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


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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
  • 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

NA

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