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The Big Data Market: Business Case, Market Analysis & Forecasts 2015 - 2020

The Big Data Market: Business Case, Market Analysis & Forecasts 2015 - 2020

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 46% between 2015 and 2020. Big Data revenues will reach almost $190 Billion by the end of 2020.

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 with forecasting from 2015 to 2020.

Topics covered in the report:

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
The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
Vendor Assessment and 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 2015 to 2020

All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

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
  • Infrastructure, Software, and Service Vendors
Select Findings:
  • Big Data opens a vast array of applications & opportunities in multiple vertical sectors including not limited to retail & hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government & homeland security and the emerging industrial internet vertical. We see certain verticals leading the way in terms of best practices including optimized data collection, analysis, and reporting.
  • 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 46% between 2015 and 2020. Big Data revenues will reach $190 Billion by the end of 2020.
Report Benefits:
  • Detailed forecasts 2015 – 2020
  • Learn about Big Data technologies
  • Identify leading market segments
  • Identify key players and strategies
  • Identify opportunities in data analytics
  • Understand market drivers and barriers
  • Understand the business case for Big Data
  • Understand regulatory issues and initiatives
Companies and Organizations in Report:
  • 1010Data
  • Accenture
  • Actuate Corporation
  • Adaptive
  • Adobe
  • Amazon
  • Apache Software Foundation
  • APTEAN (Formerly CDC Software)
  • Bank of America
  • Bill & Melinda Gates Foundation
  • Booz Allen Hamilton
  • Bristol Myers Squibb
  • Brooks Brothers
  • CapGemini
  • Carnegie Corporation
  • Centre for Economics and Business Research
  • CIA
  • Cisco Systems
  • Cloud Security Alliance (CSA)
  • Cloud Standard Customer Council
  • Cloudera
  • Computer Science Corporation
  • DARPA
  • Data Direct Network
  • Dell
  • Deliotte
  • EMC
  • Facebook
  • Fujitsu
  • Gartner
  • General Electric
  • General Electric Capital
  • GoodData Corporation
  • Google
  • Guavus
  • Harley Davidson
  • Hitachi Data Systems
  • Hortonworks
  • HP
  • IBM
  • inBloom
  • Informatica
  • Intel
  • International Standards Organization (ISO)
  • International Telecommunications Union (ITU)
  • Jaspersoft
  • JP Morgan Chase
  • Juniper Networks
  • MarkLogic
  • McLaren Racing Team
  • Microsoft
  • MongoDB (Formerly 10Gen)
  • Morgan Stanley
  • MU Sigma
  • Netapp
  • New Classrooms Innovation Partners
  • National Institute of Standards & Technology (NIST)
  • NSA
  • OASIS
  • Open Data Center Alliance
  • Open Data Foundation (ODaF)
  • Opera Solutions
  • Oracle
  • Pentaho
  • Platfora
  • Qliktech
  • Quantum
  • Rackspace
  • Raytheon
  • Renaissance Learning
  • Revolution Analytics
  • Rockwell Automation
  • Salesforce
  • SAP
  • SAS Institute
  • Sherwin Williams
  • Siemans
  • Sisense
  • Software AG/Terracotta
  • Splunk
  • Sqrrl
  • Supermicro
  • Tableau Software
  • Tata Consultancy Services
  • Teradata
  • Think Big Analytics
  • TIBCO
  • Tidemark Systems
  • T-Mobile
  • TomTom
  • Twitter
  • US Federal Government (various agencies and departments)
  • US Xpress
  • VMware (Part of EMC)
  • Vodafone
  • Wipro
  • Zettics


General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


1 Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
1.4 Target Audience
1.5 Companies Mentioned
2 Big Data Technology & Business Case
2.1 Defining Big Data
2.2 Key Characteristics of Big Data
2.2.1 Volume
2.2.2 Variety
2.2.3 Velocity
2.2.4 Variability
2.2.5 Complexity
2.3 Big Data Technology
2.3.1 Hadoop
2.3.2 Other Apache Projects
2.3.3 NoSQL
2.3.3.1 Hbase
2.3.3.2 Cassandra
2.3.3.3 Mongo DB
2.3.3.4 Riak
2.3.3.5 CouchDB
2.3.4 MPP Databases
2.3.5 Others and Emerging Technologies
2.3.5.1 Storm
2.3.5.2 Drill
2.3.5.3 Dremel
2.3.5.4 SAP HANA
2.3.5.5 Gremlin & Giraph
2.3.6 New Paradigms and Techniques
2.3.6.1 Streaming Analytics
2.3.6.2 Cloud Technology
2.3.6.3 Google Search
2.3.6.4 Customize Analytical Tools
2.3.6.5 Internet Keywords
2.3.6.6 Gamification
2.4 Big Data Roadmap
2.5 Market Drivers
2.5.1 Data Volume & Variety
2.5.2 Increasing Adoption of Big Data by Enterprises and Telecom
2.5.3 Maturation of Big Data Software
2.5.4 Continued Investments in Big Data by Web Giants
2.5.5 Business Drivers
2.6 Market Barriers
2.6.1 Privacy and Security: The ‘Big’ Barrier
2.6.2 Workforce Re-skilling and Organizational Resistance
2.6.3 Lack of Clear Big Data Strategies
2.6.4 Technical Challenges: Scalability & Maintenance
2.6.5 Big Data Development Expertise
3 Key Investment Sectors for Big Data
3.1 Industrial Internet and Machine-to-Machine
3.1.1 Big Data in M2M
3.1.2 Vertical Opportunities
3.2 Retail and Hospitality
3.2.1 Improving Accuracy of Forecasts & Stock Management
3.2.2 Determining Buying Patterns
3.2.3 Hospitality Use Cases
3.2.4 Personalized Marketing
3.3 Media
3.3.1 Social Media
3.3.2 Social Gaming Analytics
3.3.3 Usage of Social Media Analytics by Other Verticals
3.3.4 Internet Keyword Search
3.4 Utilities
3.4.1 Analysis of Operational Data
3.4.2 Application Areas for the Future
3.5 Financial Services
3.5.1 Fraud Analysis, Mitigation & Risk Profiling
3.5.2 Merchant-Funded Reward Programs
3.5.3 Customer Segmentation
3.5.4 Customer Retention & Personalized Product Offering
3.5.5 Insurance Companies
3.6 Healthcare and Pharmaceutical
3.6.1 Drug Development
3.6.2 Medical Data Analytics
3.6.3 Case Study: Identifying Heartbeat Patterns
3.7 Telecommunications
3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization
3.7.2 Big Data Analytic Tools
3.7.3 Speech Analytics
3.7.4 New Products and Services
3.8 Government and Homeland Security
3.8.1 Big Data Research
3.8.2 Statistical Analysis
3.8.3 Language Translation
3.8.4 Developing New Applications for the Public
3.8.5 Tracking Crime
3.8.6 Intelligence Gathering
3.8.7 Fraud Detection & Revenue Generation
3.9 Other Sectors
3.9.1 Aviation
3.9.2 Transportation & Logistics: Optimizing Fleet Usage
3.9.3 Sports: Real-Time Processing of Statistics
3.9.4 Education
3.9.5 Manufacturing
4 The Big Data Value Chain
4.1 How Fragmented is the Big Data Value Chain?
4.2 Data Acquisitioning & Provisioning
4.3 Data Warehousing & Business Intelligence
4.4 Analytics & Virtualization
4.5 Actioning and Business Process Management
4.6 Data Governance
5 Big Data Analytics
5.1 What is Big Data Analytics?
5.2 The Importance of Big Data Analytics
5.3 Reactive vs. Proactive Analytics
5.4 Technology and Implementation Approaches
5.4.1 Grid Computing
5.4.2 In-Database processing
5.4.3 In-Memory Analytics
5.4.4 Data Mining
5.4.5 Predictive Analytics
5.4.6 Natural Language Processing
5.4.7 Text Analytics
5.4.8 Visual Analytics
5.4.9 Association rule learning
5.4.10 Classification tree analysis
5.4.11 Machine Learning
5.4.11.1 Neural networks
5.4.11.2 Multilayer Perceptron (MLP)
5.4.11.3 Radial Basis Functions
5.4.11.4 Support vector machines
5.4.11.5 Naïve Bayes
5.4.11.6 k-nearest neighbors
5.4.11.7 Geospatial predictive modelling
5.4.12 Regression Analysis
5.4.13 Social Network Analysis
6 Standardization and Regulatory Initiatives
6.1 Cloud Standards Customer Council – Big Data Working Group
6.2 National Institute of Standards and Technology – Big Data Working Group
6.3 OASIS
6.4 Open Data Foundation
6.5 Open Data Center Alliance
6.6 Cloud Security Alliance – Big Data Working Group
6.7 International Telecommunications Union
6.8 International Organization for Standardization
6.9 International Organization for Standardization)
7 Key Players in the Big Data Market
7.1 Vendor Assessment Matrix
7.2 1010Data
7.3 Actuate Corporation
7.4 Accenture
7.5 Amazon
7.6 Apache Software Foundation
7.7 APTEAN (Formerly CDC Software)
7.8 Booz Allen Hamilton
7.9 Cap Gemini
7.10 Cisco Systems
7.11 Cloudera
7.12 Computer Science Corporation
7.13 DataDirect Network
7.14 Dell
7.15 Deloitte
7.16 EMC
7.17 Facebook
7.18 Fujitsu
7.19 General Electric
7.20 GoodData Corporation
7.21 Google
7.22 Guavus
7.23 Hitachi Data Systems
7.24 Hortonworks
7.25 HP
7.26 IBM
7.27 Informatica
7.28 Intel
7.29 Jaspersoft
7.30 Juniper Networks
7.31 Marklogic
7.32 Microsoft
7.33 MongoDB (Formerly 10Gen)
7.34 MU Sigma
7.35 Netapp
7.36 NTT Data
7.37 Opera Solutions
7.38 Oracle
7.39 Pentaho
7.40 Platfora
7.41 Qliktech
7.42 Quantum
7.43 Rackspace
7.44 Revolution Analytics
7.45 Salesforce
7.46 SAP
7.47 SAS Institute
7.48 Sisense
7.49 Software AG/Terracotta
7.50 Splunk
7.51 Sqrrl
7.52 Supermicro
7.53 Tableau Software
7.54 Tata Consultancy Services
7.55 Teradata
7.56 Think Big Analytics
7.57 TIBCO
7.58 Tidemark Systems
7.59 VMware (Part of EMC)
7.60 Wipro
7.61 Zettics
8 Market Analysis
8.1 Big Data Revenue 2014 - 2020
8.2 Big Data Revenue by Functional Area 2014 - 2020
8.2.1 Supply Chain Management
8.2.2 Business Intelligence
8.2.3 Application Infrastructure & Middleware
8.2.4 Data Integration Tools & Data Quality Tools
8.2.5 Database Management Systems
8.2.6 Big Data Social & Content Analytics
8.2.7 Big Data Storage Management
8.2.8 Big Data Professional Services
8.3 Big Data Revenue by Region 2014 - 2020
8.3.1 Asia Pacific
8.3.2 Eastern Europe
8.3.3 Latin & Central America
8.3.4 Middle East & Africa
8.3.5 North America
8.3.6 Western Europe
Figures
Figure 1: NoSQL vs Legacy DB Performance Comparisons
Figure 2: 2014 Gartner Hype Cycle for Emerging Technologies
Figure 3: Roadmap Big Data Technologies 2014 - 2030
Figure 4: The Big Data Value Chain
Figure 5: Big Data Vendor Ranking Matrix
Figure 6: Big Data Revenue 2013 – 2020
Figure 7: Big Data Revenue by Functional Area 2013 – 2020
Figure 8: Big Data Supply Chain Management Revenue 2013 – 2020
Figure 9: Big Data Supply Business Intelligence Revenue 2013 – 2020
Figure 10: Big Data Application Infrastructure & Middleware Revenue 2013 – 2020
Figure 11: Big Data Integration and Quality Tools Revenue 2013 – 2020
Figure 12: Big Data DB Management Systems Revenue 2013 – 2020
Figure 13: Big Data Social & Content Analytics Revenue 2013 – 2020
Figure 14: Big Data Storage Management Revenue 2013 – 2020
Figure 15: Big Data Professional Services Revenue 2013 – 2020
Figure 16: Big Data Revenue by Region 2013 – 2020
Figure 17: Asia Pacific Big Data Revenue 2013 – 2020
Figure 18: Eastern Europe Big Data Revenue 2013 – 2020
Figure 19: Latin & Central America Big Data Revenue 2013 – 2020
Figure 20: Middle East & Africa Big Data Revenue 2013 – 2020
Figure 21: North America Big Data Revenue 2013 – 2020
Figure 22: Western Europe Big Data Revenue 2013 – 2020

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