Big Data Market: 2018-2030: Big Data Vendors will Pocket Over $65 Billion from Hardware, Software and Professional Services Revenues with revenues to Hit $96 Billion by 2021

DUBLIN, June 22, 2018 /PRNewswire/ --

The "The Big Data Market: 2018 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts" report from SNS Telecom & IT has been added to ResearchAndMarkets.com's offering.

These investments are further expected to grow at a CAGR of approximately 14% over the next three years.

The Big Data Market: 2018 - 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 profiles, market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2018 to 2030. The forecasts are segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.

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. The research estimates that Big Data investments will account for over $65 Billion in 2018 alone.

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

Key Findings

• In 2018, Big Data vendors will pocket over $65 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 14% over the next three years, eventually accounting for more than $96 Billion by the end of 2021.
• With ongoing advances in AI (Artificial Intelligence) technologies, Big Data analytics initiatives are beginning to leverage sophisticated deep learning systems with an autonomous sense of judgment - to enable a range of applications from chatbots and virtual assistants to self-driving vehicles and precision medicine.
• In order to analyze data closer to where it is collected, Big Data and advanced analytics technologies are increasingly being integrated into edge environments, including network nodes, numerous industrial machines and IoT (Internet of Things) devices.
• The vendor arena is continuing to consolidate with several prominent M&A deals such as Oracle's recent acquisition of enterprise data science platform provider DataScience.com - in a bid to beef up its capabilities in machine learning and Big Data for predictive analytics, and Google's acquisition of Big Data application platform provider Cask Data.

Key Topics Covered:

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

Chapter 2: An Overview of Big Data
2.1 What is Big Data?
2.2 Key Approaches to Big Data Processing
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Chapter 21: Vendor Landscape

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

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
• ALTEN
• Alteryx
• Altiscale
• Amazon.com
• Ambulance Victoria
• AMD (Advanced Micro Devices)
• Amgen
• Anaconda
• ANSI (American National Standards Institute)
• Antivia
• Apixio
• Arcadia Data
• Arimo
• ARM
• ASF (Apache Software Foundation)
• AstraZeneca
• AT&T
• AtScale
• Attivio
• Attunity
• Automated Insights
• AVORA
• AWS (Amazon Web Services)
• Axiomatics
• Ayasdi
• BackOffice Associates
• BAE Systems
• Baidu
• Bangkok Hospital Group
• Basho Technologies
• BCG (Boston Consulting Group)
• Bedrock Data
• Bet365 Group
• BetterWorks
• Big Panda
• BigML
• Bina Technologies
• Biogen
• Birst
• Bitam
• Blue Medora
• BlueData Software
• BlueTalon
• BMC Software
• BMW
• BOARD International
• Boeing
• Booz Allen Hamilton
• Boxever
• British Gas
• Broadcom
• BT Group
• CACI International
• Cambridge Semantics
• Capgemini
• Capital One Financial Corporation
• Cask Data
• Cazena
• CBA (Commonwealth Bank of Australia)
• Centrifuge Systems
• CenturyLink
• Chartio
• Cisco Systems
• Civis Analytics
• ClearStory Data
• Cloudability
• Cloudera
• Cloudian
• Clustrix
• CognitiveScale
• Collibra
• Concurrent Technology
• Confluent
• Constant Contact
• Contexti
• Coriant
• Couchbase
• Crate.io
• Cray
• Credit Agricole Group
• CSA (Cloud Security Alliance)
• CSCC (Cloud Standards Customer Council)
• Dash Labs
• Data Clairvoyance Group
• Databricks
• DataGravity
• Dataiku
• Datalytyx
• Datameer
• DataRobot
• DataScience.com
• DataStax
• Datawatch Corporation
• Datos IO
• DDN (DataDirect Networks)
• Decisyon
• Dell EMC
• Dell Technologies
• Deloitte
• Demandbase
• Denodo Technologies
• Denso Corporation
• DGSE (General Directorate for External Security, France)
• Dianomic Systems
• Digital Reasoning Systems
• Dimensional Insight
• DMG (Data Mining Group)
• Dolphin Enterprise Solutions Corporation
• Domino Data Lab
• Domo
• Dow Chemical Company
• Dremio
• DriveScale
• Druva
• DT (Deutsche Telekom)
• Dubai Police
• Dundas Data Visualization
• DXC Technology
• eBay
• Edith Cowen University
• Elastic
• Engineering Group (Engineering Ingegneria Informatica)
• EnterpriseDB Corporation
• eQ Technologic
• Ericsson
• Erwin
• EVO (Big Cloud Analytics)
• EXASOL
• EXL (ExlService Holdings)
• Facebook
• FDNY (Fire Department of the City of New York)
• FICO (Fair Isaac Corporation)
• Figure Eight
• FogHorn Systems
• Ford Motor Company
• Fractal Analytics
• Franz
• Fujitsu
• Fuzzy Logix
• Gainsight
• GE (General Electric)
• Glasgow City Council
• Glassbeam
• GoodData Corporation
• Google
• Grakn Labs
• Greenwave Systems
• GridGain Systems
• Groupe Renault
• Guavus
• H2O.ai
• Hanse Orga Group
• HarperDB
• HCL Technologies
• Hedvig
• Hitachi
• Hitachi Vantara
• Honda Motor Company
• Hortonworks
• HPE (Hewlett Packard Enterprise)
• HSBC Group
• Huawei
• HVR
• HyperScience
• HyTrust
• IBM Corporation
• iDashboards
• IDERA
• IEC (International Electrotechnical Commission)
• IEEE (Institute of Electrical and Electronics Engineers)
• Ignite Technologies
• Imanis Data
• Impetus Technologies
• INCITS (InterNational Committee for Information Technology Standards)
• Incorta
• InetSoft Technology Corporation
• Infer
• InfluxData
• Infogix
• Infor
• Informatica
• Information Builders
• Infosys
• Infoworks
• Insightsoftware.com
• InsightSquared
• Intel Corporation
• Interana
• InterSystems Corporation
• ISO (International Organization for Standardization)
• ITU (International Telecommunication Union)
• Jedox
• Jethro
• Jinfonet Software
• JJ Food Service
• JPMorgan Chase & Co.
• Juniper Networks
• Kaiser Permanente
• KALEAO
• Keen IO
• Keyrus
• Kinetica
• KNIME
• Kofax
• Kognitio
• Kyvos Insights
• Lavastorm
• Leadspace
• LeanXcale
• Lexalytics
• Lexmark International
• Lightbend
• Linux Foundation
• Logi Analytics
• Logical Clocks
• 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
• Melissa
• 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
• NEC Corporation
• Neo4j
• NetApp
• Netflix
• Neustar
• New York State Department of Taxation and Finance
• NextBio
• NFL (National Football League)
• Nimbix
• Nokia
• Northwest Analytics
• Nottingham Trent University
• Novartis
• NTT Data Corporation
• NTT Group
• Numerify
• NuoDB
• Nutonian
• NVIDIA Corporation
• OASIS (Organization for the Advancement of Structured Information Standards)
• Objectivity
• 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
• Optum
• OptumLabs
• Oracle Corporation
• OTP Bank
• OVG Real Estate
• Palantir Technologies
• Panasonic Corporation
• Panorama Software
• Paxata
• Pentaho
• Pepperdata
• Pfizer
• Philips
• Phocas Software
• Pivotal Software
• Predixion Software
• Primerica
• Procter & Gamble
• Prognoz
• Progress Software Corporation
• Provalis Research
• Purdue University
• Pure Storage
• PwC (PricewaterhouseCoopers International)
• Pyramid Analytics
• Qlik
• Qrama/Tengu
• Qualcomm
• Quantum Corporation
• Qubole
• Rackspace
• Radius Intelligence
• RapidMiner
• Recorded Future
• Red Hat
• Redis Labs
• RedPoint Global
• Reltio
• Rocket Fuel
• Rosenberger
• Royal Bank of Canada
• Royal Dutch Shell
• Royal Navy
• RSA Group
• RStudio
• Rubrik
• Ryft
• Sailthru
• Salesforce.com
• Salient Management Company
• Samsung Electronics
• Samsung Group
• Samsung SDS
• Sanofi
• SAP
• SAS Institute
• ScaleArc
• ScaleOut Software
• Scaleworks
• Schneider Electric
• SCIO Health Analytics
• Seagate Technology
• Search Technologies
• Siemens
• Sinequa
• SiSense
• Sizmek
• SnapLogic
• Snowflake Computing
• SoftBank Group
• Software AG
• SpagoBI Labs
• Sparkline Data
• Splice Machine
• Splunk
• Sqrrl
• Strategy Companion Corporation
• Stratio
• Streamlio
• StreamSets
• Striim
• Sumo Logic
• Supermicro (Super Micro Computer)
• Syncsort
• SynerScope
• SYNTASA
• T-Mobile USA
• Tableau Software
• Talend
• Tamr
• TARGIT
• TCS (Tata Consultancy Services)
• TEOCO
• Teradata Corporation
• Tesco
• Thales
• The Walt Disney Company
• The Weather Company
• Thomson Reuters
• ThoughtSpot
• TIBCO Software
• Tidemark
• TM Forum
• Toshiba Corporation
• TPC (Transaction Processing Performance Council)
• Transwarp
• Trifacta
• Twitter
• 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. Department of Defense
• U.S. DHS (Department of Homeland Security)
• U.S. ICE (Immigration and Customs Enforcement)
• U.S. NASA (National Aeronautics and Space Administration)
• U.S. NIST (National Institute of Standards and Technology)
• U.S. NSA (National Security Agency)
• Unifi Software
• UnitedHealth Group
• Unravel Data
• USCIS (U.S. Citizenship and Immigration Services)
• VANTIQ
• Vecima Networks
• Verizon Communications
• Vmware
• Vodafone Group
• VoltDB
• W3C (World Wide Web Consortium)
• WANdisco
• Waterline Data
• Wavefront
• Western Digital Corporation
• WhereScape
• WiPro
• Wolfram Research
• Workday
• Xplenty
• Yellowfin BI
• Yseop
• Zendesk
• Zoomdata
• Zucchetti
• Zurich Insurance Group
  

For more information about this report visit https://www.researchandmarkets.com/research/6qgjf8/big_data_market?w=5

Media Contact:

Research and Markets
Laura Wood, Senior Manager
[email protected]   

For E.S.T Office Hours Call +1-917-300-0470
For U.S./CAN Toll Free Call +1-800-526-8630
For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1907
Fax (outside U.S.): +353-1-481-1716

SOURCE Research and Markets

Related Links

http://www.researchandmarkets.com