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.
1 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned
2 Chapter 2: An Overview of Big Data
2.1 What is Big Data?
2.2 Key Approaches to Big Data Processing
2.2.1 Hadoop
2.2.2 NoSQL
2.2.3 MPAD (Massively Parallel Analytic Databases)
2.2.4 In-Memory Processing
2.2.5 Stream Processing Technologies
2.2.6 Spark
2.2.7 Other Databases & Analytic Technologies
2.3 Key Characteristics of Big Data
2.3.1 Volume
2.3.2 Velocity
2.3.3 Variety
2.3.4 Value
2.4 Market Growth Drivers
2.4.1 Awareness of Benefits
2.4.2 Maturation of Big Data Platforms
2.4.3 Continued Investments by Web Giants, Governments & Enterprises
2.4.4 Growth of Data Volume, Velocity & Variety
2.4.5 Vendor Commitments & Partnerships
2.4.6 Technology Trends Lowering Entry Barriers
2.5 Market Barriers
2.5.1 Lack of Analytic Specialists
2.5.2 Uncertain Big Data Strategies
2.5.3 Organizational Resistance to Big Data Adoption
2.5.4 Technical Challenges: Scalability & Maintenance
2.5.5 Security & Privacy Concerns
3 Chapter 3: Big Data Analytics
3.1 What are Big Data Analytics?
3.2 The Importance of Analytics
3.3 Reactive vs. Proactive Analytics
3.4 Customer vs. Operational Analytics
3.5 Technology & Implementation Approaches
3.5.1 Grid Computing
3.5.2 In-Database Processing
3.5.3 In-Memory Analytics
3.5.4 Machine Learning & Data Mining
3.5.5 Predictive Analytics
3.5.6 NLP (Natural Language Processing)
3.5.7 Text Analytics
3.5.8 Visual Analytics
3.5.9 Graph Analytics
3.5.10 Social Media, IT & Telco Network Analytics
4 Chapter 4: Big Data in Automotive, Aerospace & Transportation
4.1 Overview & Investment Potential
4.2 Key Applications
4.2.1 Autonomous & Semi-Autonomous Driving
4.2.2 Streamlining Vehicle Recalls & Warranty Management
4.2.3 Fleet Management
4.2.4 Intelligent Transportation
4.2.5 UBI (Usage Based Insurance)
4.2.6 Predictive Aircraft Maintenance & Fuel Optimization
4.2.7 Air Traffic Control
4.3 Case Studies
4.3.1 Boeing: Making Flying More Efficient with Big Data
4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data
4.3.3 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data
4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data
4.3.5 Groupe Renault: Boosting Driver Safety with Big Data
4.3.6 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data
5 Chapter 5: Big Data in Banking & Securities
5.1 Overview & Investment Potential
5.2 Key Applications
5.2.1 Customer Retention & Personalized Products
5.2.2 Risk Management
5.2.3 Fraud Detection
5.2.4 Credit Scoring
5.3 Case Studies
5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data
5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data
5.3.3 OTP Bank: Reducing Loan Defaults with Big Data
5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data
6 Chapter 6: Big Data in Defense & Intelligence
6.1 Overview & Investment Potential
6.2 Key Applications
6.2.1 Intelligence Gathering
6.2.2 Battlefield Analytics
6.2.3 Energy Saving Opportunities in the Battlefield
6.2.4 Preventing Injuries on the Battlefield
6.3 Case Studies
6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data
6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data
6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats
6.3.4 Ministry of State Security, China: Predictive Policing with Big Data
6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data
7 Chapter 7: Big Data in Education
7.1 Overview & Investment Potential
7.2 Key Applications
7.2.1 Information Integration
7.2.2 Identifying Learning Patterns
7.2.3 Enabling Student-Directed Learning
7.3 Case Studies
7.3.1 Purdue University: Improving Academic Performance with Big Data
7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data
7.3.3 Edith Cowen University: Increasing Student Retention with Big Data
8 Chapter 8: Big Data in Healthcare & Pharma
8.1 Overview & Investment Potential
8.2 Key Applications
8.2.1 Drug Discovery, Design & Development
8.2.2 Clinical Development & Trials
8.2.3 Population Health Management
8.2.4 Personalized Healthcare & Targeted Treatments
8.2.5 Proactive & Remote Patient Monitoring
8.2.6 Preventive Care & Health Interventions
8.3 Case Studies
8.3.1 AstraZeneca: Analytics-Driven Drug Development with Big Data
8.3.2 Bangkok Hospital Group: Transforming the Patient Experience with Big Data
8.3.3 Novartis: Digitizing Healthcare with Big Data
8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data
8.3.5 Sanofi: Proactive Diabetes Care with Big Data
8.3.6 UnitedHealth Group: Enhancing Patient Care & Value with Big Data
9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings
9.1 Overview & Investment Potential
9.2 Key Applications
9.2.1 Energy Optimization & Fault Detection
9.2.2 Intelligent Building Analytics
9.2.3 Urban Transportation Management
9.2.4 Optimizing Energy Production
9.2.5 Water Management
9.2.6 Urban Waste Management
9.3 Case Studies
9.3.1 Singapore: Building a Smart Nation with Big Data
9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data
9.3.3 OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data
10 Chapter 10: Big Data in Insurance
10.1 Overview & Investment Potential
10.2 Key Applications
10.2.1 Claims Fraud Mitigation
10.2.2 Customer Retention & Profiling
10.2.3 Risk Management
10.3 Case Studies
10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data
10.3.2 RSA Group: Improving Customer Relations with Big Data
10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data
11 Chapter 11: Big Data in Manufacturing & Natural Resources
11.1 Overview & Investment Potential
11.2 Key Applications
11.2.1 Asset Maintenance & Downtime Reduction
11.2.2 Quality & Environmental Impact Control
11.2.3 Optimized Supply Chain
11.2.4 Exploration & Identification of Natural Resources
11.3 Case Studies
11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data
11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data
11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data
11.3.4 Brunei: Saving Natural Resources with Big Data
12 Chapter 12: Big Data in Web, Media & Entertainment
12.1 Overview & Investment Potential
12.2 Key Applications
12.2.1 Audience & Advertising Optimization
12.2.2 Channel Optimization
12.2.3 Recommendation Engines
12.2.4 Optimized Search
12.2.5 Live Sports Event Analytics
12.2.6 Outsourcing Big Data Analytics to Other Verticals
12.3 Case Studies
12.3.1 Twitter: Cracking Down on Abusive Content with Big Data
12.3.2 Netflix: Improving Viewership with Big Data
12.3.3 NFL (National Football League): Improving Stadium Experience with Big Data
12.3.4 Baidu: Reshaping Search Capabilities with Big Data
12.3.5 Constant Contact: Effective Marketing with Big Data
13 Chapter 13: Big Data in Public Safety & Homeland Security
13.1 Overview & Investment Potential
13.2 Key Applications
13.2.1 Cyber Crime Mitigation
13.2.2 Crime Prediction Analytics
13.2.3 Video Analytics & Situational Awareness
13.3 Case Studies
13.3.1 DHS (Department of Homeland Security): Identifying Threats with Big Data
13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data
13.3.3 Memphis Police Department: Crime Reduction with Big Data
14 Chapter 14: Big Data in Public Services
14.1 Overview & Investment Potential
14.2 Key Applications
14.2.1 Public Sentiment Analysis
14.2.2 Tax Collection & Fraud Detection
14.2.3 Economic Analysis
14.2.4 Predicting & Mitigating Disasters
14.3 Case Studies
14.3.1 ONS (Office for National Statistics): Exploring the UK Economy with Big Data
14.3.2 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data
14.3.3 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data
14.3.4 City of Chicago: Improving Government Productivity with Big Data
14.3.5 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data
14.3.6 Ambulance Victoria: Improving Patient Survival Rates with Big Data
15 Chapter 15: Big Data in Retail, Wholesale & Hospitality
15.1 Overview & Investment Potential
15.2 Key Applications
15.2.1 Customer Sentiment Analysis
15.2.2 Customer & Branch Segmentation
15.2.3 Price Optimization
15.2.4 Personalized Marketing
15.2.5 Optimizing & Monitoring the Supply Chain
15.2.6 In-Field Sales Analytics
15.3 Case Studies
15.3.1 Walmart: Making Smarter Stocking Decision with Big Data
15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data
15.3.3 The Walt Disney Company: Theme Park Management with Big Data
15.3.4 Marriott International: Elevating Guest Services with Big Data
15.3.5 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data
16 Chapter 16: Big Data in Telecommunications
16.1 Overview & Investment Potential
16.2 Key Applications
16.2.1 Network Performance & Coverage Optimization
16.2.2 Customer Churn Prevention
16.2.3 Personalized Marketing
16.2.4 Tailored Location Based Services
16.2.5 Fraud Detection
16.3 Case Studies
16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data
16.3.2 AT&T: Smart Network Management with Big Data
16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data
16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data
16.3.5 Freedom Mobile: Optimizing Video Quality with Big Data
16.3.6 Coriant: SaaS Based Analytics with Big Data
17 Chapter 17: Big Data in Utilities & Energy
17.1 Overview & Investment Potential
17.2 Key Applications
17.2.1 Customer Retention
17.2.2 Forecasting Energy
17.2.3 Billing Analytics
17.2.4 Predictive Maintenance
17.2.5 Maximizing the Potential of Drilling
17.2.6 Production Optimization
17.3 Case Studies
17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data
17.3.2 British Gas: Improving Customer Service with Big Data
17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data
18 Chapter 18: Future Roadmap & Value Chain
18.1 Future Roadmap
18.1.1 Pre-2020: Towards Cloud-Based Big Data Offerings for Advanced Analytics
18.1.2 2020 – 2025: Growing Focus on AI (Artificial Intelligence), Deep Learning & Edge Analytics
18.1.3 2025 – 2030: Convergence with Future IoT Applications
18.2 The Big Data Value Chain
18.2.1 Hardware Providers
18.2.1.1 Storage & Compute Infrastructure Providers
18.2.1.2 Networking Infrastructure Providers
18.2.2 Software Providers
18.2.2.1 Hadoop & Infrastructure Software Providers
18.2.2.2 SQL & NoSQL Providers
18.2.2.3 Analytic Platform & Application Software Providers
18.2.2.4 Cloud Platform Providers
18.2.3 Professional Services Providers
18.2.4 End-to-End Solution Providers
18.2.5 Vertical Enterprises
19 Chapter 19: Standardization & Regulatory Initiatives
19.1 ASF (Apache Software Foundation)
19.1.1 Management of Hadoop
19.1.2 Big Data Projects Beyond Hadoop
19.2 CSA (Cloud Security Alliance)
19.2.1 BDWG (Big Data Working Group)
19.3 CSCC (Cloud Standards Customer Council)
19.3.1 Big Data Working Group
19.4 DMG (Data Mining Group)
19.4.1 PMML (Predictive Model Markup Language) Working Group
19.4.2 PFA (Portable Format for Analytics) Working Group
19.5 IEEE (Institute of Electrical and Electronics Engineers)
19.5.1 Big Data Initiative
19.6 INCITS (InterNational Committee for Information Technology Standards)
19.6.1 Big Data Technical Committee
19.7 ISO (International Organization for Standardization)
19.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange
19.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
19.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques
19.7.4 ISO/IEC JTC 1/WG 9: Big Data
19.7.5 Collaborations with Other ISO Work Groups
19.8 ITU (International Telecommunication Union)
19.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
19.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks
19.8.3 Other Relevant Work
19.9 Linux Foundation
19.9.1 ODPi (Open Ecosystem of Big Data)
19.10 NIST (National Institute of Standards and Technology)
19.10.1 NBD-PWG (NIST Big Data Public Working Group)
19.11 OASIS (Organization for the Advancement of Structured Information Standards)
19.11.1 Technical Committees
19.12 ODaF (Open Data Foundation)
19.12.1 Big Data Accessibility
19.13 ODCA (Open Data Center Alliance)
19.13.1 Work on Big Data
19.14 OGC (Open Geospatial Consortium)
19.14.1 Big Data DWG (Domain Working Group)
19.15 TM Forum
19.15.1 Big Data Analytics Strategic Program
19.16 TPC (Transaction Processing Performance Council)
19.16.1 TPC-BDWG (TPC Big Data Working Group)
19.17 W3C (World Wide Web Consortium)
19.17.1 Big Data Community Group
19.17.2 Open Government Community Group
20 Chapter 20: Market Sizing & Forecasts
20.1 Global Outlook for the Big Data Market
20.2 Submarket Segmentation
20.2.1 Storage and Compute Infrastructure
20.2.2 Networking Infrastructure
20.2.3 Hadoop & Infrastructure Software
20.2.4 SQL
20.2.5 NoSQL
20.2.6 Analytic Platforms & Applications
20.2.7 Cloud Platforms
20.2.8 Professional Services
20.3 Vertical Market Segmentation
20.3.1 Automotive, Aerospace & Transportation
20.3.2 Banking & Securities
20.3.3 Defense & Intelligence
20.3.4 Education
20.3.5 Healthcare & Pharmaceutical
20.3.6 Smart Cities & Intelligent Buildings
20.3.7 Insurance
20.3.8 Manufacturing & Natural Resources
20.3.9 Media & Entertainment
20.3.10 Public Safety & Homeland Security
20.3.11 Public Services
20.3.12 Retail, Wholesale & Hospitality
20.3.13 Telecommunications
20.3.14 Utilities & Energy
20.3.15 Other Sectors
20.4 Regional Outlook
20.5 Asia Pacific
20.5.1 Country Level Segmentation
20.5.2 Australia
20.5.3 China
20.5.4 India
20.5.5 Indonesia
20.5.6 Japan
20.5.7 Malaysia
20.5.8 Pakistan
20.5.9 Philippines
20.5.10 Singapore
20.5.11 South Korea
20.5.12 Taiwan
20.5.13 Thailand
20.5.14 Rest of Asia Pacific
20.6 Eastern Europe
20.6.1 Country Level Segmentation
20.6.2 Czech Republic
20.6.3 Poland
20.6.4 Russia
20.6.5 Rest of Eastern Europe
20.7 Latin & Central America
20.7.1 Country Level Segmentation
20.7.2 Argentina
20.7.3 Brazil
20.7.4 Mexico
20.7.5 Rest of Latin & Central America
20.8 Middle East & Africa
20.8.1 Country Level Segmentation
20.8.2 Israel
20.8.3 Qatar
20.8.4 Saudi Arabia
20.8.5 South Africa
20.8.6 UAE
20.8.7 Rest of the Middle East & Africa
20.9 North America
20.9.1 Country Level Segmentation
20.9.2 Canada
20.9.3 USA
20.10 Western Europe
20.10.1 Country Level Segmentation
20.10.2 Denmark
20.10.3 Finland
20.10.4 France
20.10.5 Germany
20.10.6 Italy
20.10.7 Netherlands
20.10.8 Norway
20.10.9 Spain
20.10.10 Sweden
20.10.11 UK
20.10.12 Rest of Western Europe
21 Chapter 21: Vendor Landscape
21.1 1010data
21.2 Absolutdata
21.3 Accenture
21.4 Actian Corporation/HCL Technologies
21.5 Adaptive Insights
21.6 Adobe Systems
21.7 Advizor Solutions
21.8 AeroSpike
21.9 AFS Technologies
21.10 Alation
21.11 Algorithmia
21.12 Alluxio
21.13 ALTEN
21.14 Alteryx
21.15 AMD (Advanced Micro Devices)
21.16 Anaconda
21.17 Apixio
21.18 Arcadia Data
21.19 ARM
21.20 AtScale
21.21 Attivio
21.22 Attunity
21.23 Automated Insights
21.24 AVORA
21.25 AWS (Amazon Web Services)
21.26 Axiomatics
21.27 Ayasdi
21.28 BackOffice Associates
21.29 Basho Technologies
21.30 BCG (Boston Consulting Group)
21.31 Bedrock Data
21.32 BetterWorks
21.33 Big Panda
21.34 BigML
21.35 Bitam
21.36 Blue Medora
21.37 BlueData Software
21.38 BlueTalon
21.39 BMC Software
21.40 BOARD International
21.41 Booz Allen Hamilton
21.42 Boxever
21.43 CACI International
21.44 Cambridge Semantics
21.45 Capgemini
21.46 Cazena
21.47 Centrifuge Systems
21.48 CenturyLink
21.49 Chartio
21.50 Cisco Systems
21.51 Civis Analytics
21.52 ClearStory Data
21.53 Cloudability
21.54 Cloudera
21.55 Cloudian
21.56 Clustrix
21.57 CognitiveScale
21.58 Collibra
21.59 Concurrent Technology/Vecima Networks
21.60 Confluent
21.61 Contexti
21.62 Couchbase
21.63 Crate.io
21.64 Cray
21.65 Databricks
21.66 Dataiku
21.67 Datalytyx
21.68 Datameer
21.69 DataRobot
21.70 DataStax
21.71 Datawatch Corporation
21.72 DDN (DataDirect Networks)
21.73 Decisyon
21.74 Dell Technologies
21.75 Deloitte
21.76 Demandbase
21.77 Denodo Technologies
21.78 Dianomic Systems
21.79 Digital Reasoning Systems
21.80 Dimensional Insight
21.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group
21.82 Domino Data Lab
21.83 Domo
21.84 Dremio
21.85 DriveScale
21.86 Druva
21.87 Dundas Data Visualization
21.88 DXC Technology
21.89 Elastic
21.90 Engineering Group (Engineering Ingegneria Informatica)
21.91 EnterpriseDB Corporation
21.92 eQ Technologic
21.93 Ericsson
21.94 Erwin
21.95 EVŌ (Big Cloud Analytics)
21.96 EXASOL
21.97 EXL (ExlService Holdings)
21.98 Facebook
21.99 FICO (Fair Isaac Corporation)
21.100 Figure Eight
21.101 FogHorn Systems
21.102 Fractal Analytics
21.103 Franz
21.104 Fujitsu
21.105 Fuzzy Logix
21.106 Gainsight
21.107 GE (General Electric)
21.108 Glassbeam
21.109 GoodData Corporation
21.110 Google/Alphabet
21.111 Grakn Labs
21.112 Greenwave Systems
21.113 GridGain Systems
21.114 H2O.ai
21.115 HarperDB
21.116 Hedvig
21.117 Hitachi Vantara
21.118 Hortonworks
21.119 HPE (Hewlett Packard Enterprise)
21.120 Huawei
21.121 HVR
21.122 HyperScience
21.123 HyTrust
21.124 IBM Corporation
21.125 iDashboards
21.126 IDERA
21.127 Ignite Technologies
21.128 Imanis Data
21.129 Impetus Technologies
21.130 Incorta
21.131 InetSoft Technology Corporation
21.132 InfluxData
21.133 Infogix
21.134 Infor/Birst
21.135 Informatica
21.136 Information Builders
21.137 Infosys
21.138 Infoworks
21.139 Insightsoftware.com
21.140 InsightSquared
21.141 Intel Corporation
21.142 Interana
21.143 InterSystems Corporation
21.144 Jedox
21.145 Jethro
21.146 Jinfonet Software
21.147 Juniper Networks
21.148 KALEAO
21.149 Keen IO
21.150 Keyrus
21.151 Kinetica
21.152 KNIME
21.153 Kognitio
21.154 Kyvos Insights
21.155 LeanXcale
21.156 Lexalytics
21.157 Lexmark International
21.158 Lightbend
21.159 Logi Analytics
21.160 Logical Clocks
21.161 Longview Solutions/Tidemark
21.162 Looker Data Sciences
21.163 LucidWorks
21.164 Luminoso Technologies
21.165 Maana
21.166 Manthan Software Services
21.167 MapD Technologies
21.168 MapR Technologies
21.169 MariaDB Corporation
21.170 MarkLogic Corporation
21.171 Mathworks
21.172 Melissa
21.173 MemSQL
21.174 Metric Insights
21.175 Microsoft Corporation
21.176 MicroStrategy
21.177 Minitab
21.178 MongoDB
21.179 Mu Sigma
21.180 NEC Corporation
21.181 Neo4j
21.182 NetApp
21.183 Nimbix
21.184 Nokia
21.185 NTT Data Corporation
21.186 Numerify
21.187 NuoDB
21.188 NVIDIA Corporation
21.189 Objectivity
21.190 Oblong Industries
21.191 OpenText Corporation
21.192 Opera Solutions
21.193 Optimal Plus
21.194 Oracle Corporation
21.195 Palantir Technologies
21.196 Panasonic Corporation/Arimo
21.197 Panorama Software
21.198 Paxata
21.199 Pepperdata
21.200 Phocas Software
21.201 Pivotal Software
21.202 Prognoz
21.203 Progress Software Corporation
21.204 Provalis Research
21.205 Pure Storage
21.206 PwC (PricewaterhouseCoopers International)
21.207 Pyramid Analytics
21.208 Qlik
21.209 Qrama/Tengu
21.210 Quantum Corporation
21.211 Qubole
21.212 Rackspace
21.213 Radius Intelligence
21.214 RapidMiner
21.215 Recorded Future
21.216 Red Hat
21.217 Redis Labs
21.218 RedPoint Global
21.219 Reltio
21.220 RStudio
21.221 Rubrik/Datos IO
21.222 Ryft
21.223 Sailthru
21.224 Salesforce.com
21.225 Salient Management Company
21.226 Samsung Group
21.227 SAP
21.228 SAS Institute
21.229 ScaleOut Software
21.230 Seagate Technology
21.231 Sinequa
21.232 SiSense
21.233 Sizmek
21.234 SnapLogic
21.235 Snowflake Computing
21.236 Software AG
21.237 Splice Machine
21.238 Splunk
21.239 Strategy Companion Corporation
21.240 Stratio
21.241 Streamlio
21.242 StreamSets
21.243 Striim
21.244 Sumo Logic
21.245 Supermicro (Super Micro Computer)
21.246 Syncsort
21.247 SynerScope
21.248 SYNTASA
21.249 Tableau Software
21.250 Talend
21.251 Tamr
21.252 TARGIT
21.253 TCS (Tata Consultancy Services)
21.254 Teradata Corporation
21.255 Thales/Guavus
21.256 ThoughtSpot
21.257 TIBCO Software
21.258 Toshiba Corporation
21.259 Transwarp
21.260 Trifacta
21.261 Unifi Software
21.262 Unravel Data
21.263 VANTIQ
21.264 VMware
21.265 VoltDB
21.266 WANdisco
21.267 Waterline Data
21.268 Western Digital Corporation
21.269 WhereScape
21.270 WiPro
21.271 Wolfram Research
21.272 Workday
21.273 Xplenty
21.274 Yellowfin BI
21.275 Yseop
21.276 Zendesk
21.277 Zoomdata
21.278 Zucchetti
22 Chapter 22: Conclusion & Strategic Recommendations
22.1 Why is the Market Poised to Grow?
22.2 Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena
22.3 Maturation of AI (Artificial Intelligence): From Machine Learning to Deep Learning
22.4 Blockchain: Impact on Big Data
22.5 The Emergence of Edge Analytics
22.6 Beyond Data Capture & Analytics
22.7 Transforming IT from a Cost Center to a Profit Center
22.8 Can Privacy Implications Hinder Success?
22.9 Maximizing Innovation with Careful Regulation
22.10 Battling Organizational & Data Silos
22.11 Moving Big Data to the Cloud
22.12 Software vs. Hardware Investments
22.13 Vendor Share: Who Leads the Market?
22.14 Big Data Driving Wider IT Industry Investments
22.15 Assessing the Impact of the IoT
22.16 Recommendations
22.16.1 Big Data Hardware, Software & Professional Services Providers
22.16.2 Enterprises
List of Figures
Figure 1: Hadoop Architecture
Figure 2: Reactive vs. Proactive Analytics
Figure 3: Big Data Future Roadmap: 2018 – 2030
Figure 4: Big Data Value Chain
Figure 5: Key Aspects of Big Data Standardization
Figure 6: Global Big Data Revenue: 2018 – 2030 ($ Million)
Figure 7: Global Big Data Revenue by Submarket: 2018 – 2030 ($ Million)
Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2018 – 2030 ($ Million)
Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2018 – 2030 ($ Million)
Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2018 – 2030 ($ Million)
Figure 11: Global Big Data SQL Submarket Revenue: 2018 – 2030 ($ Million)
Figure 12: Global Big Data NoSQL Submarket Revenue: 2018 – 2030 ($ Million)
Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2018 – 2030 ($ Million)
Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2018 – 2030 ($ Million)
Figure 15: Global Big Data Professional Services Submarket Revenue: 2018 – 2030 ($ Million)
Figure 16: Global Big Data Revenue by Vertical Market: 2018 – 2030 ($ Million)
Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2018 – 2030 ($ Million)
Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2018 – 2030 ($ Million)
Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2018 – 2030 ($ Million)
Figure 20: Global Big Data Revenue in the Education Sector: 2018 – 2030 ($ Million)
Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2018 – 2030 ($ Million)
Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2018 – 2030 ($ Million)
Figure 23: Global Big Data Revenue in the Insurance Sector: 2018 – 2030 ($ Million)
Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2018 – 2030 ($ Million)
Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2018 – 2030 ($ Million)
Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2018 – 2030 ($ Million)
Figure 27: Global Big Data Revenue in the Public Services Sector: 2018 – 2030 ($ Million)
Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2018 – 2030 ($ Million)
Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2018 – 2030 ($ Million)
Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2018 – 2030 ($ Million)
Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2018 – 2030 ($ Million)
Figure 32: Big Data Revenue by Region: 2018 – 2030 ($ Million)
Figure 33: Asia Pacific Big Data Revenue: 2018 – 2030 ($ Million)
Figure 34: Asia Pacific Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 35: Australia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 36: China Big Data Revenue: 2018 – 2030 ($ Million)
Figure 37: India Big Data Revenue: 2018 – 2030 ($ Million)
Figure 38: Indonesia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 39: Japan Big Data Revenue: 2018 – 2030 ($ Million)
Figure 40: Malaysia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 41: Pakistan Big Data Revenue: 2018 – 2030 ($ Million)
Figure 42: Philippines Big Data Revenue: 2018 – 2030 ($ Million)
Figure 43: Singapore Big Data Revenue: 2018 – 2030 ($ Million)
Figure 44: South Korea Big Data Revenue: 2018 – 2030 ($ Million)
Figure 45: Taiwan Big Data Revenue: 2018 – 2030 ($ Million)
Figure 46: Thailand Big Data Revenue: 2018 – 2030 ($ Million)
Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2018 – 2030 ($ Million)
Figure 48: Eastern Europe Big Data Revenue: 2018 – 2030 ($ Million)
Figure 49: Eastern Europe Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 50: Czech Republic Big Data Revenue: 2018 – 2030 ($ Million)
Figure 51: Poland Big Data Revenue: 2018 – 2030 ($ Million)
Figure 52: Russia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2018 – 2030 ($ Million)
Figure 54: Latin & Central America Big Data Revenue: 2018 – 2030 ($ Million)
Figure 55: Latin & Central America Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 56: Argentina Big Data Revenue: 2018 – 2030 ($ Million)
Figure 57: Brazil Big Data Revenue: 2018 – 2030 ($ Million)
Figure 58: Mexico Big Data Revenue: 2018 – 2030 ($ Million)
Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2018 – 2030 ($ Million)
Figure 60: Middle East & Africa Big Data Revenue: 2018 – 2030 ($ Million)
Figure 61: Middle East & Africa Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 62: Israel Big Data Revenue: 2018 – 2030 ($ Million)
Figure 63: Qatar Big Data Revenue: 2018 – 2030 ($ Million)
Figure 64: Saudi Arabia Big Data Revenue: 2018 – 2030 ($ Million)
Figure 65: South Africa Big Data Revenue: 2018 – 2030 ($ Million)
Figure 66: UAE Big Data Revenue: 2018 – 2030 ($ Million)
Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2018 – 2030 ($ Million)
Figure 68: North America Big Data Revenue: 2018 – 2030 ($ Million)
Figure 69: North America Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 70: Canada Big Data Revenue: 2018 – 2030 ($ Million)
Figure 71: USA Big Data Revenue: 2018 – 2030 ($ Million)
Figure 72: Western Europe Big Data Revenue: 2018 – 2030 ($ Million)
Figure 73: Western Europe Big Data Revenue by Country: 2018 – 2030 ($ Million)
Figure 74: Denmark Big Data Revenue: 2018 – 2030 ($ Million)
Figure 75: Finland Big Data Revenue: 2018 – 2030 ($ Million)
Figure 76: France Big Data Revenue: 2018 – 2030 ($ Million)
Figure 77: Germany Big Data Revenue: 2018 – 2030 ($ Million)
Figure 78: Italy Big Data Revenue: 2018 – 2030 ($ Million)
Figure 79: Netherlands Big Data Revenue: 2018 – 2030 ($ Million)
Figure 80: Norway Big Data Revenue: 2018 – 2030 ($ Million)
Figure 81: Spain Big Data Revenue: 2018 – 2030 ($ Million)
Figure 82: Sweden Big Data Revenue: 2018 – 2030 ($ Million)
Figure 83: UK Big Data Revenue: 2018 – 2030 ($ Million)
Figure 84: Big Data Revenue in the Rest of Western Europe: 2018 – 2030 ($ Million)
Figure 85: Global Big Data Workload Distribution by Environment: 2018 – 2030 (%)
Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2018 – 2030 ($ Million)
Figure 87: Big Data Vendor Market Share: 2017 (%)
Figure 88: Global IT Expenditure Driven by Big Data Investments: 2018 – 2030 ($ Million)
Figure 89: Global IoT Connections by Access Technology: 2018 – 2030 (Millions)
Figure 1: Hadoop Architecture
Figure 2: Reactive vs. Proactive Analytics
Figure 3: Distribution of Big Data Investments in the Automotive Industry, by Application Area: 2018 (%)
Figure 4: Autonomous Vehicle Generated Data Volume by Sensor (%)
Figure 5: On-Board Sensors in an Autonomous Vehicle
Figure 6: Audi's Enterprise Big Data Platform
Figure 7: Toyota's Smart Center Architecture
Figure 8: Progressive Corporation's Use of Big Data for Automotive Insurance
Figure 9: Big Data Roadmap in the Automotive Industry: 2018 – 2030
Figure 10: Big Data Value Chain in the Automotive Industry
Figure 11: Key Aspects of Big Data Standardization
Figure 12: Global Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 13: Global Big Data Revenue in the Automotive Industry, by Hardware, Software & Professional Services: 2018 – 2030 ($ Million)
Figure 14: Global Big Data Revenue in the Automotive Industry, by Submarket: 2018 – 2030 ($ Million)
Figure 15: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 16: Global Big Data Networking Infrastructure Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 17: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 18: Global Big Data SQL Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 19: Global Big Data NoSQL Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 20: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 21: Global Big Data Cloud Platforms Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 22: Global Big Data Professional Services Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 23: Global Big Data Revenue in the Automotive Industry, by Application Area: 2018 – 2030 ($ Million)
Figure 24: Global Big Data Revenue in Automotive Product Development, Manufacturing & Supply Chain: 2018 – 2030 ($ Million)
Figure 25: Global Big Data Revenue in Automotive After-Sales, Warranty & Dealer Management: 2018 – 2030 ($ Million)
Figure 26: Global Big Data Revenue in Connected Vehicles & Intelligent Transportation: 2018 – 2030 ($ Million)
Figure 27: Global Big Data Revenue in Automotive Marketing, Sales & Other Applications: 2018 – 2030 ($ Million)
Figure 28: Global Big Data Revenue in the Automotive Industry, by Use Case: 2018 – 2030 ($ Million)
Figure 29: Global Big Data Revenue in Automotive Supply Chain Management: 2018 – 2030 ($ Million)
Figure 30: Global Big Data Revenue in Automotive Manufacturing: 2018 – 2030 ($ Million)
Figure 31: Global Big Data Revenue in Automotive Product Design & Planning: 2018 – 2030 ($ Million)
Figure 32: Global Big Data Revenue in Automotive Predictive Maintenance & Real-Time Diagnostics: 2018 – 2030 ($ Million)
Figure 33: Global Big Data Revenue in Automotive Recall & Warranty Management: 2018 – 2030 ($ Million)
Figure 34: Global Big Data Revenue in Automotive Parts Inventory & Pricing Optimization: 2018 – 2030 ($ Million)
Figure 35: Global Big Data Revenue in Automotive Dealer Management & Customer Support Services: 2018 – 2030 ($ Million)
Figure 36: Global Big Data Revenue in UBI (Usage-Based Insurance): 2018 – 2030 ($ Million)
Figure 37: Global Big Data Revenue in Autonomous & Semi-Autonomous Driving: 2018 – 2030 ($ Million)
Figure 38: Global Big Data Revenue in Intelligent Transportation: 2018 – 2030 ($ Million)
Figure 39: Global Big Data Revenue in Fleet Management: 2018 – 2030 ($ Million)
Figure 40: Global Big Data Revenue in Driver Safety & Vehicle Cyber Security: 2018 – 2030 ($ Million)
Figure 41: Global Big Data Revenue in In-Vehicle Experience, Navigation & Infotainment: 2018 – 2030 ($ Million)
Figure 42: Global Big Data Revenue in Ride Sourcing, Sharing & Rentals: 2018 – 2030 ($ Million)
Figure 43: Global Big Data Revenue in Automotive Marketing & Sales: 2018 – 2030 ($ Million)
Figure 44: Global Big Data Revenue in Automotive Customer Retention: 2018 – 2030 ($ Million)
Figure 45: Global Big Data Revenue in Automotive Third Party Monetization: 2018 – 2030 ($ Million)
Figure 46: Global Big Data Revenue in Other Automotive Industry Use Cases: 2018 – 2030 ($ Million)
Figure 47: Big Data Revenue in the Automotive Industry, by Region: 2018 – 2030 ($ Million)
Figure 48: Asia Pacific Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 49: Asia Pacific Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million)
Figure 50: Australia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 51: China Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 52: India Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 53: Indonesia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 54: Japan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 55: Malaysia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 56: Pakistan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 57: Philippines Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 58: Singapore Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 59: South Korea Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 60: Taiwan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 61: Thailand Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 62: Rest of Asia Pacific Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 63: Eastern Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 64: Eastern Europe Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million)
Figure 65: Czech Republic Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 66: Poland Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 67: Russia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 68: Rest of Eastern Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 69: Latin & Central America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 70: Latin & Central America Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million)
Figure 71: Argentina Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 72: Brazil Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 73: Mexico Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 74: Rest of Latin & Central America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 75: Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 76: Middle East & Africa Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million)
Figure 77: Israel Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 78: Qatar Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 79: Saudi Arabia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 80: South Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 81: UAE Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 82: Rest of the Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 83: North America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 84: North America Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million)
Figure 85: Canada Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 86: USA Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 87: Western Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 88: Western Europe Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million)
Figure 89: Denmark Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 90: Finland Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 91: France Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 92: Germany Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 93: Italy Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 94: Netherlands Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 95: Norway Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 96: Spain Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 97: Sweden Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 98: UK Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
Figure 99: Rest of Western Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)
