FDI (Foreign Direct Investment) Perspective Of Big Data Technology Market 2017 In-depth Analysis and Detail Forecast Study to 2022

Investment in Big Data Technologies continues to expand, and this trend is to continue as more than 70% of the organizations around the world have either already invested or are planning to invest in the technology in the next few years. 2013, which was considered to be the year of experimentation and early deployment of the technology, had only 31% adoption; in 2015, the technology has grown to $ XX billion at a CAGR of XX % and is estimated to touch $ XX Billion by the end of the decade. Though the technology had a late and slow start, it has now accelerated due to an explosion of data, both structured and complex. A drastic improvement in algorithm development and machine learning, and the need for customer analytics are making big corporate bodies shift focus towards investing in Analytics. This is purely for making more informed decisions, minimizing operational errors and uncertainties, and efficiently utilizing resources. The change in the market trends indicates the evident shift to business opportunities in digital entrepreneurship, both on the supply and the demand side.

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Big Data is defined as a continuous increase in data and the technologies required for collection, storing, managing, manipulating and analysing this ever increasing volume of data. It can be characterized on the basis of its volume, variety, velocity and value. Its role is more dominant in the Marketing unit of an organization, followed by its applicability in Operations, IT and Sales.

The technology has some major constraints such as cultural barriers, organizational and structural barriers, high cost of initial investment, lack of technical expertise, data security concerns and uncertainties in strategies etc.

The market can be segmented on the basis of deployment (On-Premise and Cloud), approach (Hadoop, NoSQL, MPAD and others), verticals (Telecom and IT, Financial Services, Energy and Power, Retail, Tourism, Manufacturing, Engineering and Construction, Aerospace and Defense, Media and Entertainment, Healthcare and others) with sub-verticals, regional market and specific country market based on the rates of adoption.

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The technology though widely accepted in North America (more specifically in the U.S) shows maximum growth is the Asia-Pacific (APAC) region with countries such as India, China, France, Brazil, Italy, Mexico, U.K, Russia and Spain indicating maximum adoption and minimum gap in skill and technology in the countries.

Giants in the IT market domain such as IBM, Microsoft, Oracle, SAP, Cisco and SAS have invested heavily in global data centres, developing services and acquiring software companies that specialize in business intelligence tools.

What the report offers
1. Market Definition for the specified topic along with identification of key drivers and restraints for the market.
2. Market analysis for the Big Data Analytics Market, with region specific assessments and competition analysis on a global and regional scale.
3. Identification of factors instrumental in changing the market scenarios, rising prospective opportunities and identification of key companies which can influence the market on a global and regional scale.
4. Extensively researched competitive landscape section with profiles of major companies along with their strategic initiatives and market shares.
5. Identification and analysis of the Macro and Micro factors that affect the Big Data Analytics Market on both global and regional scale.
6. A comprehensive list of key market players along with the analysis of their current strategic interests and key financial information.

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Table of Content
1. Introduction
1.1 Big Data technology
1.2 Characteristics of Big Data
1.2.1 Volume
1.2.2 Variety
1.2.3 Velocity
1.2.4 Value
1.3 Big Data Adoption
1.4 Role of Big Data in Business Units
1.4.1 Marketing
1.4.2 Operations
1.4.3 IT
1.4.4 Sales
1.4.5 Finance
1.4.6 Others

2. Scope of Report

3. Market Overview
3.1 Introduction
3.2 Supply-Side Analysis
3.3 Demand-Side Anaysis
3.4 Porter's Five Industry Forces Analysis
3.4.1 Industry competition
3.4.2 Potential of new entrants
3.4.3 Power of suppliers
3.4.4 Power of customers
3.4.5 Threat of substitute products
3.5 PESTEL Analysis
3.5.1 Political Factors
3.5.2 Economical Factors
3.5.3 Social Factors
3.5.4 Technological Factors
3.5.5 Environmental Factors
3.5.6 Legal Factors
3.6 Market Regulations and Policies

4. Market Dynamics
4.1 Drivers
4.1.1 Data Explosion: Unstructure, Semi-structured and Complex
4.1.2 Improvement in Algorithm Development
4.1.3 Need For Customer Analytics
4.2 Restraints
4.2.1 Cultural Barriers
4.2.2 Organizational Barriers and Industry Structures
4.2.3 Budget Restrictions
4.2.4 Lack Of Technical Expertise
4.2.5 Data Security
4.2.6 Uncertain Strategies
4.3 Opportunities
4.3.1 Supply Side Business Opportunities
4.3.1.1 E-commerce
4.3.1.2 Analytics Sofware
4.3.1.3 Outsourcing, Hosting and As-a-Service Offering
4.3.1.4 Consulting
4.3.1.5 Enhanced Customer Support
4.3.2 Demand-Side Business Opportunities
4.3.2.1 Information Access
4.3.2.2 Analytics Sofware
4.3.2.3 Operations

5. Market Segmentation
5.1 By Deployment
5.1.1 On-Premise
5.1.2 Cloud
5.2 By Approach
5.2.1 Hadoop
5.2.2 NoSQL
5.2.3 MPAD (Massively Parallel Analytic Databases)
5.2.4 Others and Analytic Technologies
5.3 By Vertical
5.3.1 Telecom and IT
5.3.1.1 Location-Based Services
5.3.1.2 Customer Churn Prevention
5.3.2 Energy and Power
5.3.2.1 Optimizing Energy Production
5.3.2.2 Forcasting Energy
5.3.2.3 Exploration and Identification of Wells and Mines
5.3.2.4 Drilling Potential Maximization
5.3.2.5 Billing Analytics
5.3.2.5.1 Predictive Maintenance
5.3.2.5.2 Turbine Placement Optimization
5.3.3 Financial Services
5.3.3.1 Customer Retention
5.3.3.2 Personalized Product Offering
5.3.3.3 Risk Management
5.3.3.4 Credit Scoring
5.3.3.5 Fraud Prevention and Detection
5.3.3.6 Insurance Analytics
5.3.3.6.1 Customer Retention and Profiling
5.3.3.6.2 Fraud Mitigation
5.3.3.6.3 Risk Management
5.3.3.7 Economic Analysis
5.3.3.8 Business Intelligence
5.3.4 Retail
5.3.4.1 Sales Analytics
5.3.4.2 Supply Chain Monitoring
5.3.4.3 Personalized Marketing
5.3.4.4 Price Optimization
5.3.4.5 Customer Sentiment Analysis
5.3.4.6 Customer Retention
5.3.5 Tourism
5.3.6 Manufacturing
5.3.6.1 Asset Management
5.3.6.2 Quality Analytics
5.3.6.3 Environemental Impact Assessment
5.3.7 Professional Services
5.3.8 Transportation and Logistics
5.3.8.1 Predictive Warranty Analysis
5.3.8.2 Traffic Control
5.3.8.3 Transport Fleet Optimization
5.3.8.4 Urban Transportation Management
5.3.9 Aerospace and Defense
5.3.9.1 Predictive Warranty Analysis
5.3.9.2 Air Traffic Control
5.3.9.3 Predictive Airport Maintenance and Fuel Optimization
5.3.9.4 Intelligent Gathering
5.3.9.5 Injury Prevention and Mitigation
5.3.9.6 Energy Saving In Battlefield
5.3.10 Media and Entertainment
5.3.10.1 Channel Optimization
5.3.10.2 Optimized Search Engine
5.3.10.3 Recommended Engines
5.3.11 Engineering and Construction
5.3.11.1 Information Integration
5.3.11.2 Identifying and Learning Patterns
5.3.11.3 Energy Optimization
5.3.11.4 Fault Detection
5.3.11.5 Intelligent Home Analytics
5.3.11.6 Water Management
5.3.11.7 Urban Waste Management
5.3.12 Healthcare and Pharmaceuticals
5.3.12.1 Health Efficiency
5.3.12.2 Medical Data Analytics
5.3.13 Others
5.4 By Region
5.4.1 North America (NA)
5.4.2 Latin America (LA)
5.4.3 Europe
5.4.4 APAC
5.4.5 Middle East
5.4.6 Africa
5.5 By Country
5.5.1 U.S
5.5.2 India
5.5.3 China
5.5.4 France
5.5.5 Brazil
5.5.6 Italy
5.5.7 Mexico
5.5.8 U.K
5.5.9 Russia
5.5.10 Spain
5.5.11 Competitive Intelligence
5.5.12 (Profiles, Overview, Products and Services, Business Strategies, Recent Developments and Innovations)
5.5.13 IBM
5.5.14 Microsoft
5.5.15 Oracle
5.5.16 SAP
5.5.17 Cisco
5.5.18 SAS
5.5.19 Information Builders
5.5.20 MicroStrategy

6. Actuate

7. Accenture

8. Market Landscape
8.1 Market Trends
8.2 Market Forecasts
8.3 New Product Development and Innovations
8.4 Mergers and Acquisitions

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