The Big Data Market: 2014-2020-Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Published: Jun, 2014 | Pages: 289 | Publisher: SNS Research
Industry: Telecommunications | Report Format: Electronic (PDF)

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 R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for nearly $30 Billion in 2014 alone. These investments are further expected to grow at a CAGR of 17% over the next 6 years.

The “Big Data Market: 2014 – 2020 – 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 market share and strategies.

The report also presents market size forecasts for Big Data hardware, software and professional services from 2014 through to 2020. Historical figures are also presented for 2010, 2011, 2012 and 2013. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 34 countries.

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

 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Historical Revenue & 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 Approaches to Big Data Processing
2.2.1 Hadoop
2.2.2 NoSQL
2.2.3 MPAD (Massively Parallel Analytic Databases)
2.2.4 Others & Analytic Technologies
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: Vertical Opportunities & Use Cases for Big Data
3.1 Automotive, Aerospace & Transportation
3.1.1 Predictive Warranty Analysis
3.1.2 Predictive Aircraft Maintenance & Fuel Optimization
3.1.3 Air Traffic Control
3.1.4 Transport Fleet Optimization
3.2 Banking & Securities
3.2.1 Customer Retention & Personalized Product Offering
3.2.2 Risk Management
3.2.3 Fraud Detection
3.2.4 Credit Scoring
3.3 Defense & Intelligence
3.3.1 Intelligence Gathering
3.3.2 Energy Saving Opportunities in the Battlefield
3.3.3 Preventing Injuries on the Battlefield
3.4 Education
3.4.1 Information Integration
3.4.2 Identifying Learning Patterns
3.4.3 Enabling Student-Directed Learning
3.5 Healthcare & Pharmaceutical
3.5.1 Managing Population Health Efficiently
3.5.2 Improving Patient Care with Medical Data Analytics
3.5.3 Improving Clinical Development & Trials
3.5.4 Improving Time to Market
3.6 Smart Cities & Intelligent Buildings
3.6.1 Energy Optimization & Fault Detection
3.6.2 Intelligent Building Analytics
3.6.3 Urban Transportation Management
3.6.4 Optimizing Energy Production
3.6.5 Water Management
3.6.6 Urban Waste Management
3.7 Insurance
3.7.1 Claims Fraud Mitigation
3.7.2 Customer Retention & Profiling
3.7.3 Risk Management
3.8 Manufacturing & Natural Resources
3.8.1 Asset Maintenance & Downtime Reduction
3.8.2 Quality & Environmental Impact Control
3.8.3 Optimized Supply Chain
3.8.4 Exploration & Identification of Wells & Mines
3.8.5 Maximizing the Potential of Drilling
3.8.6 Production Optimization
3.9 Web, Media & Entertainment
3.9.1 Audience & Advertising Optimization
3.9.2 Channel Optimization
3.9.3 Recommendation Engines
3.9.4 Optimized Search
3.9.5 Live Sports Event Analytics
3.9.6 Outsourcing Big Data Analytics to Other Verticals
3.10 Public Safety & Homeland Security
3.10.1 Cyber Crime Mitigation
3.10.2 Crime Prediction Analytics
3.10.3 Video Analytics & Situational Awareness
3.11 Public Services
3.11.1 Public Sentiment Analysis
3.11.2 Fraud Detection & Prevention
3.11.3 Economic Analysis
3.12 Retail & Hospitality
3.12.1 Customer Sentiment Analysis
3.12.2 Customer & Branch Segmentation
3.12.3 Price Optimization
3.12.4 Personalized Marketing
3.12.5 Optimized Supply Chain
3.13 Telecommunications
3.13.1 Network Performance & Coverage Optimization
3.13.2 Customer Churn Prevention
3.13.3 Personalized Marketing
3.13.4 Location Based Services
3.13.5 Fraud Detection
3.14 Utilities & Energy
3.14.1 Customer Retention
3.14.2 Forecasting Energy
3.14.3 Billing Analytics
3.14.4 Predictive Maintenance
3.14.5 Turbine Placement Optimization
3.15 Wholesale Trade
3.15.1 In-field Sales Analytics
3.15.2 Monitoring the Supply Chain

Chapter 4: Big Data Industry Roadmap & Value Chain
4.1 Big Data Industry Roadmap
4.1.1 2010 – 2013: Initial Hype and the Rise of Analytics
4.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions
4.1.3 2018 – 2020 & Beyond: Large Scale Proliferation of Scalable Machine Learning
4.2 The Big Data Value Chain
4.2.1 Hardware Providers
4.2.1.1 Storage & Compute Infrastructure Providers
4.2.1.2 Networking Infrastructure Providers
4.2.2 Software Providers
4.2.2.1 Hadoop & Infrastructure Software Providers
4.2.2.2 SQL & NoSQL Providers
4.2.2.3 Analytic Platform & Application Software Providers
4.2.2.4 Cloud Platform Providers
4.2.3 Professional Services Providers
4.2.4 End-to-End Solution Providers
4.2.5 Vertical Enterprises

Chapter 5: Big Data Analytics
5.1 What are Big Data Analytics?
5.2 The Importance of Analytics
5.3 Reactive vs. Proactive Analytics
5.4 Customer vs. Operational Analytics
5.5 Technology & Implementation Approaches
5.5.1 Grid Computing
5.5.2 In-Database Processing
5.5.3 In-Memory Analytics
5.5.4 Machine Learning & Data Mining
5.5.5 Predictive Analytics
5.5.6 NLP (Natural Language Processing)
5.5.7 Text Analytics
5.5.8 Visual Analytics
5.5.9 Social Media, IT & Telco Network Analytics
5.6 Vertical Market Case Studies
5.6.1 Amazon – Delivering Cloud Based Big Data Analytics
5.6.2 Facebook – Using Analytics to Monetize Users with Advertising
5.6.3 WIND Mobile – Using Analytics to Monitor Video Quality
5.6.4 Coriant Analytics Services – SaaS Based Big Data Analytics for Telcos
5.6.5 Boeing – Analytics for the Battlefield
5.6.6 The Walt Disney Company – Utilizing Big Data and Analytics in Theme Parks

Chapter 6: Standardization & Regulatory Initiatives
6.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group
6.2 NIST (National Institute of Standards and Technology) – Big Data Working Group
6.3 OASIS –Technical Committees
6.4 ODaF (Open Data Foundation)
6.5 Open Data Center Alliance
6.6 CSA (Cloud Security Alliance) – Big Data Working Group
6.7 ITU (International Telecommunications Union)
6.8 ISO (International Organization for Standardization) and Others

Chapter 7: Market Analysis & Forecasts
7.1 Global Outlook of the Big Data Market
7.2 Submarket Segmentation
7.2.1 Storage and Compute Infrastructure
7.2.2 Networking Infrastructure
7.2.3 Hadoop & Infrastructure Software
7.2.4 SQL
7.2.5 NoSQL
7.2.6 Analytic Platforms & Applications
7.2.7 Cloud Platforms
7.2.8 Professional Services
7.3 Vertical Market Segmentation
7.3.1 Automotive, Aerospace & Transportation
7.3.2 Banking & Securities
7.3.3 Defense & Intelligence
7.3.4 Education
7.3.5 Healthcare & Pharmaceutical
7.3.6 Smart Cities & Intelligent Buildings
7.3.7 Insurance
7.3.8 Manufacturing & Natural Resources
7.3.9 Media & Entertainment
7.3.10 Public Safety & Homeland Security
7.3.11 Public Services
7.3.12 Retail & Hospitality
7.3.13 Telecommunications
7.3.14 Utilities & Energy
7.3.15 Wholesale Trade
7.3.16 Other Sectors
7.4 Regional Outlook
7.5 Asia Pacific
7.5.1 Country Level Segmentation
7.5.2 Australia
7.5.3 China
7.5.4 India
7.5.5 Japan
7.5.6 South Korea
7.5.7 Pakistan
7.5.8 Thailand
7.5.9 Indonesia
7.5.10 Malaysia
7.5.11 Taiwan
7.5.12 Philippines
7.5.13 Singapore
7.5.14 Rest of Asia Pacific
7.6 Eastern Europe
7.6.1 Country Level Segmentation
7.6.2 Czech Republic
7.6.3 Poland
7.6.4 Russia
7.6.5 Rest of Eastern Europe
7.7 Latin & Central America
7.7.1 Country Level Segmentation
7.7.2 Argentina
7.7.3 Brazil
7.7.4 Mexico
7.7.5 Rest of Latin & Central America
7.8 Middle East & Africa
7.8.1 Country Level Segmentation
7.8.2 South Africa
7.8.3 UAE
7.8.4 Qatar
7.8.5 Saudi Arabia
7.8.6 Israel
7.8.7 Rest of the Middle East & Africa
7.9 North America
7.9.1 Country Level Segmentation
7.9.2 USA
7.9.3 Canada
7.10 Western Europe
7.10.1 Country Level Segmentation
7.10.2 Denmark
7.10.3 Finland
7.10.4 France
7.10.5 Germany
7.10.6 Italy
7.10.7 Spain
7.10.8 Sweden
7.10.9 Norway
7.10.10 UK
7.10.11 Rest of Western Europe

Chapter 8: Vendor Landscape
8.1 1010data
8.2 Accenture
8.3 Actian Corporation
8.4 Actuate Corporation
8.5 AeroSpike
8.6 Alpine Data Labs
8.7 Alteryx
8.8 AWS (Amazon Web Services)
8.9 Attivio
8.10 Basho
8.11 Booz Allen Hamilton
8.12 InfiniDB
8.13 Capgemini
8.14 Cellwize
8.15 CenturyLink
8.16 Cisco Systems
8.17 Cloudera
8.18 Comptel
8.19 Contexti
8.20 Couchbase
8.21 CSC (Computer Science Corporation)
8.22 Datameer
8.23 DataStax
8.24 DDN (DataDirect Network)
8.25 Dell
8.26 Deloitte
8.27 Digital Reasoning
8.28 EMC Corporation
8.29 Facebook
8.30 Fractal Analytics
8.31 Fujitsu
8.32 Fusion-io
8.33 GE (General Electric)
8.34 GoodData Corporation
8.35 Google
8.36 Guavus
8.37 HDS (Hitachi Data Systems)
8.38 Hortonworks
8.39 HP
8.40 IBM
8.41 Informatica Corporation
8.42 Information Builders
8.43 Intel
8.44 Jaspersoft
8.45 Juniper Networks
8.46 Kognitio
8.47 Lavastorm Analytics
8.48 LucidWorks
8.49 MapR
8.50 MarkLogic
8.51 Microsoft
8.52 MicroStrategy
8.53 MongoDB (formerly 10gen)
8.54 Mu Sigma
8.55 NTT Data
8.56 Neo Technology
8.57 NetApp
8.58 Opera Solutions
8.59 Oracle
8.60 Palantir Technologies
8.61 ParStream
8.62 Pentaho
8.63 Platfora
8.64 Pivotal Software
8.65 PwC
8.66 QlikTech
8.67 Quantum Corporation
8.68 Rackspace
8.69 RainStor
8.70 Revolution Analytics
8.71 Salesforce.com
8.72 Sailthru
8.73 SAP
8.74 SAS Institute
8.75 SGI
8.76 SiSense
8.77 Software AG/Terracotta
8.78 Splunk
8.79 Sqrrl
8.80 Supermicro
8.81 Tableau Software
8.82 Talend
8.83 TCS (Tata Consultancy Services)
8.84 Teradata
8.85 Think Big Analytics
8.86 TIBCO Software
8.87 Tidemark
8.88 VMware (EMC Subsidiary)
8.89 WiPro
8.90 Zettics

Chapter 9: Expert Opinion – Interview Transcripts
9.1 Comptel
9.2 Lavastorm Analytics
9.3 ParStream
9.4 Sailthru

Chapter 10: Conclusion & Strategic Recommendations
10.1 Big Data Technology: Beyond Data Capture & Analytics
10.2 Transforming IT from a Cost Center to a Profit Center
10.3 Can Privacy Implications Hinder Success?
10.4 Will Regulation have a Negative Impact on Big Data Investments?
10.5 Battling Organization & Data Silos
10.6 Software vs. Hardware Investments
10.7 Vendor Share: Who Leads the Market?
10.8 Big Data Driving Wider IT Industry Investments
10.9 Assessing the Impact of IoT & M2M
10.10 Recommendations
10.10.1 Big Data Hardware, Software & Professional Services Providers
10.10.2 Enterprises