HPE (Hewlett Packard Enterprise)	294
21.67	IBM	295
21.68	iDashboards	296
21.69	Incorta	297
21.70	InetSoft Technology Corporation	298
21.71	InfiniDB	299
21.72	Infor	301
21.73	Informatica Corporation	302
21.74	Information Builders	303
21.75	Intel	304
21.76	Jedox	305
21.77	Jinfonet Software	306
21.78	Juniper Networks	307
21.79	Knime	308
21.80	Kofax	309
21.81	Kognitio	310
21.82	L-3 Communications	311
21.83	Lavastorm Analytics	312
21.84	Logi Analytics	313
21.85	Looker Data Sciences	314
21.86	LucidWorks	315
21.87	Maana	316
21.88	Manthan Software Services	317
21.89	MapR	318
21.90	MarkLogic	319
21.91	MemSQL	320
21.92	Microsoft	321
21.93	MicroStrategy	323
21.94	MongoDB (formerly 10gen)	324
21.95	Mu Sigma	325
21.96	NTT Data	326
21.97	Neo Technology	327
21.98	NetApp	328
21.99	Nutonian	329
21.100	OpenText Corporation	330
21.101	Opera Solutions	331
21.102	Oracle	332
21.103	Palantir Technologies	333
21.104	Panorama Software	334
21.105	ParStream	335
21.106	Pentaho	336
21.107	Phocas	337
21.108	Pivotal Software	338
21.109	Platfora	339
21.110	Prognoz	340
21.111	PwC	341
21.112	Pyramid Analytics	342
21.113	Qlik	343
21.114	Quantum Corporation	344
21.115	Qubole	345
21.116	Rackspace	346
21.117	RapidMiner	347
21.118	Recorded Future	348
21.119	RJMetrics	349
21.120	Salesforce.com	350
21.121	Sailthru	351
21.122	Salient Management Company	352
21.123	SAP	353
21.124	SAS Institute	354
21.125	SGI	355
21.126	SiSense	356
21.127	Software AG	357
21.128	Splice Machine	358
21.129	Splunk	359
21.130	Sqrrl	360
21.131	Strategy Companion	361
21.132	Supermicro	362
21.133	Syncsort	363
21.134	SynerScope	364
21.135	Tableau Software	365
21.136	Talend	366
21.137	Targit	367
21.138	TCS (Tata Consultancy Services)	368
21.139	Teradata	369
21.140	Think Big Analytics	370
21.141	ThoughtSpot	371
21.142	TIBCO Software	372
21.143	Tidemark	373
21.144	VMware (EMC Subsidiary)	374
21.145	WiPro	375
21.146	Yellowfin International	376
21.147	Zendesk	377
21.148	Zettics	378
21.149	Zoomdata	379
21.150	Zucchetti	380
		
22	Chapter 22: Conclusion & Strategic Recommendations	381
22.1	Big Data Technology: Beyond Data Capture & Analytics	381
22.2	Transforming IT from a Cost Center to a Profit Center	381
22.3	Can Privacy Implications Hinder Success?	382
22.4	Will Regulation have a Negative Impact on Big Data Investments?	382
22.5	Battling Organization & Data Silos	383
22.6	Software vs. Hardware Investments	384
22.7	Vendor Share: Who Leads the Market?	385
22.8	Big Data Driving Wider IT Industry Investments	386
22.9	Assessing the Impact of IoT & M2M	387
22.10	Recommendations	388
22.10.1	Big Data Hardware, Software & Professional Services Providers	388
22.10.2	Enterprises	389
List of Figures

	Figure 1: Reactive vs. Proactive Analytics	48
	Figure 2: Big Data Industry Roadmap	145
	Figure 3: The Big Data Value Chain	148
	Figure 4: Global Big Data Revenue: 2016 - 2030 ($ Million)	158
	Figure 5: Global Big Data Revenue by Submarket: 2016 - 2030 ($ Million)	159
	Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)	160
	Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)	161
	Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2016 - 2030 ($ Million)	162
	Figure 9: Global Big Data SQL Submarket Revenue: 2016 - 2030 ($ Million)	163
	Figure 10: Global Big Data NoSQL Submarket Revenue: 2016 - 2030 ($ Million)	164
	Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2016 - 2030 ($ Million)	165
	Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2016 - 2030 ($ Million)	166
	Figure 13: Global Big Data Professional Services Submarket Revenue: 2016 - 2030 ($ Million)	167
	Figure 14: Global Big Data Revenue by Vertical Market: 2016 - 2030 ($ Million)	168
	Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2016 - 2030 ($ Million)	169
	Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2016 - 2030 ($ Million)	170
	Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2016 - 2030 ($ Million)	171
	Figure 18: Global Big Data Revenue in the Education Sector: 2016 - 2030 ($ Million)	172
	Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2016 - 2030 ($ Million)	173
	Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2016 - 2030 ($ Million)	174
	Figure 21: Global Big Data Revenue in the Insurance Sector: 2016 - 2030 ($ Million)	175
	Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2016 - 2030 ($ Million)	176
	Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2016 - 2030 ($ Million)	177
	Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2016 - 2030 ($ Million)	178
	Figure 25: Global Big Data Revenue in the Public Services Sector: 2016 - 2030 ($ Million)	179
	Figure 26: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2016 - 2030 ($ Million)	180
	Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2016 - 2030 ($ Million)	181
	Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2016 - 2030 ($ Million)	182
	Figure 29: Global Big Data Revenue in Other Vertical Sectors: 2016 - 2030 ($ Million)	183
	Figure 30: Big Data Revenue by Region: 2016 - 2030 ($ Million)	184
	Figure 31: Asia Pacific Big Data Revenue: 2016 - 2030 ($ Million)	185
	Figure 32: Asia Pacific Big Data Revenue by Country: 2016 - 2030 ($ Million)	185
	Figure 33: Australia Big Data Revenue: 2016 - 2030 ($ Million)	186
	Figure 34: China Big Data Revenue: 2016 - 2030 ($ Million)	186
	Figure 35: India Big Data Revenue: 2016 - 2030 ($ Million)	187
	Figure 36: Indonesia Big Data Revenue: 2016 - 2030 ($ Million)	187
	Figure 37: Japan Big Data Revenue: 2016 - 2030 ($ Million)	188
	Figure 38: Malaysia Big Data Revenue: 2016 - 2030 ($ Million)	188
	Figure 39: Pakistan Big Data Revenue: 2016 - 2030 ($ Million)	189
	Figure 40: Philippines Big Data Revenue: 2016 - 2030 ($ Million)	189
	Figure 41: Singapore Big Data Revenue: 2016 - 2030 ($ Million)	190
	Figure 42: South Korea Big Data Revenue: 2016 - 2030 ($ Million)	190
	Figure 43: Taiwan Big Data Revenue: 2016 - 2030 ($ Million)	191
	Figure 44: Thailand Big Data Revenue: 2016 - 2030 ($ Million)	191
	Figure 45: Big Data Revenue in the Rest of Asia Pacific: 2016 - 2030 ($ Million)	192
	Figure 46: Eastern Europe Big Data Revenue: 2016 - 2030 ($ Million)	193
	Figure 47: Eastern Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)	193
	Figure 48: Czech Republic Big Data Revenue: 2016 - 2030 ($ Million)	194
	Figure 49: Poland Big Data Revenue: 2016 - 2030 ($ Million)	194
	Figure 50: Russia Big Data Revenue: 2016 - 2030 ($ Million)	195
	Figure 51: Big Data Revenue in the Rest of Eastern Europe: 2016 - 2030 ($ Million)	195
	Figure 52: Latin & Central America Big Data Revenue: 2016 - 2030 ($ Million)	196
	Figure 53: Latin & Central America Big Data Revenue by Country: 2016 - 2030 ($ Million)	196
	Figure 54: Argentina Big Data Revenue: 2016 - 2030 ($ Million)	197
	Figure 55: Brazil Big Data Revenue: 2016 - 2030 ($ Million)	197
	Figure 56: Mexico Big Data Revenue: 2016 - 2030 ($ Million)	198
	Figure 57: Big Data Revenue in the Rest of Latin & Central America: 2016 - 2030 ($ Million)	198
	Figure 58: Middle East & Africa Big Data Revenue: 2016 - 2030 ($ Million)	199
	Figure 59: Middle East & Africa Big Data Revenue by Country: 2016 - 2030 ($ Million)	199
	Figure 60: Israel Big Data Revenue: 2016 - 2030 ($ Million)	200
	Figure 61: Qatar Big Data Revenue: 2016 - 2030 ($ Million)	200
	Figure 62: Saudi Arabia Big Data Revenue: 2016 - 2030 ($ Million)	201
	Figure 63: South Africa Big Data Revenue: 2016 - 2030 ($ Million)	201
	Figure 64: UAE Big Data Revenue: 2016 - 2030 ($ Million)	202
	Figure 65: Big Data Revenue in the Rest of the Middle East & Africa: 2016 - 2030 ($ Million)	202
	Figure 66: North America Big Data Revenue: 2016 - 2030 ($ Million)	203
	Figure 67: North America Big Data Revenue by Country: 2016 - 2030 ($ Million)	203
	Figure 68: Canada Big Data Revenue: 2016 - 2030 ($ Million)	204
	Figure 69: USA Big Data Revenue: 2016 - 2030 ($ Million)	204
	Figure 70: Western Europe Big Data Revenue: 2016 - 2030 ($ Million)	205
	Figure 71: Western Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)	205
	Figure 72: Denmark Big Data Revenue: 2016 - 2030 ($ Million)	206
	Figure 73: Finland Big Data Revenue: 2016 - 2030 ($ Million)	206
	Figure 74: France Big Data Revenue: 2016 - 2030 ($ Million)	207
	Figure 75: Germany Big Data Revenue: 2016 - 2030 ($ Million)	207
	Figure 76: Italy Big Data Revenue: 2016 - 2030 ($ Million)	208
	Figure 77: Netherlands Big Data Revenue: 2016 - 2030 ($ Million)	208
	Figure 78: Norway Big Data Revenue: 2016 - 2030 ($ Million)	209
	Figure 79: Spain Big Data Revenue: 2016 - 2030 ($ Million)	209
	Figure 80: Sweden Big Data Revenue: 2016 - 2030 ($ Million)	210
	Figure 81: UK Big Data Revenue: 2016 - 2030 ($ Million)	210
	Figure 82: Big Data Revenue in the Rest of Western Europe: 2016 - 2030 ($ Million)	211
	Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services: 2016 – 2030 ($ Million)	385
	Figure 84: Big Data Vendor Market Share (%)	386
	Figure 85: Global IT Expenditure Driven by Big Data Investments: 2016 - 2030 ($ Million)	387
	Figure 86: Global M2M Connections by Access Technology: 2016 – 2030 (Millions)	388 



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