Welcome!

AJAX & REA Authors: Liz McMillan, Elizabeth White, ChandraShekar Dattatreya, David H Deans, Pat Romanski

News Feed Item

Big Data Market: Business Case, Market Analysis and Forecasts 2014 - 2019

LONDON, Jan. 8, 2014 /PRNewswire/ -- Reportbuyer.com just published a new market research report:

Big Data Market: Business Case, Market Analysis and Forecasts 2014 - 2019


Overview:

Big Data refers to a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. While the presence of such datasets is not something new, the past few years have witnessed immense commercial investments in solutions that address the processing and analysis of Big Data.

Big Data opens a vast array of applications and opportunities in multiple vertical sectors including, but not limited to, retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical.

Despite challenges, such as the lack of clear big data strategies, security concerns and the need for workforce re-skilling, the growth potential of Big Data is unprecedented. Mind Commerce estimates that global spending on Big Data will grow at a CAGR of 48% between 2014 and 2019. Big Data revenues will reach $135 Billion by the end of 2019.

This report provides an in-depth assessment of the global Big Data market, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry from 2013 to 2019.

Topics covered in the report:

The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
Big Data Technology: A review of the underlying technologies that resolve big data complexities
Big Data Use Cases: A review of investments sectors and specific use cases for the Big Data market
The Big Data Value Chain: An analysis of the value chain of Big Data and the major players involved within it
Vendor Assessment & Key Player Profiles: An assessment of the vendor landscape of leading players within the Big Data market
Market Analysis and Forecasts: A global and regional assessment of the market size and forecasts for the Big Data market from 2014 to 2019

Key Findings:

Big Data opens a vast array of applications and opportunities in multiple vertical sectors including, but not limited to, retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical.
Mind Commerce has determined that IBM leads the Big Data market in terms of current investments (from a vendor perspective), with estimated revenue for $1.3 Billion in 2012 for its Big Data services, software and hardware sale
Despite challenges such as the lack of clear big data strategies, security concerns and the need for workforce re-skilling, the growth potential of Big Data is unprecedented. Mind Commerce estimates that global spending on Big Data will grow at a CAGR of 48% between 2014 and 2019. Big Data revenues will reach $135 Billion by the end of 2019

Companies in Report:

Accenture
Adaptive
Adobe
Amazon
Apache Software Foundation
APTEAN (Formerly CDC Software)
BoA
Bristol Myers Squibb
Brooks Brothers
Centre for Economics and Business Research
CIA
Cisco Systems
Cloud Security Alliance (CSA)
Cloudera
Dell
EMC
Facebook
Facebook
GoodData Corporation
Google
Google
Guavus
Hitachi Data Systems
Hortonworks
HP
IBM
Informatica
Intel
Jaspersoft
JPMC
McLaren
Microsoft
MongoDB (Formerly 10Gen)
Morgan Stanley
MU Sigma
Netapp
NSA
Opera Solutions
Oracle
Pentaho
Platfora
Qliktech
Quantum
Rackspace
Revolution Analytics
Salesforce
SAP
SAS Institute
Sisense
Software AG/Terracotta
Splunk
Sqrrl
Supermicro
Tableau Software
Teradata
Think Big Analytics
Tidemark Systems
T-Mobile
TomTom
US Xpress
VMware (Part of EMC)
Vodafone

Target Audience:

Investment Firms
Media Companies
Utilities Companies
Financial Institutions
Application Developers
Government Organizations
Retail & Hospitality Companies
Other Vertical Industry Players
Analytics and Data Reporting Companies
Healthcare Service Providers & Institutions
Fixed and Mobile Telecom service providers
Big Data Technology/Solution (Infrastructure, Software, Service) Vendors
1 Chapter 1: Introduction 8
1.1 Executive Summary 8
1.2 Topics Covered 9
1.3 Key Findings 10
1.4 Target Audience 11
1.5 Companies Mentioned 12
2 Chapter 2: Big Data Technology & Business Case 15
2.1 Defining Big Data 15
2.2 Key Characteristics of Big Data 15
2.2.1 Volume 15
2.2.2 Variety 16
2.2.3 Velocity 16
2.2.4 Variability 16
2.2.5 Complexity 16
2.3 Big Data Technology 17
2.3.1 Hadoop 17
2.3.1.1 MapReduce 17
2.3.1.2 HDFS 17
2.3.1.3 Other Apache Projects 18
2.3.2 NoSQL 18
2.3.2.1 Hbase 18
2.3.2.2 Cassandra 18
2.3.2.3 Mongo DB 18
2.3.2.4 Riak 19
2.3.2.5 CouchDB 19
2.3.3 MPP Databases 19
2.3.4 Others and Emerging Technologies 20
2.3.4.1 Storm 20
2.3.4.2 Drill 20
2.3.4.3 Dremel 20
2.3.4.4 SAP HANA 20
2.3.4.5 Gremlin & Giraph 20
2.4 Market Drivers 21
2.4.1 Data Volume & Variety 21
2.4.2 Increasing Adoption of Big Data by Enterprises & Telcos 21
2.4.3 Maturation of Big Data Software 21
2.4.4 Continued Investments in Big Data by Web Giants 21
2.5 Market Barriers 22
2.5.1 Privacy & Security: The 'Big' Barrier 22
2.5.2 Workforce Re-skilling & Organizational Resistance 22
2.5.3 Lack of Clear Big Data Strategies 23
2.5.4 Technical Challenges: Scalability & Maintenance 23
3 Chapter 3: Key Investment Sectors for Big Data 24
3.1 Industrial Internet & M2M 24
3.1.1 Big Data in M2M 24
3.1.2 Vertical Opportunities 24
3.2 Retail & Hospitality 25
3.2.1 Improving Accuracy of Forecasts & Stock Management 25
3.2.2 Determining Buying Patterns 25
3.2.3 Hospitality Use Cases 25
3.3 Media 26
3.3.1 Social Media 26
3.3.2 Social Gaming Analytics 26
3.3.3 Usage of Social Media Analytics by Other Verticals 26
3.4 Utilities 27
3.4.1 Analysis of Operational Data 27
3.4.2 Application Areas for the Future 27
3.5 Financial Services 27
3.5.1 Fraud Analysis & Risk Profiling 27
3.5.2 Merchant-Funded Reward Programs 27
3.5.3 Customer Segmentation 28
3.5.4 Insurance Companies 28
3.6 Healthcare & Pharmaceutical 28
3.6.1 Drug Development 28
3.6.2 Medical Data Analytics 28
3.6.3 Case Study: Identifying Heartbeat Patterns 28
3.7 Telcos 29
3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization 29
3.7.2 Speech Analytics 29
3.7.3 Other Use Cases 29
3.8 Government & Homeland Security 30
3.8.1 Developing New Applications for the Public 30
3.8.2 Tracking Crime 30
3.8.3 Intelligence Gathering 30
3.8.4 Fraud Detection & Revenue Generation 30
3.9 Other Sectors 31
3.9.1 Aviation: Air Traffic Control 31
3.9.2 Transportation & Logistics: Optimizing Fleet Usage 31
3.9.3 Sports: Real-Time Processing of Statistics 31
4 Chapter 4: The Big Data Value Chain 32
4.1 How Fragmented is the Big Data Value Chain? 32
4.2 Data Acquisitioning & Provisioning 33
4.3 Data Warehousing & Business Intelligence 33
4.4 Analytics & Virtualization 33
4.5 Actioning & Business Process Management (BPM) 34
4.6 Data Governance 34
5 Chapter 5: Key Players in the Big Data Market 35
5.1 Vendor Assessment Matrix 35
5.2 Apache Software Foundation 36
5.3 Accenture 36
5.4 Amazon 36
5.5 APTEAN (Formerly CDC Software) 37
5.6 Cisco Systems 37
5.7 Cloudera 37
5.8 Dell 37
5.9 EMC 38
5.10 Facebook 38
5.11 GoodData Corporation 38
5.12 Google 38
5.13 Guavus 39
5.14 Hitachi Data Systems 39
5.15 Hortonworks 39
5.16 HP 40
5.17 IBM 40
5.18 Informatica 40
5.19 Intel 40
5.20 Jaspersoft 41
5.21 Microsoft 41
5.22 MongoDB (Formerly 10Gen) 41
5.23 MU Sigma 42
5.24 Netapp 42
5.25 Opera Solutions 42
5.26 Oracle 42
5.27 Pentaho 43
5.28 Platfora 43
5.29 Qliktech 43
5.30 Quantum 44
5.31 Rackspace 44
5.32 Revolution Analytics 44
5.33 Salesforce 45
5.34 SAP 45
5.35 SAS Institute 45
5.36 Sisense 45
5.37 Software AG/Terracotta 46
5.38 Splunk 46
5.39 Sqrrl 46
5.40 Supermicro 47
5.41 Tableau Software 47
5.42 Teradata 47
5.43 Think Big Analytics 48
5.44 Tidemark Systems 48
5.45 VMware (Part of EMC) 48
6 Chapter 6: Market Analysis 49
6.1 Big Data Revenue: 2014 - 2019 49
6.2 Big Data Revenue by Functional Area: 2014 - 2019 50
6.2.1 Supply Chain Management 51
6.2.2 Business Intelligence 52
6.2.3 Application Infrastructure & Middleware 53
6.2.4 Data Integration Tools & Data Quality Tools 54
6.2.5 Database Management Systems 55
6.2.6 Big Data Social & Content Analytics 56
6.2.7 Big Data Storage Management 57
6.2.8 Big Data Professional Services 58
6.3 Big Data Revenue by Region 2014 - 2019 59
6.3.1 Asia Pacific 60
6.3.2 Eastern Europe 61
6.3.3 Latin & Central America 62
6.3.4 Middle East & Africa 63
6.3.5 North America 64
6.3.6 Western Europe 65

List of Figures

Figure 1: The Big Data Value Chain 32
Figure 2: Big Data Vendor Ranking Matrix 2013 35
Figure 3: Big Data Revenue: 2013 - 2019 ($ Million) 49
Figure 4: Big Data Revenue by Functional Area: 2013 - 2019 ($ Million) 50
Figure 5: Big Data Supply Chain Management Revenue: 2013 - 2019 ($ Million) 51
Figure 6: Big Data Supply Business Intelligence Revenue: 2013 - 2019 ($ Million) 52
Figure 7: Big Data Application Infrastructure & Middleware Revenue: 2013 - 2019 ($ Million) 53
Figure 8: Big Data Integration Tools & Data Quality Tools Revenue: 2013 - 2019 ($ Million) 54
Figure 9: Big Data Database Management Systems Revenue: 2013 - 2019 ($ Million) 55
Figure 10: Big Data Social & Content Analytics Revenue: 2013 - 2019 ($ Million) 56
Figure 11: Big Data Storage Management Revenue: 2013 - 2019 ($ Million) 57
Figure 12: Big Data Professional Services Revenue: 2013 - 2019 ($ Million) 58
Figure 13: Big Data Revenue by Region: 2013 - 2019 ($ Million) 59
Figure 14: Asia Pacific Big Data Revenue: 2013 - 2019 ($ Million) 60
Figure 15: Eastern Europe Big Data Revenue: 2013 - 2019 ($ Million) 61
Figure 16: Latin & Central America Big Data Revenue: 2013 - 2019 ($ Million) 62
Figure 17: Middle East & Africa Big Data Revenue: 2013 - 2019 ($ Million) 63
Figure 18: North America Big Data Revenue: 2013 - 2019 ($ Million) 64
Figure 19: Western Europe Big Data Revenue: 2013 - 2019 ($ Million) 65


Read the full report:
Big Data Market: Business Case, Market Analysis and Forecasts 2014 - 2019
http://www.reportbuyer.com/business_government/outsourcing_bpo/big_data_market_business_case_market_analysis_forecasts_2014_2019.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=Business_Outsourcing


For more information:
Sarah Smith
Research Advisor at Reportbuyer.com
Email: [email protected]
Tel: +44 208 816 85 48
Website: www.reportbuyer.com

SOURCE ReportBuyer

More Stories By PR Newswire

Copyright © 2007 PR Newswire. All rights reserved. Republication or redistribution of PRNewswire content is expressly prohibited without the prior written consent of PRNewswire. PRNewswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

@CloudExpo Stories
How do APIs and IoT relate? The answer is not as simple as merely adding an API on top of a dumb device, but rather about understanding the architectural patterns for implementing an IoT fabric. There are typically two or three trends: Exposing the device to a management framework Exposing that management framework to a business centric logic Exposing that business layer and data to end users. This last trend is the IoT stack, which involves a new shift in the separation of what stuff happe...
"Matrix is an ambitious open standard and implementation that's set up to break down the fragmentation problems that exist in IP messaging and VoIP communication," explained John Woolf, Technical Evangelist at Matrix, in this SYS-CON.tv interview at @ThingsExpo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Connected devices and the Internet of Things are getting significant momentum in 2014. In his session at Internet of @ThingsExpo, Jim Hunter, Chief Scientist & Technology Evangelist at Greenwave Systems, examined three key elements that together will drive mass adoption of the IoT before the end of 2015. The first element is the recent advent of robust open source protocols (like AllJoyn and WebRTC) that facilitate M2M communication. The second is broad availability of flexible, cost-effective ...
Cultural, regulatory, environmental, political and economic (CREPE) conditions over the past decade are creating cross-industry solution spaces that require processes and technologies from both the Internet of Things (IoT), and Data Management and Analytics (DMA). These solution spaces are evolving into Sensor Analytics Ecosystems (SAE) that represent significant new opportunities for organizations of all types. Public Utilities throughout the world, providing electricity, natural gas and water,...
The Internet of Things will put IT to its ultimate test by creating infinite new opportunities to digitize products and services, generate and analyze new data to improve customer satisfaction, and discover new ways to gain a competitive advantage across nearly every industry. In order to help corporate business units to capitalize on the rapidly evolving IoT opportunities, IT must stand up to a new set of challenges. In his session at @ThingsExpo, Jeff Kaplan, Managing Director of THINKstrateg...
DevOps is all about agility. However, you don't want to be on a high-speed bus to nowhere. The right DevOps approach controls velocity with a tight feedback loop that not only consists of operational data but also incorporates business context. With a business context in the decision making, the right business priorities are incorporated, which results in a higher value creation. In his session at DevOps Summit, Todd Rader, Solutions Architect at AppDynamics, discussed key monitoring techniques...
Want to enable self-service provisioning of application environments in minutes that mirror production? Can you automatically provide rich data with code-level detail back to the developers when issues occur in production? In his session at DevOps Summit, David Tesar, Microsoft Technical Evangelist on Microsoft Azure and DevOps, will discuss how to accomplish this and more utilizing technologies such as Microsoft Azure, Visual Studio online, and Application Insights in this demo-heavy session.
When an enterprise builds a hybrid IaaS cloud connecting its data center to one or more public clouds, security is often a major topic along with the other challenges involved. Security is closely intertwined with the networking choices made for the hybrid cloud. Traditional networking approaches for building a hybrid cloud try to kludge together the enterprise infrastructure with the public cloud. Consequently this approach requires risky, deep "surgery" including changes to firewalls, subnets...
The Internet of Things will greatly expand the opportunities for data collection and new business models driven off of that data. In her session at @ThingsExpo, Esmeralda Swartz, CMO of MetraTech, discussed how for this to be effective you not only need to have infrastructure and operational models capable of utilizing this new phenomenon, but increasingly service providers will need to convince a skeptical public to participate. Get ready to show them the money!
One of the biggest challenges when developing connected devices is identifying user value and delivering it through successful user experiences. In his session at Internet of @ThingsExpo, Mike Kuniavsky, Principal Scientist, Innovation Services at PARC, described an IoT-specific approach to user experience design that combines approaches from interaction design, industrial design and service design to create experiences that go beyond simple connected gadgets to create lasting, multi-device exp...
P2P RTC will impact the landscape of communications, shifting from traditional telephony style communications models to OTT (Over-The-Top) cloud assisted & PaaS (Platform as a Service) communication services. The P2P shift will impact many areas of our lives, from mobile communication, human interactive web services, RTC and telephony infrastructure, user federation, security and privacy implications, business costs, and scalability. In his session at @ThingsExpo, Robin Raymond, Chief Architect...
High-performing enterprise Software Quality Assurance (SQA) teams validate systems that are ready for use - getting most actively involved as components integrate and form complete systems. These teams catch and report on defects, making sure the customer gets the best software possible. SQA teams have leveraged automation and virtualization to execute more thorough testing in less time - bringing Dev and Ops together, ensuring production readiness. Does the emergence of DevOps mean the end of E...
Scott Jenson leads a project called The Physical Web within the Chrome team at Google. Project members are working to take the scalability and openness of the web and use it to talk to the exponentially exploding range of smart devices. Nearly every company today working on the IoT comes up with the same basic solution: use my server and you'll be fine. But if we really believe there will be trillions of these devices, that just can't scale. We need a system that is open a scalable and by using ...
The Internet of Things is tied together with a thin strand that is known as time. Coincidentally, at the core of nearly all data analytics is a timestamp. When working with time series data there are a few core principles that everyone should consider, especially across datasets where time is the common boundary. In his session at Internet of @ThingsExpo, Jim Scott, Director of Enterprise Strategy & Architecture at MapR Technologies, discussed single-value, geo-spatial, and log time series dat...
"Verizon offers public cloud, virtual private cloud as well as private cloud on-premises - many different alternatives. Verizon's deep knowledge in applications and the fact that we are responsible for applications that make call outs to other systems. Those systems and those resources may not be in Verizon Cloud, we understand at the end of the day it's going to be federated," explained Anne Plese, Senior Consultant, Cloud Product Marketing at Verizon Enterprise, in this SYS-CON.tv interview at...
"For the past 4 years we have been working mainly to export. For the last 3 or 4 years the main market was Russia. In the past year we have been working to expand our footprint in Europe and the United States," explained Andris Gailitis, CEO of DEAC, in this SYS-CON.tv interview at Cloud Expo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
The Domain Name Service (DNS) is one of the most important components in networking infrastructure, enabling users and services to access applications by translating URLs (names) into IP addresses (numbers). Because every icon and URL and all embedded content on a website requires a DNS lookup loading complex sites necessitates hundreds of DNS queries. In addition, as more internet-enabled ‘Things' get connected, people will rely on DNS to name and find their fridges, toasters and toilets. Acco...
The term culture has had a polarizing effect among DevOps supporters. Some propose that culture change is critical for success with DevOps, but are remiss to define culture. Some talk about a DevOps culture but then reference activities that could lead to culture change and there are those that talk about culture change as a set of behaviors that need to be adopted by those in IT. There is no question that businesses successful in adopting a DevOps mindset have seen departmental culture change, ...
"Cloud consumption is something we envision at Solgenia. That is trying to let the cloud spread to the user as a consumption, as utility computing. We want to allow the people to just pay for what they use, not a subscription model," explained Ermanno Bonifazi, CEO & Founder of Solgenia, in this SYS-CON.tv interview at Cloud Expo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Enthusiasm for the Internet of Things has reached an all-time high. In 2013 alone, venture capitalists spent more than $1 billion dollars investing in the IoT space. With "smart" appliances and devices, IoT covers wearable smart devices, cloud services to hardware companies. Nest, a Google company, detects temperatures inside homes and automatically adjusts it by tracking its user's habit. These technologies are quickly developing and with it come challenges such as bridging infrastructure gaps,...