Big Data Market: 2018-2030 - $65 Billion Opportunities,

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News Desk 2018

Jun 22, 2018 at 6:30 AM 406

Big Data Market: 2018-2030 - $65 Billion Opportunities, Challenges, Strategies, Industry Verticals & Forecasts

Dublin, June 22, 2018 (GLOBE NEWSWIRE) -- 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. Topics Covered: The report covers the following topics:

Big Data ecosystem
Market drivers and barriers
Enabling technologies, standardization and regulatory initiatives
Big Data analytics and implementation models
Key trends - including AI (Artificial Intelligence), machine learning, edge analytics, cloud-based Big Data platforms, and the impact of the IoT (Internet of Things)
Analysis of key applications and investment potential for 14 vertical markets
Over 60 case studies on the use of Big Data and analytics
Big Data vendor market share
Future roadmap and value chain
Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
Strategic recommendations for Big Data hardware, software and professional services vendors, and enterprises
Market analysis and forecasts from 2018 till 2030

Forecast Segmentation: Market forecasts are provided for each of the following submarkets and their subcategories:

Hardware, Software & Professional Services
Hardware
Software
Professional Services

Horizontal Submarkets:

Storage & Compute Infrastructure
Networking Infrastructure
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services

Vertical Submarkets:

Automotive, Aerospace & Transportation
Banking & Securities
Defense & Intelligence
Education
Healthcare & Pharmaceutical
Smart Cities & Intelligent Buildings
Insurance
Manufacturing & Natural Resources
Web, Media & Entertainment
Public Safety & Homeland Security
Public Services
Retail, Wholesale & Hospitality
Telecommunications
Utilities & Energy
Others

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 2021, 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 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 2.4 Market Growth Drivers 2.5 Market Barriers 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 Chapter 4: Big Data in Automotive, Aerospace & Transportation 4.1 Overview & Investment Potential 4.2 Key Applications 4.3 Case Studies Chapter 5: Big Data in Banking & Securities 5.1 Overview & Investment Potential 5.2 Key Applications 5.3 Case Studies Chapter 6: Big Data in Defense & Intelligence 6.1 Overview & Investment Potential 6.2 Key Applications 6.3 Case Studies Chapter 7: Big Data in Education 7.1 Overview & Investment Potential 7.2 Key Applications 7.3 Case Studies Chapter 8: Big Data in Healthcare & Pharma 8.1 Overview & Investment Potential 8.2 Key Applications 8.3 Case Studies Chapter 9: Big Data in Smart Cities & Intelligent Buildings 9.1 Overview & Investment Potential 9.2 Key Applications 9.3 Case Studies Chapter 10: Big Data in Insurance 10.1 Overview & Investment Potential 10.2 Key Applications 10.3 Case Studies Chapter 11: Big Data in Manufacturing & Natural Resources 11.1 Overview & Investment Potential 11.2 Key Applications 11.3 Case Studies Chapter 12: Big Data in Web, Media & Entertainment 12.1 Overview & Investment Potential 12.2 Key Applications 12.3 Case Studies Chapter 13: Big Data in Public Safety & Homeland Security 13.1 Overview & Investment Potential 13.2 Key Applications 13.3 Case Studies Chapter 14: Big Data in Public Services 14.1 Overview & Investment Potential 14.2 Key Applications 14.3 Case Studies Chapter 15: Big Data in Retail, Wholesale & Hospitality 15.1 Overview & Investment Potential 15.2 Key Applications 15.3 Case Studies Chapter 16: Big Data in Telecommunications 16.1 Overview & Investment Potential 16.2 Key Applications 16.3 Case Studies Chapter 17: Big Data in Utilities & Energy 17.1 Overview & Investment Potential 17.2 Key Applications 17.3 Case Studies Chapter 18: Future Roadmap & Value Chain 18.1 Future Roadmap 18.2 The Big Data Value Chain Chapter 19: Standardization & Regulatory Initiatives 19.1 ASF (Apache Software Foundation) 19.2 CSA (Cloud Security Alliance) 19.3 CSCC (Cloud Standards Customer Council) 19.4 DMG (Data Mining Group) 19.5 IEEE (Institute of Electrical and Electronics Engineers) 19.6 INCITS (InterNational Committee for Information Technology Standards) 19.7 ISO (International Organization for Standardization) 19.8 ITU (International Telecommunication Union) 19.9 Linux Foundation 19.10 NIST (National Institute of Standards and Technology) 19.11 OASIS (Organization for the Advancement of Structured Information Standards) 19.12 ODaF (Open Data Foundation) 19.13 ODCA (Open Data Center Alliance) 19.14 OGC (Open Geospatial Consortium) 19.15 TM Forum 19.16 TPC (Transaction Processing Performance Council) 19.17 W3C (World Wide Web Consortium) Chapter 20: Market Sizing & Forecasts 20.1 Global Outlook for the Big Data Market 20.2 Submarket Segmentation 20.3 Vertical Market Segmentation 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

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/w...arket?w=12

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