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

May 24, 2015 | 351 Pages | SNS Research | Technology & Media

"Big Data" originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.

Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for nearly $40 Billion in 2015 alone. These investments are further expected to grow at a CAGR of 14% over the next 5 years.

The “Big Data Market: 2015 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2015 through to 2030. Historical figures are also presented for 2010, 2011, 2012, 2013 and 2014. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

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 140 Big Data ecosystem players
 - Strategic recommendations for Big Data hardware, software and professional services vendors and enterprises
 - Market analysis and forecasts from 2015 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 & 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, 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 2015, Big Data vendors will pocket nearly $40 Billion from hardware, software and professional services revenues
 - Big Data investments are further expected to grow at a CAGR of 14% over the next 5 years, eventually accounting for nearly $80 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 nearly $8 Billion
 Table of Contents
1	Chapter 1: Introduction	18
1.1	Executive Summary	18
1.2	Topics Covered	20
1.3	Historical Revenue & Forecast Segmentation	21
1.4	Key Questions Answered	23
1.5	Key Findings	24
1.6	Methodology	25
1.7	Target Audience	26
1.8	Companies & Organizations Mentioned	27
		
2	Chapter 2: An Overview of Big Data	31
2.1	What is Big Data?	31
2.2	Key Approaches to Big Data Processing	31
2.2.1	Hadoop	32
2.2.2	NoSQL	33
2.2.3	MPAD (Massively Parallel Analytic Databases)	33
2.2.4	In-memory Processing	34
2.2.5	Stream Processing Technologies	34
2.2.6	Spark	35
2.2.7	Other Databases & Analytic Technologies	35
2.3	Key Characteristics of Big Data	36
2.3.1	Volume	36
2.3.2	Velocity	36
2.3.3	Variety	36
2.3.4	Value	37
2.4	Market Growth Drivers	38
2.4.1	Awareness of Benefits	38
2.4.2	Maturation of Big Data Platforms	38
2.4.3	Continued Investments by Web Giants, Governments & Enterprises	39
2.4.4	Growth of Data Volume, Velocity & Variety	39
2.4.5	Vendor Commitments & Partnerships	39
2.4.6	Technology Trends Lowering Entry Barriers	40
2.5	Market Barriers	40
2.5.1	Lack of Analytic Specialists	40
2.5.2	Uncertain Big Data Strategies	40
2.5.3	Organizational Resistance to Big Data Adoption	41
2.5.4	Technical Challenges: Scalability & Maintenance	41
2.5.5	Security & Privacy Concerns	41
		
3	Chapter 3: Vertical Opportunities & Use Cases for Big Data	43
3.1	Automotive, Aerospace & Transportation	43
3.1.1	Predictive Warranty Analysis	43
3.1.2	Predictive Aircraft Maintenance & Fuel Optimization	44
3.1.3	Air Traffic Control	44
3.1.4	Transport Fleet Optimization	44
3.2	Banking & Securities	46
3.2.1	Customer Retention & Personalized Product Offering	46
3.2.2	Risk Management	46
3.2.3	Fraud Detection	46
3.2.4	Credit Scoring	47
3.3	Defense & Intelligence	48
3.3.1	Intelligence Gathering	48
3.3.2	Energy Saving Opportunities in the Battlefield	48
3.3.3	Preventing Injuries on the Battlefield	49
3.4	Education	50
3.4.1	Information Integration	50
3.4.2	Identifying Learning Patterns	50
3.4.3	Enabling Student-Directed Learning	50
3.5	Healthcare & Pharmaceutical	52
3.5.1	Managing Population Health Efficiently	52
3.5.2	Improving Patient Care with Medical Data Analytics	52
3.5.3	Improving Clinical Development & Trials	52
3.5.4	Improving Time to Market	53
3.6	Smart Cities & Intelligent Buildings	54
3.6.1	Energy Optimization & Fault Detection	54
3.6.2	Intelligent Building Analytics	54
3.6.3	Urban Transportation Management	55
3.6.4	Optimizing Energy Production	55
3.6.5	Water Management	55
3.6.6	Urban Waste Management	55
3.7	Insurance	57
3.7.1	Claims Fraud Mitigation	57
3.7.2	Customer Retention & Profiling	57
3.7.3	Risk Management	58
3.8	Manufacturing & Natural Resources	59
3.8.1	Asset Maintenance & Downtime Reduction	59
3.8.2	Quality & Environmental Impact Control	59
3.8.3	Optimized Supply Chain	59
3.8.4	Exploration & Identification of Wells & Mines	60
3.8.5	Maximizing the Potential of Drilling	60
3.8.6	Production Optimization	60
3.9	Web, Media & Entertainment	61
3.9.1	Audience & Advertising Optimization	61
3.9.2	Channel Optimization	61
3.9.3	Recommendation Engines	61
3.9.4	Optimized Search	62
3.9.5	Live Sports Event Analytics	62
3.9.6	Outsourcing Big Data Analytics to Other Verticals	62
3.10	Public Safety & Homeland Security	63
3.10.1	Cyber Crime Mitigation	63
3.10.2	Crime Prediction Analytics	63
3.10.3	Video Analytics & Situational Awareness	63
3.11	Public Services	65
3.11.1	Public Sentiment Analysis	65
3.11.2	Fraud Detection & Prevention	65
3.11.3	Economic Analysis	65
3.12	Retail & Hospitality	66
3.12.1	Customer Sentiment Analysis	66
3.12.2	Customer & Branch Segmentation	66
3.12.3	Price Optimization	66
3.12.4	Personalized Marketing	67
3.12.5	Optimized Supply Chain	67
3.13	Telecommunications	68
3.13.1	Network Performance & Coverage Optimization	68
3.13.2	Customer Churn Prevention	68
3.13.3	Personalized Marketing	68
3.13.4	Location Based Services	69
3.13.5	Fraud Detection	69
3.14	Utilities & Energy	70
3.14.1	Customer Retention	70
3.14.2	Forecasting Energy	70
3.14.3	Billing Analytics	70
3.14.4	Predictive Maintenance	70
3.14.5	Turbine Placement Optimization	71
3.15	Wholesale Trade	72
3.15.1	In-field Sales Analytics	72
3.15.2	Monitoring the Supply Chain	72
		
4	Chapter 4: Big Data Industry Roadmap & Value Chain	73
4.1	Big Data Industry Roadmap	73
4.1.1	2010 – 2013: Initial Hype and the Rise of Analytics	73
4.1.2	2014 – 2017: Emergence of SaaS Based Big Data Solutions	74
4.1.3	2018 – 2020: Growing Adoption of Scalable Machine Learning	75
4.1.4	2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics	75
4.2	The Big Data Value Chain	76
4.2.1	Hardware Providers	76
4.2.1.1	Storage & Compute Infrastructure Providers	76
4.2.1.2	Networking Infrastructure Providers	77
4.2.2	Software Providers	78
4.2.2.1	Hadoop & Infrastructure Software Providers	78
4.2.2.2	SQL & NoSQL Providers	78
4.2.2.3	Analytic Platform & Application Software Providers	78
4.2.2.4	Cloud Platform Providers	79
4.2.3	Professional Services Providers	79
4.2.4	End-to-End Solution Providers	79
4.2.5	Vertical Enterprises	79
		
5	Chapter 5: Big Data Analytics	80
5.1	What are Big Data Analytics?	80
5.2	The Importance of Analytics	80
5.3	Reactive vs. Proactive Analytics	81
5.4	Customer vs. Operational Analytics	82
5.5	Technology & Implementation Approaches	82
5.5.1	Grid Computing	82
5.5.2	In-Database Processing	83
5.5.3	In-Memory Analytics	83
5.5.4	Machine Learning & Data Mining	83
5.5.5	Predictive Analytics	84
5.5.6	NLP (Natural Language Processing)	84
5.5.7	Text Analytics	85
5.5.8	Visual Analytics	86
5.5.9	Social Media, IT & Telco Network Analytics	86
5.6	Vertical Market Case Studies	87
5.6.1	Amazon – Delivering Cloud Based Big Data Analytics	87
5.6.2	Facebook – Using Analytics to Monetize Users with Advertising	87
5.6.3	WIND Mobile – Using Analytics to Monitor Video Quality	88
5.6.4	Coriant Analytics Services – SaaS Based Big Data Analytics for Telcos	88
5.6.5	Boeing – Analytics for the Battlefield	89
5.6.6	The Walt Disney Company – Utilizing Big Data and Analytics in Theme Parks	89
		
6	Chapter 6: Standardization & Regulatory Initiatives	91
6.1	CSCC (Cloud Standards Customer Council) – Big Data Working Group	91
6.2	NIST (National Institute of Standards and Technology) – Big Data Working Group	92
6.3	OASIS –Technical Committees	93
6.4	ODaF (Open Data Foundation)	94
6.5	Open Data Center Alliance	94
6.6	CSA (Cloud Security Alliance) – Big Data Working Group	95
6.7	ITU (International Telecommunications Union)	96
6.8	ISO (International Organization for Standardization) and Others	96
		
7	Chapter 7: Market Analysis & Forecasts	97
7.1	Global Outlook of the Big Data Market	97
7.2	Submarket Segmentation	98
7.2.1	Storage and Compute Infrastructure	99
7.2.2	Networking Infrastructure	100
7.2.3	Hadoop & Infrastructure Software	101
7.2.4	SQL	102
7.2.5	NoSQL	103
7.2.6	Analytic Platforms & Applications	104
7.2.7	Cloud Platforms	105
7.2.8	Professional Services	106
7.3	Vertical Market Segmentation	107
7.3.1	Automotive, Aerospace & Transportation	108
7.3.2	Banking & Securities	109
7.3.3	Defense & Intelligence	110
7.3.4	Education	111
7.3.5	Healthcare & Pharmaceutical	112
7.3.6	Smart Cities & Intelligent Buildings	113
7.3.7	Insurance	114
7.3.8	Manufacturing & Natural Resources	115
7.3.9	Media & Entertainment	116
7.3.10	Public Safety & Homeland Security	117
7.3.11	Public Services	118
7.3.12	Retail & Hospitality	119
7.3.13	Telecommunications	120
7.3.14	Utilities & Energy	121
7.3.15	Wholesale Trade	122
7.3.16	Other Sectors	123
7.4	Regional Outlook	124
7.5	Asia Pacific	125
7.5.1	Country Level Segmentation	126
7.5.2	Australia	127
7.5.3	China	128
7.5.4	India	129
7.5.5	Indonesia	130
7.5.6	Japan	131
7.5.7	Malaysia	132
7.5.8	Pakistan	133
7.5.9	Philippines	134
7.5.10	Singapore	135
7.5.11	South Korea	136
7.5.12	Taiwan	137
7.5.13	Thailand	138
7.5.14	Rest of Asia Pacific	139
7.6	Eastern Europe	140
7.6.1	Country Level Segmentation	141
7.6.2	Czech Republic	142
7.6.3	Poland	143
7.6.4	Russia	144
7.6.5	Rest of Eastern Europe	145
7.7	Latin & Central America	146
7.7.1	Country Level Segmentation	147
7.7.2	Argentina	148
7.7.3	Brazil	149
7.7.4	Mexico	150
7.7.5	Rest of Latin & Central America	151
7.8	Middle East & Africa	152
7.8.1	Country Level Segmentation	153
7.8.2	Israel	154
7.8.3	Qatar	155
7.8.4	Saudi Arabia	156
7.8.5	South Africa	157
7.8.6	UAE	158
7.8.7	Rest of the Middle East & Africa	159
7.9	North America	160
7.9.1	Country Level Segmentation	161
7.9.2	Canada	162
7.9.3	USA	163
7.10	Western Europe	164
7.10.1	Country Level Segmentation	165
7.10.2	Denmark	166
7.10.3	Finland	167
7.10.4	France	168
7.10.5	Germany	169
7.10.6	Italy	170
7.10.7	Netherlands	171
7.10.8	Norway	172
7.10.9	Spain	173
7.10.10	Sweden	174
7.10.11	UK	175
7.10.12	Rest of Western Europe	176
		
8	Chapter 8: Vendor Landscape	177
8.1	1010data	177
8.2	Accenture	179
8.3	Actian Corporation	181
8.4	Actuate Corporation	183
8.5	Adaptive Insights	185
8.6	Advizor Solutions	186
8.7	AeroSpike	187
8.8	AFS Technologies	189
8.9	Alpine Data Labs	190
8.10	Alteryx	191
8.11	Altiscale	193
8.12	Antivia	194
8.13	Arcplan	195
8.14	Attivio	196
8.15	Automated Insights	198
8.16	AWS (Amazon Web Services)	199
8.17	Ayasdi	201
8.18	Basho	202
8.19	BeyondCore	204
8.20	Birst	205
8.21	Bitam	206
8.22	Board International	207
8.23	Booz Allen Hamilton	208
8.24	Capgemini	210
8.25	Cellwize	212
8.26	Centrifuge Systems	213
8.27	CenturyLink	214
8.28	Chartio	215
8.29	Cisco Systems	216
8.30	ClearStory Data	218
8.31	Cloudera	219
8.32	Comptel	221
8.33	Concurrent	223
8.34	Contexti	224
8.35	Couchbase	225
8.36	CSC (Computer Science Corporation)	227
8.37	DataHero	228
8.38	Datameer	229
8.39	DataRPM	230
8.40	DataStax	231
8.41	Datawatch Corporation	232
8.42	DDN (DataDirect Network)	233
8.43	Decisyon	234
8.44	Dell	235
8.45	Deloitte	237
8.46	Denodo Technologies	238
8.47	Digital Reasoning	239
8.48	Dimensional Insight	240
8.49	Domo	241
8.50	Dundas Data Visualization	242
8.51	Eligotech	243
8.52	EMC Corporation	244
8.53	Engineering Group (Engineering Ingegneria Informatica)	245
8.54	eQ Technologic	246
8.55	Facebook	247
8.56	FICO	249
8.57	Fractal Analytics	250
8.58	Fujitsu	251
8.59	Fusion-io	253
8.60	GE (General Electric)	254
8.61	GoodData Corporation	255
8.62	Google	256
8.63	Guavus	257
8.64	HDS (Hitachi Data Systems)	258
8.65	Hortonworks	259
8.66	HP	260
8.67	IBM	261
8.68	iDashboards	262
8.69	Incorta	263
8.70	InetSoft Technology Corporation	264
8.71	InfiniDB	265
8.72	Infor	267
8.73	Informatica Corporation	268
8.74	Information Builders	269
8.75	Intel	270
8.76	Jedox	271
8.77	Jinfonet Software	272
8.78	Juniper Networks	273
8.79	Knime	274
8.80	Kofax	275
8.81	Kognitio	276
8.82	L-3 Communications	277
8.83	Lavastorm Analytics	278
8.84	Logi Analytics	279
8.85	Looker Data Sciences	280
8.86	LucidWorks	281
8.87	Manthan Software Services	282
8.88	MapR	283
8.89	MarkLogic	284
8.90	MemSQL	285
8.91	Microsoft	286
8.92	MicroStrategy	287
8.93	MongoDB (formerly 10gen)	288
8.94	Mu Sigma	289
8.95	NTT Data	290
8.96	Neo Technology	291
8.97	NetApp	292
8.98	OpenText Corporation	293
8.99	Opera Solutions	294
8.100	Oracle	295
8.101	Palantir Technologies	296
8.102	Panorama Software	297
8.103	ParStream	298
8.104	Pentaho	299
8.105	Phocas	300
8.106	Pivotal Software	301
8.107	Platfora	302
8.108	Prognoz	303
8.109	PwC	304
8.110	Pyramid Analytics	305
8.111	Qlik	306
8.112	Quantum Corporation	307
8.113	Qubole	308
8.114	Rackspace	309
8.115	RainStor	310
8.116	RapidMiner	311
8.117	Recorded Future	312
8.118	Revolution Analytics	313
8.119	RJMetrics	314
8.120	Salesforce.com	315
8.121	Sailthru	316
8.122	Salient Management Company	317
8.123	SAP	318
8.124	SAS Institute	319
8.125	SGI	320
8.126	SiSense	321
8.127	Software AG	322
8.128	Splice Machine	323
8.129	Splunk	324
8.130	Sqrrl	325
8.131	Strategy Companion	326
8.132	Supermicro	327
8.133	SynerScope	328
8.134	Tableau Software	329
8.135	Talend	330
8.136	Targit	331
8.137	TCS (Tata Consultancy Services)	332
8.138	Teradata	333
8.139	Think Big Analytics	334
8.140	ThoughtSpot	335
8.141	TIBCO Software	336
8.142	Tidemark	337
8.143	VMware (EMC Subsidiary)	338
8.144	WiPro	339
8.145	Yellowfin International	340
8.146	Zettics	341
8.147	Zoomdata	342
8.148	Zucchetti	343
		
9	Chapter 9: Conclusion & Strategic Recommendations	344
9.1	Big Data Technology: Beyond Data Capture & Analytics	344
9.2	Transforming IT from a Cost Center to a Profit Center	344
9.3	Can Privacy Implications Hinder Success?	345
9.4	Will Regulation have a Negative Impact on Big Data Investments?	345
9.5	Battling Organization & Data Silos	346
9.6	Software vs. Hardware Investments	347
9.7	Vendor Share: Who Leads the Market?	348
9.8	Big Data Driving Wider IT Industry Investments	349
9.9	Assessing the Impact of IoT & M2M	350
9.10	Recommendations	351
9.10.1	Big Data Hardware, Software & Professional Services Providers	351
9.10.2	Enterprises	352
		

List of Figures		
	Figure 1: Big Data Industry Roadmap	73
	Figure 2: The Big Data Value Chain	76
	Figure 3: Reactive vs. Proactive Analytics	81
	Figure 4: Global Big Data Revenue: 2015 - 2030 ($ Million)	97
	Figure 5: Global Big Data Revenue by Submarket: 2015 - 2030 ($ Million)	98
	Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2015 - 2030 ($ Million)	99
	Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2015 - 2030 ($ Million)	100
	Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2015 - 2030 ($ Million)	101
	Figure 9: Global Big Data SQL Submarket Revenue: 2015 - 2030 ($ Million)	102
	Figure 10: Global Big Data NoSQL Submarket Revenue: 2015 - 2030 ($ Million)	103
	Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2015 - 2030 ($ Million)	104
	Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2015 - 2030 ($ Million)	105
	Figure 13: Global Big Data Professional Services Submarket Revenue: 2015 - 2030 ($ Million)	106
	Figure 14: Global Big Data Revenue by Vertical Market: 2015 - 2030 ($ Million)	107
	Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2015 - 2030 ($ Million)	108
	Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2015 - 2030 ($ Million)	109
	Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2015 - 2030 ($ Million)	110
	Figure 18: Global Big Data Revenue in the Education Sector: 2015 - 2030 ($ Million)	111
	Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2015 - 2030 ($ Million)	112
	Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2015 - 2030 ($ Million)	113
	Figure 21: Global Big Data Revenue in the Insurance Sector: 2015 - 2030 ($ Million)	114
	Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2015 - 2030 ($ Million)	115
	Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2015 - 2030 ($ Million)	116
	Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2015 - 2030 ($ Million)	117
	Figure 25: Global Big Data Revenue in the Public Services Sector: 2015 - 2030 ($ Million)	118
	Figure 26: Global Big Data Revenue in the Retail & Hospitality Sector: 2015 - 2030 ($ Million)	119
	Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2015 - 2030 ($ Million)	120
	Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2015 - 2030 ($ Million)	121
	Figure 29: Global Big Data Revenue in the Wholesale Trade Sector: 2015 - 2030 ($ Million)	122
	Figure 30: Global Big Data Revenue in Other Vertical Sectors: 2015 - 2030 ($ Million)	123
	Figure 31: Big Data Revenue by Region: 2015 - 2030 ($ Million)	124
	Figure 32: Asia Pacific Big Data Revenue: 2015 - 2030 ($ Million)	125
	Figure 33: Asia Pacific Big Data Revenue by Country: 2015 - 2030 ($ Million)	126
	Figure 34: Australia Big Data Revenue: 2015 - 2030 ($ Million)	127
	Figure 35: China Big Data Revenue: 2015 - 2030 ($ Million)	128
	Figure 36: India Big Data Revenue: 2015 - 2030 ($ Million)	129
	Figure 37: Indonesia Big Data Revenue: 2015 - 2030 ($ Million)	130
	Figure 38: Japan Big Data Revenue: 2015 - 2030 ($ Million)	131
	Figure 39: Malaysia Big Data Revenue: 2015 - 2030 ($ Million)	132
	Figure 40: Pakistan Big Data Revenue: 2015 - 2030 ($ Million)	133
	Figure 41: Philippines Big Data Revenue: 2015 - 2030 ($ Million)	134
	Figure 42: Singapore Big Data Revenue: 2015 - 2030 ($ Million)	135
	Figure 43: South Korea Big Data Revenue: 2015 - 2030 ($ Million)	136
	Figure 44: Taiwan Big Data Revenue: 2015 - 2030 ($ Million)	137
	Figure 45: Thailand Big Data Revenue: 2015 - 2030 ($ Million)	138
	Figure 46: Big Data Revenue in the Rest of Asia Pacific: 2015 - 2030 ($ Million)	139
	Figure 47: Eastern Europe Big Data Revenue: 2015 - 2030 ($ Million)	140
	Figure 48: Eastern Europe Big Data Revenue by Country: 2015 - 2030 ($ Million)	141
	Figure 49: Czech Republic Big Data Revenue: 2015 - 2030 ($ Million)	142
	Figure 50: Poland Big Data Revenue: 2015 - 2030 ($ Million)	143
	Figure 51: Russia Big Data Revenue: 2015 - 2030 ($ Million)	144
	Figure 52: Big Data Revenue in the Rest of Eastern Europe: 2015 - 2030 ($ Million)	145
	Figure 53: Latin & Central America Big Data Revenue: 2015 - 2030 ($ Million)	146
	Figure 54: Latin & Central America Big Data Revenue by Country: 2015 - 2030 ($ Million)	147
	Figure 55: Argentina Big Data Revenue: 2015 - 2030 ($ Million)	148
	Figure 56: Brazil Big Data Revenue: 2015 - 2030 ($ Million)	149
	Figure 57: Mexico Big Data Revenue: 2015 - 2030 ($ Million)	150
	Figure 58: Big Data Revenue in the Rest of Latin & Central America: 2015 - 2030 ($ Million)	151
	Figure 59: Middle East & Africa Big Data Revenue: 2015 - 2030 ($ Million)	152
	Figure 60: Middle East & Africa Big Data Revenue by Country: 2015 - 2030 ($ Million)	153
	Figure 61: Israel Big Data Revenue: 2015 - 2030 ($ Million)	154
	Figure 62: Qatar Big Data Revenue: 2015 - 2030 ($ Million)	155
	Figure 63: Saudi Arabia Big Data Revenue: 2015 - 2030 ($ Million)	156
	Figure 64: South Africa Big Data Revenue: 2015 - 2030 ($ Million)	157
	Figure 65: UAE Big Data Revenue: 2015 - 2030 ($ Million)	158
	Figure 66: Big Data Revenue in the Rest of the Middle East & Africa: 2015 - 2030 ($ Million)	159
	Figure 67: North America Big Data Revenue: 2015 - 2030 ($ Million)	160
	Figure 68: North America Big Data Revenue by Country: 2015 - 2030 ($ Million)	161
	Figure 69: Canada Big Data Revenue: 2015 - 2030 ($ Million)	162
	Figure 70: USA Big Data Revenue: 2015 - 2030 ($ Million)	163
	Figure 71: Western Europe Big Data Revenue: 2015 - 2030 ($ Million)	164
	Figure 72: Western Europe Big Data Revenue by Country: 2015 - 2030 ($ Million)	165
	Figure 73: Denmark Big Data Revenue: 2015 - 2030 ($ Million)	166
	Figure 74: Finland Big Data Revenue: 2015 - 2030 ($ Million)	167
	Figure 75: France Big Data Revenue: 2015 - 2030 ($ Million)	168
	Figure 76: Germany Big Data Revenue: 2015 - 2030 ($ Million)	169
	Figure 77: Italy Big Data Revenue: 2015 - 2030 ($ Million)	170
	Figure 78: Netherlands Big Data Revenue: 2015 - 2030 ($ Million)	171
	Figure 79: Norway Big Data Revenue: 2015 - 2030 ($ Million)	172
	Figure 80: Spain Big Data Revenue: 2015 - 2030 ($ Million)	173
	Figure 81: Sweden Big Data Revenue: 2015 - 2030 ($ Million)	174
	Figure 82: UK Big Data Revenue: 2015 - 2030 ($ Million)	175
	Figure 83: Big Data Revenue in the Rest of Western Europe: 2015 - 2030 ($ Million)	176
	Figure 84: Global Big Data Revenue by Hardware, Software & Professional Services ($ Million): 2015 - 2030	347
	Figure 85: Big Data Vendor Market Share (%)	348
	Figure 86: Global IT Expenditure Driven by Big Data Investments: 2015 - 2030 ($ Million)	349
	Figure 87: Global M2M Connections by Access Technology (Millions): 2015 - 2030	350 



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