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

Published: Apr, 2017 | Pages: 498 | Publisher: SNS Research
Industry: ICT | 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 $57 Billion in 2017 alone. These investments are further expected to grow at a CAGR of approximately 10% over the next three years.

The “Big Data Market: 2017 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2017 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.

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

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 240 Big Data ecosystem players
 - Strategic recommendations for Big Data hardware, software and professional services vendors, and enterprises
 - Market analysis and forecasts from 2017 till 2030

Forecast Segmentation
Market forecasts 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 2017, Big Data vendors will pocket over $57 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 10% over the next three years, eventually accounting for over $76 Billion by the end of 2020.
 - As part of wider plans to revitalize their economies, countries across the world are incorporating legislative initiatives to capitalize on Big Data. For example, the Japanese government is engaged in developing intellectual property protection and dispute resolution frameworks for Big Data assets, in a bid to encourage data sharing and accelerate the development of domestic industries.
 - By the end of 2017, SNS Research estimates that as much as 30% of all Big Data workloads will be processed via cloud services as enterprises seek to avoid large-scale infrastructure investments and security issues associated with on-premise implementations.
 - The vendor arena is continuing to consolidate with several prominent M&A deals such as computer hardware giant Dell's $60 Billion merger with data storage specialist EMC.

List of Companies Mentioned

1010data
Absolutdata
Accenture
Actian Corporation
Actuate Corporation
Adaptive Insights
Adobe Systems
Advizor Solutions
AeroSpike
AFS Technologies
Airbus Group
Alameda County Social Services Agency
Alation
Algorithmia
Alluxio
Alphabet
Alpine Data
Alteryx
Altiscale
Amazon.com
Ambulance Victoria
AMD (Advanced Micro Devices)
Amgen
Amino
ANSI (American National Standards Institute)
Antivia
Apixio
Arcadia Data
Arimo
ARM
ASF (Apache Software Foundation)
AstraZeneca
AT&T
Atos
AtScale
Attivio
Attunity
Automated Insights
AWS (Amazon Web Services)
Axiomatics
Ayasdi
BAE Systems
Baidu
Barclays Bank
Basho Technologies
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Cloud Analytics
Big Panda
Bina Technologies
Biogen
Birst
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BMW
BOARD International
Boeing
Booz Allen Hamilton
Boxever
British Gas
Brocade
BT Group
CACI International
Cambridge Semantics
Capgemini
Capital One Financial Corporation
Cazena
CBA (Commonwealth Bank of Australia)
CBP (U.S. Customs and Border Protection)
Centrifuge Systems
CenturyLink
Chartio
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Clustrix
CognitiveScale
Collibra
Concurrent Computer Corporation
Confluent
Constant Contact
Contexti
Continuum Analytics
Coriant
Couchbase
Credit Agricole Group
CrowdFlower
CSA (Cloud Security Alliance)
CSCC (Cloud Standards Customer Council)
Databricks
DataGravity
Dataiku
Datameer
DataRobot
DataScience
DataStax
DataTorrent
Datawatch Corporation
Datos IO
DDN (DataDirect Networks)
Decisyon
Dell EMC
Dell Technologies
Deloitte
Delphi Automotive
Demandbase
Denodo Technologies
Denso Corporation
DGSE (General Directorate for External Security, France)
DHS (U.S. Department of Homeland Security)
Digital Reasoning Systems
Dimensional Insight
DMG  (Data Mining Group)
Dolphin Enterprise Solutions Corporation
Domino Data Lab
Domo
Dow Chemical Company
DriveScale
DT (Deutsche Telekom)
Dubai Police
Dundas Data Visualization
DXC Technology
eBay
Edith Cowen University
Eligotech
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB
eQ Technologic
Ericsson
EXASOL
Facebook
FDNY (Fire Department of the City of New York)
FICO (Fair Isaac Corporation)
Ford Motor Company
Fractal Analytics
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Glasgow City Council
Glassbeam
GoodData Corporation
Google
Greenwave Systems
GridGain Systems
GSK (GlaxoSmithKline)
Guavus
H2O.ai
HDS (Hitachi Data Systems)
Hedvig
Hitachi
Hortonworks
HPE (Hewlett Packard Enterprise)
HSBC Group
Huawei
IBM Corporation
ICE (U.S. Immigration and Customs Enforcement)
iDashboards
IEC (International Electrotechnical Commission)
IEEE (Institute of Electrical and Electronics Engineers)
Impetus Technologies
INCITS (InterNational Committee for Information Technology Standards)
Incorta
InetSoft Technology Corporation
Infer
Infor
Informatica Corporation
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
ISO (International Organization for Standardization)
ITU (International Telecommunications Union)
Jedox
Jethro
Jinfonet Software
JJ Food Service
JPMorgan Chase & Co.
Juniper Networks
Kaiser Permanente
KALEAO
Keen IO
Kinetica
KNIME
Kofax
Kognitio
Kyvos Insights
Lavastorm
Lexalytics
Lexmark International
Linux Foundation
Logi Analytics
Longview Solutions
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Magento Commerce
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Marriott International
Mathworks
Memphis Police Department
MemSQL
Mercer
METI (Ministry of Economy, Trade and Industry, Japan)
Metric Insights
Michelin
Microsoft Corporation
MicroStrategy
Ministry of State Security, China
Minitab
MongoDB
Mu Sigma
NASA (U.S. National Aeronautics and Space Administration)
Neo Technology
NetApp
Netflix
Neustar
New York State Department of Taxation and Finance
NextBio
NFL (U.S. National Football League)
Nimbix
NIST (U.S. National Institute of Standards and Technology)
Nokia
Northwest Analytics
Nottingham Trent University
Novartis
NSA (U.S. National Security Agency)
NTT Data Corporation
NTT Group
Numerify
NuoDB
Nutonian
NVIDIA Corporation
OASIS (Organization for the Advancement of Structured Information Standards)
Oblong Industries
ODaF (Open Data Foundation)
ODCA (Open Data Center Alliance)
ODPi (Open Ecosystem of Big Data)
Ofcom
OGC (Open Geospatial Consortium)
Oncor Electric Delivery Company
ONS (Office for National Statistics, United Kingdom)
OpenText Corporation
Opera Solutions
Optimal Plus
Oracle Corporation
Otonomo
OTP Bank
OVG Real Estate
Palantir Technologies
Panorama Software
Paxata
Pentaho Corporation
Pepperdata
Pfizer
Philips
Phocas Software
Pivotal Software
Predixion Software
Primerica
Procter & Gamble
Prognoz
Progress Software Corporation
Purdue University
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
Qualcomm
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
Roche
Rosenberger
Royal Bank of Canada
Royal Dutch Shell
Royal Navy
RSA Group
RStudio
Ryft Systems
Sailthru
Salesforce.com
Salient Management Company
Samsung Electronics
Samsung Group
Samsung SDS
Sanofi
SAP
SAS Institute
ScaleDB
ScaleOut Software
Schneider Electric
SCIO Health Analytics
Seagate Technology
Siemens
Sinequa
SiSense
SnapLogic
Snowflake Computing
SoftBank Group
Software AG
SpagoBI Labs
Splice Machine
Splunk
Sqrrl
Strategy Companion Corporation
StreamSets
Striim
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
Tableau Software
Talena
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
TEOCO
Teradata Corporation
Tesco
The Weather Company
Thomson Reuters
ThoughtSpot
TIBCO Software
Tidemark
TM Forum
T-Mobile USA
Toshiba Corporation
Toyota Motor Corporation
TPC (Transaction Processing Performance Council)
Trifacta
U.S. Air Force
U.S. Army
U.S. Coast Guard
U.S. Department of Commerce
U.S. Department of Defense
UCB
Unravel Data
USCIS (U.S. Citizenship and Immigration Services)
Valens
Verizon Communications
VMware
Vodafone Group
VoltDB
W3C (World Wide Web Consortium)
Walt Disney Company
Waterline Data
Wavefront
Western Digital Corporation
WiPro
Workday
Xevo
Xplenty
Yellowfin International
Yseop
Zendesk
Zoomdata
Zucchetti
Zurich Insurance Group
 Table of Contents

Chapter 1: Introduction	25
1.1	Executive Summary	25
1.2	Topics Covered	27
1.3	Forecast Segmentation	28
1.4	Key Questions Answered	30
1.5	Key Findings	31
1.6	Methodology	32
1.7	Target Audience	33
1.8	Companies & Organizations Mentioned	34
		
Chapter 2: An Overview of Big Data	39
2.1	What is Big Data?	39
2.2	Key Approaches to Big Data Processing	39
2.2.1	Hadoop	40
2.2.2	NoSQL	42
2.2.3	MPAD (Massively Parallel Analytic Databases)	42
2.2.4	In-Memory Processing	43
2.2.5	Stream Processing Technologies	43
2.2.6	Spark	44
2.2.7	Other Databases & Analytic Technologies	44
2.3	Key Characteristics of Big Data	45
2.3.1	Volume	45
2.3.2	Velocity	45
2.3.3	Variety	45
2.3.4	Value	46
2.4	Market Growth Drivers	47
2.4.1	Awareness of Benefits	47
2.4.2	Maturation of Big Data Platforms	47
2.4.3	Continued Investments by Web Giants, Governments & Enterprises	48
2.4.4	Growth of Data Volume, Velocity & Variety	48
2.4.5	Vendor Commitments & Partnerships	48
2.4.6	Technology Trends Lowering Entry Barriers	49
2.5	Market Barriers	49
2.5.1	Lack of Analytic Specialists	49
2.5.2	Uncertain Big Data Strategies	49
2.5.3	Organizational Resistance to Big Data Adoption	50
2.5.4	Technical Challenges: Scalability & Maintenance	50
2.5.5	Security & Privacy Concerns	50
		
Chapter 3: Big Data Analytics	52
3.1	What are Big Data Analytics?	52
3.2	The Importance of Analytics	52
3.3	Reactive vs. Proactive Analytics	53
3.4	Customer vs. Operational Analytics	54
3.5	Technology & Implementation Approaches	54
3.5.1	Grid Computing	54
3.5.2	In-Database Processing	55
3.5.3	In-Memory Analytics	55
3.5.4	Machine Learning & Data Mining	55
3.5.5	Predictive Analytics	56
3.5.6	NLP (Natural Language Processing)	56
3.5.7	Text Analytics	57
3.5.8	Visual Analytics	58
3.5.9	Social Media, IT & Telco Network Analytics	58
		
Chapter 4: Big Data in Automotive, Aerospace & Transportation	59
4.1	Overview & Investment Potential	59
4.2	Key Applications	59
4.2.1	Autonomous Driving	59
4.2.2	Warranty Analytics for Automotive OEMs	60
4.2.3	Predictive Aircraft Maintenance & Fuel Optimization	60
4.2.4	Air Traffic Control	61
4.2.5	Transport Fleet Optimization	61
4.2.6	UBI (Usage Based Insurance)	62
4.3	Case Studies	62
4.3.1	Delphi Automotive: Monetizing Connected Vehicles with Big Data	62
4.3.2	Boeing: Making Flying More Efficient with Big Data	64
4.3.3	BMW: Eliminating Defects in New Vehicle Models with Big Data	65
4.3.4	Toyota Motor Corporation: Powering Smart Cars with Big Data	66
4.3.5	Ford Motor Company: Making Efficient Transportation Decisions with Big Data	67
		
Chapter 5: Big Data in Banking & Securities	68
5.1	Overview & Investment Potential	68
5.2	Key Applications	68
5.2.1	Customer Retention & Personalized Product Offering	68
5.2.2	Risk Management	69
5.2.3	Fraud Detection	69
5.2.4	Credit Scoring	69
5.3	Case Studies	69
5.3.1	HSBC Group: Avoiding Regulatory Penalties with Big Data	70
5.3.2	JPMorgan Chase & Co.: Improving Business Processes with Big Data	71
5.3.3	OTP Bank: Reducing Loan Defaults with Big Data	72
5.3.4	CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data	73
		
Chapter 6: Big Data in Defense & Intelligence	74
6.1	Overview & Investment Potential	74
6.2	Key Applications	74
6.2.1	Intelligence Gathering	74
6.2.2	Battlefield Analytics	75
6.2.3	Energy Saving Opportunities in the Battlefield	75
6.2.4	Preventing Injuries on the Battlefield	76
6.3	Case Studies	77
6.3.1	U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data	77
6.3.2	Royal Navy: Empowering Submarine Warfare with Big Data	78
6.3.3	NSA (National Security Agency): Capitalizing on Big Data to Detect Threats	79
6.3.4	Ministry of State Security, China: Predictive Policing with Big Data	80
6.3.5	French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data	81
		
Chapter 7: Big Data in Education	82
7.1	Overview & Investment Potential	82
7.2	Key Applications	82
7.2.1	Information Integration	82
7.2.2	Identifying Learning Patterns	83
7.2.3	Enabling Student-Directed Learning	83
7.3	Case Studies	83
7.3.1	Purdue University: Ensuring Successful Higher Education Outcomes with Big Data	84
7.3.2	Nottingham Trent University: Successful Student Outcomes with Big Data	85
7.3.3	Edith Cowen University: Increasing Student Retention with Big Data	86
		
Chapter 8: Big Data in Healthcare & Pharma	87
8.1	Overview & Investment Potential	87
8.2	Key Applications	87
8.2.1	Managing Population Health Efficiently	87
8.2.2	Improving Patient Care with Medical Data Analytics	88
8.2.3	Improving Clinical Development & Trials	88
8.2.4	Drug Development: Improving Time to Market	88
8.3	Case Studies	89
8.3.1	Amino: Healthcare Transparency with Big Data	89
8.3.2	Novartis: Digitizing Healthcare with Big Data	90
8.3.3	GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data	91
8.3.4	Pfizer: Developing Effective and Targeted Therapies with Big Data	92
8.3.5	Roche: Personalizing Healthcare with Big Data	93
8.3.6	Sanofi: Proactive Diabetes Care with Big Data	94
	
Chapter 9: Big Data in Smart Cities & Intelligent Buildings	96
9.1	Overview & Investment Potential	96
9.2	Key Applications	96
9.2.1	Energy Optimization & Fault Detection	97
9.2.2	Intelligent Building Analytics	97
9.2.3	Urban Transportation Management	97
9.2.4	Optimizing Energy Production	98
9.2.5	Water Management	98
9.2.6	Urban Waste Management	98
9.3	Case Studies	98
9.3.1	Singapore: Building a Smart Nation with Big Data	99
9.3.2	Glasgow City Council: Promoting Smart City Efforts with Big Data	100
9.3.3	OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data	101
		
Chapter 10: Big Data in Insurance	102
10.1	Overview & Investment Potential	102
10.2	Key Applications	102
10.2.1	Claims Fraud Mitigation	102
10.2.2	Customer Retention & Profiling	103
10.2.3	Risk Management	103
10.3	Case Studies	103
10.3.1	Zurich Insurance Group: Enhancing Risk Management with Big Data	103
10.3.2	RSA Group: Improving Customer Relations with Big Data	105
10.3.3	Primerica: Improving Insurance Sales Force Productivity with Big Data	106
		
Chapter 11: Big Data in Manufacturing & Natural Resources	107
11.1	Overview & Investment Potential	107
11.2	Key Applications	107
11.2.1	Asset Maintenance & Downtime Reduction	107
11.2.2	Quality & Environmental Impact Control	108
11.2.3	Optimized Supply Chain	108
11.2.4	Exploration & Identification of Natural Resources	108
11.3	Case Studies	109
11.3.1	Intel Corporation: Cutting Manufacturing Costs with Big Data	109
11.3.2	Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data	110
11.3.3	Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data	111
11.3.4	Brunei: Saving Natural Resources with Big Data	112
		
Chapter 12: Big Data in Web, Media & Entertainment	113
12.1	Overview & Investment Potential	113
12.2	Key Applications	113
12.2.1	Audience & Advertising Optimization	114
12.2.2	Channel Optimization	114
12.2.3	Recommendation Engines	114
12.2.4	Optimized Search	114
12.2.5	Live Sports Event Analytics	115
12.2.6	Outsourcing Big Data Analytics to Other Verticals	115
12.3	Case Studies	115
12.3.1	Netflix: Improving Viewership with Big Data	115
12.3.2	NFL (National Football League): Improving Stadium Experience with Big Data	117
12.3.3	Walt Disney Company: Enhancing Theme Park Experience with Big Data	118
12.3.4	Baidu: Reshaping Search Capabilities with Big Data	119
12.3.5	Constant Contact: Effective Marketing with Big Data	120
		
Chapter 13: Big Data in Public Safety & Homeland Security	121
13.1	Overview & Investment Potential	121
13.2	Key Applications	121
13.2.1	Cyber Crime Mitigation	122
13.2.2	Crime Prediction Analytics	122
13.2.3	Video Analytics & Situational Awareness	122
13.3	Case Studies	123
13.3.1	DHS (U.S. Department of Homeland Security): Identifying Threats to Physical and Network Infrastructure with Big Data	123
13.3.2	Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data	124
13.3.3	Memphis Police Department: Crime Reduction with Big Data	125
		
Chapter 14: Big Data in Public Services	126
14.1	Overview & Investment Potential	126
14.2	Key Applications	126
14.2.1	Public Sentiment Analysis	126
14.2.2	Tax Collection & Fraud Detection	127
14.2.3	Economic Analysis	127
14.2.4	Predicting & Mitigating Disasters	127
14.3	Case Studies	128
14.3.1	ONS (Office for National Statistics): Exploring the UK Economy with Big Data	128
14.3.2	New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data	129
14.3.3	Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data	130
14.3.4	City of Chicago: Improving Government Productivity with Big Data	131
14.3.5	FDNY (Fire Department of the City of New York): Fighting Fires with Big Data	132
14.3.6	Ambulance Victoria: Improving Patient Survival Rates with Big Data	133
		
Chapter 15: Big Data in Retail, Wholesale & Hospitality	134
15.1	Overview & Investment Potential	134
15.2	Key Applications	134
15.2.1	Customer Sentiment Analysis	135
15.2.2	Customer & Branch Segmentation	135
15.2.3	Price Optimization	135
15.2.4	Personalized Marketing	135
15.2.5	Optimizing & Monitoring the Supply Chain	136
15.2.6	In-Field Sales Analytics	136
15.3	Case Studies	136
15.3.1	Walmart: Making Smarter Stocking Decision with Big Data	137
15.3.2	Tesco: Reducing Supermarket Energy Bills with Big Data	138
15.3.3	Marriott International: Elevating Guest Services with Big Data	139
15.3.4	JJ Food Service: Predictive Wholesale Shopping Lists with Big Data	140
		
Chapter 16: Big Data in Telecommunications	141
16.1	Overview & Investment Potential	141
16.2	Key Applications	141
16.2.1	Network Performance & Coverage Optimization	141
16.2.2	Customer Churn Prevention	142
16.2.3	Personalized Marketing	142
16.2.4	Tailored Location Based Services	142
16.2.5	Fraud Detection	142
16.3	Case Studies	143
16.3.1	BT Group: Hunting Down Nuisance Callers with Big Data	143
16.3.2	AT&T: Smart Network Management with Big Data	144
16.3.3	T-Mobile USA: Cutting Down Churn Rate with Big Data	145
16.3.4	TEOCO: Helping Service Providers Save Millions with Big Data	146
16.3.5	Freedom Mobile: Optimizing Video Quality with Big Data	147
16.3.6	Coriant: SaaS Based Analytics with Big Data	148
		
Chapter 17: Big Data in Utilities & Energy	149
17.1	Overview & Investment Potential	149
17.2	Key Applications	149
17.2.1	Customer Retention	149
17.2.2	Forecasting Energy	150
17.2.3	Billing Analytics	150
17.2.4	Predictive Maintenance	150
17.2.5	Maximizing the Potential of Drilling	150
17.2.6	Production Optimization	151
17.3	Case Studies	151
17.3.1	Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data	151
17.3.2	British Gas: Improving Customer Service with Big Data	152
17.3.3	Oncor Electric Delivery: Intelligent Power Grid Management with Big Data	153
		
Chapter 18: Big Data Industry Roadmap & Value Chain	154
18.1	Big Data Industry Roadmap	154
18.1.1	2017 – 2020: Investments in Predictive Analytics & SaaS-Based Big Data Offerings	154
18.1.2	2020 – 2025: Growing Focus on Cognitive & Personalized Analytics	155
18.1.3	2025 – 2030: Convergence with Future IoT Applications	155
18.2	The Big Data Value Chain	156
18.2.1	Hardware Providers	156
18.2.1.1	Storage & Compute Infrastructure Providers	156
18.2.1.2	Networking Infrastructure Providers	157
18.2.2	Software Providers	158
18.2.2.1	Hadoop & Infrastructure Software Providers	158
18.2.2.2	SQL & NoSQL Providers	158
18.2.2.3	Analytic Platform & Application Software Providers	158
18.2.2.4	Cloud Platform Providers	159
18.2.3	Professional Services Providers	159
18.2.4	End-to-End Solution Providers	159
18.2.5	Vertical Enterprises	159
		
Chapter 19: Standardization & Regulatory Initiatives	160
19.1	ASF (Apache Software Foundation)	160
19.1.1	Management of Hadoop	160
19.1.2	Big Data Projects Beyond Hadoop	160
19.2	CSA (Cloud Security Alliance)	163
19.2.1	BDWG (Big Data Working Group)	163
19.3	CSCC (Cloud Standards Customer Council)	164
19.3.1	Big Data Working Group	164
19.4	DMG  (Data Mining Group)	165
19.4.1	PMML (Predictive Model Markup Language) Working Group	165
19.4.2	PFA (Portable Format for Analytics) Working Group	165
19.5	IEEE (Institute of Electrical and Electronics Engineers) –Big Data Initiative	166
19.6	INCITS (InterNational Committee for Information Technology Standards)	167
19.6.1	Big Data Technical Committee	167
19.7	ISO (International Organization for Standardization)	168
19.7.1	ISO/IEC JTC 1/SC 32: Data Management and Interchange	168
19.7.2	ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms	169
19.7.3	ISO/IEC JTC 1/SC 27: IT Security Techniques	169
19.7.4	ISO/IEC JTC 1/WG 9: Big Data	169
19.7.5	Collaborations with Other ISO Work Groups	171
19.8	ITU (International Telecommunications Union)	171
19.8.1	ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities	171
19.8.2	Other Deliverables Through SG (Study Group) 13 on Future Networks	172
19.8.3	Other Relevant Work	173
19.9	Linux Foundation	173
19.9.1	ODPi (Open Ecosystem of Big Data)	173
19.10	NIST (National Institute of Standards and Technology)	174
19.10.1	NBD-PWG (NIST Big Data Public Working Group)	174
19.11	OASIS (Organization for the Advancement of Structured Information Standards)	175
19.11.1	Technical Committees	175
19.12	ODaF (Open Data Foundation)	176
19.12.1	Big Data Accessibility	176
19.13	ODCA (Open Data Center Alliance)	176
19.13.1	Work on Big Data	176
19.14	OGC (Open Geospatial Consortium)	177
19.14.1	Big Data DWG (Domain Working Group)	177
19.15	TM Forum	177
19.15.1	Big Data Analytics Strategic Program	177
19.16	TPC (Transaction Processing Performance Council)	178
19.16.1	TPC-BDWG (TPC Big Data Working Group)	178
19.17	W3C (World Wide Web Consortium)	178
19.17.1	Big Data Community Group	178
19.17.2	Open Government Community Group	179
		
Chapter 20: Market Analysis & Forecasts	180
20.1	Global Outlook for the Big Data Market	180
20.2	Submarket Segmentation	181
20.2.1	Storage and Compute Infrastructure	182
20.2.2	Networking Infrastructure	183
20.2.3	Hadoop & Infrastructure Software	184
20.2.4	SQL	185
20.2.5	NoSQL	186
20.2.6	Analytic Platforms & Applications	187
20.2.7	Cloud Platforms	188
20.2.8	Professional Services	189
20.3	Vertical Market Segmentation	190
20.3.1	Automotive, Aerospace & Transportation	191
20.3.2	Banking & Securities	192
20.3.3	Defense & Intelligence	193
20.3.4	Education	194
20.3.5	Healthcare & Pharmaceutical	195
20.3.6	Smart Cities & Intelligent Buildings	196
20.3.7	Insurance	197
20.3.8	Manufacturing & Natural Resources	198
20.3.9	Media & Entertainment	199
20.3.10	Public Safety & Homeland Security	200
20.3.11	Public Services	201
20.3.12	Retail, Wholesale & Hospitality	202
20.3.13	Telecommunications	203
20.3.14	Utilities & Energy	204
20.3.15	Other Sectors	205
20.4	Regional Outlook	206
20.5	Asia Pacific	207
20.5.1	Country Level Segmentation	207
20.5.2	Australia	208
20.5.3	China	208
20.5.4	India	209
20.5.5	Indonesia	209
20.5.6	Japan	210
20.5.7	Malaysia	210
20.5.8	Pakistan	211
20.5.9	Philippines	211
20.5.10	Singapore	212
20.5.11	South Korea	212
20.5.12	Taiwan	213
20.5.13	Thailand	213
20.5.14	Rest of Asia Pacific	214
20.6	Eastern Europe	215
20.6.1	Country Level Segmentation	215
20.6.2	Czech Republic	216
20.6.3	Poland	216
20.6.4	Russia	217
20.6.5	Rest of Eastern Europe	217
20.7	Latin & Central America	218
20.7.1	Country Level Segmentation	218
20.7.2	Argentina	219
20.7.3	Brazil	219
20.7.4	Mexico	220
20.7.5	Rest of Latin & Central America	220
20.8	Middle East & Africa	221
20.8.1	Country Level Segmentation	221
20.8.2	Israel	222
20.8.3	Qatar	222
20.8.4	Saudi Arabia	223
20.8.5	South Africa	223
20.8.6	UAE	224
20.8.7	Rest of the Middle East & Africa	224
20.9	North America	225
20.9.1	Country Level Segmentation	225
20.9.2	Canada	226
20.9.3	USA	226
20.10	Western Europe	227
20.10.1	Country Level Segmentation	227
20.10.2	Denmark	228
20.10.3	Finland	228
20.10.4	France	229
20.10.5	Germany	229
20.10.6	Italy	230
20.10.7	Netherlands	230
20.10.8	Norway	231
20.10.9	Spain	231
20.10.10	Sweden	232
20.10.11	UK	232
20.10.12	Rest of Western Europe	233
		
Chapter 21: Vendor Landscape	234
21.1	1010data	234
21.2	Absolutdata	235
21.3	Accenture	236
21.4	Actian Corporation	237
21.5	Adaptive Insights	238
21.6	Advizor Solutions	239
21.7	AeroSpike	240
21.8	AFS Technologies	241
21.9	Alation	242
21.10	Algorithmia	243
21.11	Alluxio	244
21.12	Alpine Data	245
21.13	Alteryx	246
21.14	AMD (Advanced Micro Devices)	247
21.15	Apixio	248
21.16	Arcadia Data	249
21.17	Arimo	250
21.18	ARM	251
21.19	AtScale	252
21.20	Attivio	253
21.21	Attunity	254
21.22	Automated Insights	255
21.23	AWS (Amazon Web Services)	256
21.24	Axiomatics	257
21.25	Ayasdi	258
21.26	Basho Technologies	259
21.27	BCG (Boston Consulting Group)	260
21.28	Bedrock Data	261
21.29	BetterWorks	262
21.30	Big Cloud Analytics	263
21.31	Big Panda	264
21.32	Birst	265
21.33	Bitam	266
21.34	Blue Medora	267
21.35	BlueData Software	268
21.36	BlueTalon	269
21.37	BMC Software	270
21.38	BOARD International	271
21.39	Booz Allen Hamilton	272
21.40	Boxever	273
21.41	CACI International	274
21.42	Cambridge Semantics	275
21.43	Capgemini	276
21.44	Cazena	277
21.45	Centrifuge Systems	278
21.46	CenturyLink	279
21.47	Chartio	280
21.48	Cisco Systems	281
21.49	Civis Analytics	282
21.50	ClearStory Data	283
21.51	Cloudability	284
21.52	Cloudera	285
21.53	Clustrix	286
21.54	CognitiveScale	287
21.55	Collibra	288
21.56	Concurrent Computer Corporation	289
21.57	Confluent	290
21.58	Contexti	291
21.59	Continuum Analytics	292
21.60	Couchbase	293
21.61	CrowdFlower	294
21.62	Databricks	295
21.63	DataGravity	296
21.64	Dataiku	297
21.65	Datameer	298
21.66	DataRobot	299
21.67	DataScience	300
21.68	DataStax	301
21.69	DataTorrent	302
21.70	Datawatch Corporation	303
21.71	Datos IO	304
21.72	DDN (DataDirect Networks)	305
21.73	Decisyon	306
21.74	Dell Technologies	307
21.75	Deloitte	308
21.76	Demandbase	309
21.77	Denodo Technologies	310
21.78	Digital Reasoning Systems	311
21.79	Dimensional Insight	312
21.80	Dolphin Enterprise Solutions Corporation	313
21.81	Domino Data Lab	314
21.82	Domo	315
21.83	DriveScale	316
21.84	Dundas Data Visualization	317
21.85	DXC Technology	318
21.86	Eligotech	319
21.87	Engineering Group (Engineering Ingegneria Informatica)	320
21.88	EnterpriseDB	321
21.89	eQ Technologic	322
21.90	Ericsson	323
21.91	EXASOL	324
21.92	Facebook	325
21.93	FICO (Fair Isaac Corporation)	326
21.94	Fractal Analytics	327
21.95	Fujitsu	328
21.96	Fuzzy Logix	330
21.97	Gainsight	331
21.98	GE (General Electric)	332
21.99	Glassbeam	333
21.100	GoodData Corporation	334
21.101	Google	335
21.102	Greenwave Systems	336
21.103	GridGain Systems	337
21.104	Guavus	338
21.105	H2O.ai	339
21.106	HDS (Hitachi Data Systems)	340
21.107	Hedvig	341
21.108	Hortonworks	342
21.109	HPE (Hewlett Packard Enterprise)	343
21.110	Huawei	345
21.111	IBM Corporation	346
21.112	iDashboards	348
21.113	Impetus Technologies	349
21.114	Incorta	350
21.115	InetSoft Technology Corporation	351
21.116	Infer	352
21.117	Infor	353
21.118	Informatica Corporation	354
21.119	Information Builders	355
21.120	Infosys	356
21.121	Infoworks	357
21.122	Insightsoftware.com	358
21.123	InsightSquared	359
21.124	Intel Corporation	360
21.125	Interana	361
21.126	InterSystems Corporation	362
21.127	Jedox	363
21.128	Jethro	364
21.129	Jinfonet Software	365
21.130	Juniper Networks	366
21.131	KALEAO	367
21.132	Keen IO	368
21.133	Kinetica	369
21.134	KNIME	370
21.135	Kognitio	371
21.136	Kyvos Insights	372
21.137	Lavastorm	373
21.138	Lexalytics	374
21.139	Lexmark International	375
21.140	Logi Analytics	376
21.141	Longview Solutions	377
21.142	Looker Data Sciences	378
21.143	LucidWorks	379
21.144	Luminoso Technologies	380
21.145	Maana	381
21.146	Magento Commerce	382
21.147	Manthan Software Services	383
21.148	MapD Technologies	384
21.149	MapR Technologies	385
21.150	MariaDB Corporation	386
21.151	MarkLogic Corporation	387
21.152	Mathworks	388
21.153	MemSQL	389
21.154	Metric Insights	390
21.155	Microsoft Corporation	391
21.156	MicroStrategy	392
21.157	Minitab	393
21.158	MongoDB	394
21.159	Mu Sigma	395
21.160	Neo Technology	396
21.161	NetApp	397
21.162	Nimbix	398
21.163	Nokia	399
21.164	NTT Data Corporation	400
21.165	Numerify	401
21.166	NuoDB	402
21.167	Nutonian	403
21.168	NVIDIA Corporation	404
21.169	Oblong Industries	405
21.170	OpenText Corporation	406
21.171	Opera Solutions	408
21.172	Optimal Plus	409
21.173	Oracle Corporation	410
21.174	Palantir Technologies	412
21.175	Panorama Software	413
21.176	Paxata	414
21.177	Pentaho Corporation	415
21.178	Pepperdata	416
21.179	Phocas Software	417
21.180	Pivotal Software	418
21.181	Prognoz	420
21.182	Progress Software Corporation	421
21.183	PwC (PricewaterhouseCoopers International)	422
21.184	Pyramid Analytics	423
21.185	Qlik	424
21.186	Quantum Corporation	425
21.187	Qubole	426
21.188	Rackspace	427
21.189	Radius Intelligence	428
21.190	RapidMiner	429
21.191	Recorded Future	430
21.192	Red Hat	431
21.193	Redis Labs	432
21.194	RedPoint Global	433
21.195	Reltio	434
21.196	RStudio	435
21.197	Ryft Systems	436
21.198	Sailthru	437
21.199	Salesforce.com	438
21.200	Salient Management Company	439
21.201	Samsung Group	440
21.202	SAP	441
21.203	SAS Institute	442
21.204	ScaleDB	443
21.205	ScaleOut Software	444
21.206	SCIO Health Analytics	445
21.207	Seagate Technology	446
21.208	Sinequa	447
21.209	SiSense	448
21.210	SnapLogic	449
21.211	Snowflake Computing	450
21.212	Software AG	451
21.213	Splice Machine	452
21.214	Splunk	453
21.215	Sqrrl	454
21.216	Strategy Companion Corporation	455
21.217	StreamSets	456
21.218	Striim	457
21.219	Sumo Logic	458
21.220	Supermicro (Super Micro Computer)	459
21.221	Syncsort	460
21.222	SynerScope	461
21.223	Tableau Software	462
21.224	Talena	463
21.225	Talend	464
21.226	Tamr	465
21.227	TARGIT	466
21.228	TCS (Tata Consultancy Services)	467
21.229	Teradata Corporation	468
21.230	ThoughtSpot	470
21.231	TIBCO Software	471
21.232	Tidemark	472
21.233	Toshiba Corporation	473
21.234	Trifacta	474
21.235	Unravel Data	475
21.236	VMware	476
21.237	VoltDB	477
21.238	Waterline Data	478
21.239	Western Digital Corporation	479
21.240	WiPro	480
21.241	Workday	481
21.242	Xplenty	482
21.243	Yellowfin International	483
21.244	Yseop	484
21.245	Zendesk	485
21.246	Zoomdata	486
21.247	Zucchetti	487
		
Chapter 22: Conclusion & Strategic Recommendations	488
22.1	Big Data Technology: Beyond Data Capture & Analytics	488
22.2	Transforming IT from a Cost Center to a Profit Center	488
22.3	Can Privacy Implications Hinder Success?	489
22.4	Maximizing Innovation with Careful Regulation	489
22.5	Battling Organizational & Data Silos	490
22.6	Moving Big Data to the Cloud	491
22.7	Software vs. Hardware Investments	492
22.8	Vendor Share: Who Leads the Market?	493
22.9	Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena	494
22.10	Big Data Driving Wider IT Industry Investments	495
22.11	Assessing the Impact of IoT & M2M	496
22.12	Recommendations	497
22.12.1	Big Data Hardware, Software & Professional Services Providers	497
22.12.2	Enterprises	498
List of Figures	
	
	Figure 1: Hadoop Architecture	41
	Figure 2: Reactive vs. Proactive Analytics	54
	Figure 3: Big Data Industry Roadmap	155
	Figure 4: Big Data Value Chain	157
	Figure 5: Key Aspects of Big Data Standardization	167
	Figure 6: Global Big Data Revenue: 2017 - 2030 ($ Million)	181
	Figure 7: Global Big Data Revenue by Submarket: 2017 - 2030 ($ Million)	182
	Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2017 - 2030 ($ Million)	183
	Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2017 - 2030 ($ Million)	184
	Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2017 - 2030 ($ Million)	185
	Figure 11: Global Big Data SQL Submarket Revenue: 2017 - 2030 ($ Million)	186
	Figure 12: Global Big Data NoSQL Submarket Revenue: 2017 - 2030 ($ Million)	187
	Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2017 - 2030 ($ Million)	188
	Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2017 - 2030 ($ Million)	189
	Figure 15: Global Big Data Professional Services Submarket Revenue: 2017 - 2030 ($ Million)	190
	Figure 16: Global Big Data Revenue by Vertical Market: 2017 - 2030 ($ Million)	191
	Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2017 - 2030 ($ Million)	192
	Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2017 - 2030 ($ Million)	193
	Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2017 - 2030 ($ Million)	194
	Figure 20: Global Big Data Revenue in the Education Sector: 2017 - 2030 ($ Million)	195
	Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2017 - 2030 ($ Million)	196
	Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2017 - 2030 ($ Million)	197
	Figure 23: Global Big Data Revenue in the Insurance Sector: 2017 - 2030 ($ Million)	198
	Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2017 - 2030 ($ Million)	199
	Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2017 - 2030 ($ Million)	200
	Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2017 - 2030 ($ Million)	201
	Figure 27: Global Big Data Revenue in the Public Services Sector: 2017 - 2030 ($ Million)	202
	Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2017 - 2030 ($ Million)	203
	Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2017 - 2030 ($ Million)	204
	Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2017 - 2030 ($ Million)	205
	Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2017 - 2030 ($ Million)	206
	Figure 32: Big Data Revenue by Region: 2017 - 2030 ($ Million)	207
	Figure 33: Asia Pacific Big Data Revenue: 2017 - 2030 ($ Million)	208
	Figure 34: Asia Pacific Big Data Revenue by Country: 2017 - 2030 ($ Million)	208
	Figure 35: Australia Big Data Revenue: 2017 - 2030 ($ Million)	209
	Figure 36: China Big Data Revenue: 2017 - 2030 ($ Million)	209
	Figure 37: India Big Data Revenue: 2017 - 2030 ($ Million)	210
	Figure 38: Indonesia Big Data Revenue: 2017 - 2030 ($ Million)	210
	Figure 39: Japan Big Data Revenue: 2017 - 2030 ($ Million)	211
	Figure 40: Malaysia Big Data Revenue: 2017 - 2030 ($ Million)	211
	Figure 41: Pakistan Big Data Revenue: 2017 - 2030 ($ Million)	212
	Figure 42: Philippines Big Data Revenue: 2017 - 2030 ($ Million)	212
	Figure 43: Singapore Big Data Revenue: 2017 - 2030 ($ Million)	213
	Figure 44: South Korea Big Data Revenue: 2017 - 2030 ($ Million)	213
	Figure 45: Taiwan Big Data Revenue: 2017 - 2030 ($ Million)	214
	Figure 46: Thailand Big Data Revenue: 2017 - 2030 ($ Million)	214
	Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2017 - 2030 ($ Million)	215
	Figure 48: Eastern Europe Big Data Revenue: 2017 - 2030 ($ Million)	216
	Figure 49: Eastern Europe Big Data Revenue by Country: 2017 - 2030 ($ Million)	216
	Figure 50: Czech Republic Big Data Revenue: 2017 - 2030 ($ Million)	217
	Figure 51: Poland Big Data Revenue: 2017 - 2030 ($ Million)	217
	Figure 52: Russia Big Data Revenue: 2017 - 2030 ($ Million)	218
	Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2017 - 2030 ($ Million)	218
	Figure 54: Latin & Central America Big Data Revenue: 2017 - 2030 ($ Million)	219
	Figure 55: Latin & Central America Big Data Revenue by Country: 2017 - 2030 ($ Million)	219
	Figure 56: Argentina Big Data Revenue: 2017 - 2030 ($ Million)	220
	Figure 57: Brazil Big Data Revenue: 2017 - 2030 ($ Million)	220
	Figure 58: Mexico Big Data Revenue: 2017 - 2030 ($ Million)	221
	Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2017 - 2030 ($ Million)	221
	Figure 60: Middle East & Africa Big Data Revenue: 2017 - 2030 ($ Million)	222
	Figure 61: Middle East & Africa Big Data Revenue by Country: 2017 - 2030 ($ Million)	222
	Figure 62: Israel Big Data Revenue: 2017 - 2030 ($ Million)	223
	Figure 63: Qatar Big Data Revenue: 2017 - 2030 ($ Million)	223
	Figure 64: Saudi Arabia Big Data Revenue: 2017 - 2030 ($ Million)	224
	Figure 65: South Africa Big Data Revenue: 2017 - 2030 ($ Million)	224
	Figure 66: UAE Big Data Revenue: 2017 - 2030 ($ Million)	225
	Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2017 - 2030 ($ Million)	225
	Figure 68: North America Big Data Revenue: 2017 - 2030 ($ Million)	226
	Figure 69: North America Big Data Revenue by Country: 2017 - 2030 ($ Million)	226
	Figure 70: Canada Big Data Revenue: 2017 - 2030 ($ Million)	227
	Figure 71: USA Big Data Revenue: 2017 - 2030 ($ Million)	227
	Figure 72: Western Europe Big Data Revenue: 2017 - 2030 ($ Million)	228
	Figure 73: Western Europe Big Data Revenue by Country: 2017 - 2030 ($ Million)	228
	Figure 74: Denmark Big Data Revenue: 2017 - 2030 ($ Million)	229
	Figure 75: Finland Big Data Revenue: 2017 - 2030 ($ Million)	229
	Figure 76: France Big Data Revenue: 2017 - 2030 ($ Million)	230
	Figure 77: Germany Big Data Revenue: 2017 - 2030 ($ Million)	230
	Figure 78: Italy Big Data Revenue: 2017 - 2030 ($ Million)	231
	Figure 79: Netherlands Big Data Revenue: 2017 - 2030 ($ Million)	231
	Figure 80: Norway Big Data Revenue: 2017 - 2030 ($ Million)	232
	Figure 81: Spain Big Data Revenue: 2017 - 2030 ($ Million)	232
	Figure 82: Sweden Big Data Revenue: 2017 - 2030 ($ Million)	233
	Figure 83: UK Big Data Revenue: 2017 - 2030 ($ Million)	233
	Figure 84: Big Data Revenue in the Rest of Western Europe: 2017 - 2030 ($ Million)	234
	Figure 85: Global Big Data Workload Distribution by Environment: 2017 - 2030 (%)	492
	Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2017 - 2030 ($ Million)	493
	Figure 87: Big Data Vendor Market Share (%)	494
	Figure 88: Global IT Expenditure Driven by Big Data Investments: 2017 - 2030 ($ Million)	496
	Figure 89: Global M2M Connections by Access Technology: 2017 - 2030 (Millions)	497 



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