Welcome!

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.

Latest Stories
Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development ...
What sort of WebRTC based applications can we expect to see over the next year and beyond? One way to predict development trends is to see what sorts of applications startups are building. In his session at @ThingsExpo, Arin Sime, founder of WebRTC.ventures, will discuss the current and likely future trends in WebRTC application development based on real requests for custom applications from real customers, as well as other public sources of information,
Virtualization over the past years has become a key strategy for IT to acquire multi-tenancy, increase utilization, develop elasticity and improve security. And virtual machines (VMs) are quickly becoming a main vehicle for developing and deploying applications. The introduction of containers seems to be bringing another and perhaps overlapped solution for achieving the same above-mentioned benefits. Are a container and a virtual machine fundamentally the same or different? And how? Is one techn...
SYS-CON Events announced today that Infranics will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Since 2000, Infranics has developed SysMaster Suite, which is required for the stable and efficient management of ICT infrastructure. The ICT management solution developed and provided by Infranics continues to add intelligence to the ICT infrastructure through the IMC (Infra Management Cycle) based on mathemat...
SYS-CON Events announced today that Interoute, owner-operator of one of Europe's largest networks and a global cloud services platform, has been named “Bronze Sponsor” of SYS-CON's 20th Cloud Expo, which will take place on June 6-8, 2017 at the Javits Center in New York, New York. Interoute is the owner-operator of one of Europe's largest networks and a global cloud services platform which encompasses 12 data centers, 14 virtual data centers and 31 colocation centers, with connections to 195 add...
SYS-CON Events announced today that Cloudistics, an on-premises cloud computing company, has been named “Bronze Sponsor” of SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Cloudistics delivers a complete public cloud experience with composable on-premises infrastructures to medium and large enterprises. Its software-defined technology natively converges network, storage, compute, virtualization, and management into a ...
Now that the world has connected “things,” we need to build these devices as truly intelligent in order to create instantaneous and precise results. This means you have to do as much of the processing at the point of entry as you can: at the edge. The killer use cases for IoT are becoming manifest through AI engines on edge devices. An autonomous car has this dual edge/cloud analytics model, producing precise, real-time results. In his session at @ThingsExpo, John Crupi, Vice President and Eng...
What if you could build a web application that could support true web-scale traffic without having to ever provision or manage a single server? Sounds magical, and it is! In his session at 20th Cloud Expo, Chris Munns, Senior Developer Advocate for Serverless Applications at Amazon Web Services, will show how to build a serverless website that scales automatically using services like AWS Lambda, Amazon API Gateway, and Amazon S3. We will review several frameworks that can help you build serverle...
In the enterprise today, connected IoT devices are everywhere – both inside and outside corporate environments. The need to identify, manage, control and secure a quickly growing web of connections and outside devices is making the already challenging task of security even more important, and onerous. In his session at @ThingsExpo, Rich Boyer, CISO and Chief Architect for Security at NTT i3, will discuss new ways of thinking and the approaches needed to address the emerging challenges of securit...
The taxi industry never saw Uber coming. Startups are a threat to incumbents like never before, and a major enabler for startups is that they are instantly “cloud ready.” If innovation moves at the pace of IT, then your company is in trouble. Why? Because your data center will not keep up with frenetic pace AWS, Microsoft and Google are rolling out new capabilities In his session at 20th Cloud Expo, Don Browning, VP of Cloud Architecture at Turner, will posit that disruption is inevitable for c...
SYS-CON Events announced today that Loom Systems will exhibit at SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Founded in 2015, Loom Systems delivers an advanced AI solution to predict and prevent problems in the digital business. Loom stands alone in the industry as an AI analysis platform requiring no prior math knowledge from operators, leveraging the existing staff to succeed in the digital era. With offices in S...
There are 66 million network cameras capturing terabytes of data. How did factories in Japan improve physical security at the facilities and improve employee productivity? Edge Computing reduces possible kilobytes of data collected per second to only a few kilobytes of data transmitted to the public cloud every day. Data is aggregated and analyzed close to sensors so only intelligent results need to be transmitted to the cloud. Non-essential data is recycled to optimize storage.
In his General Session at 16th Cloud Expo, David Shacochis, host of The Hybrid IT Files podcast and Vice President at CenturyLink, investigated three key trends of the “gigabit economy" though the story of a Fortune 500 communications company in transformation. Narrating how multi-modal hybrid IT, service automation, and agile delivery all intersect, he will cover the role of storytelling and empathy in achieving strategic alignment between the enterprise and its information technology.
"I think that everyone recognizes that for IoT to really realize its full potential and value that it is about creating ecosystems and marketplaces and that no single vendor is able to support what is required," explained Esmeralda Swartz, VP, Marketing Enterprise and Cloud at Ericsson, in this SYS-CON.tv interview at @ThingsExpo, held June 7-9, 2016, at the Javits Center in New York City, NY.
As businesses adopt functionalities in cloud computing, it’s imperative that IT operations consistently ensure cloud systems work correctly – all of the time, and to their best capabilities. In his session at @BigDataExpo, Bernd Harzog, CEO and founder of OpsDataStore, will present an industry answer to the common question, “Are you running IT operations as efficiently and as cost effectively as you need to?” He will expound on the industry issues he frequently came up against as an analyst, and...